Saturday, December 27, 2008
In the first paper we present a new low level compact composite descriptor for Content Based Medical Image Retrieval.
Abstract: The rapid advances made in the field of radiology, the increased frequency in which oncological diseases appear, as well as the demand for prevailing medical checks, led to the creation of a large database of radiology images in every hospital or medical center. There is now an imperative need to create an effective method for the indexing and retrieval of these images. This paper proposes a new method for content based medical image retrieval. The description of images relies on a new Composite Descriptor (CD) which includes global image features, capturing both brightness and texture characteristics at the same time. Image information is extracted using a set of fuzzy approaches. To be applicable in the design of large medical image databases, the proposed descriptor is compact, requiring only 48 bytes per image. Experiments demonstrate the effectiveness of the proposed technique. Authors: Savvas A. Chatzichristofis and Yiannis Boutalis.
The second paper is presenting a method for auto selection the proper compact composite descriptor in order to retrieve natural color images.
Abstract: Compact Composite Descriptors (CCD) are global image features capturing both, color and texture characteristics, at the same time in a very compact representation. In this paper we propose a combination of two recently introduced CCDs (CEDD and FCTH) into a Joint Composite Descriptor (JCD). We further present a method for descriptor selection to approach the best ANMRR that would result from CEDD and FCTH. With our approach the most appropriate descriptor in terms of maximization of information content can be found on a per image basis without knowledge of the data set as a whole. Experiments conducted on three known benchmarking image databases demonstrate the effectiveness of the proposed technique. Authors: Savvas A. Chatzichristofis, Mathias Lux and Yiannis Boutalis.
The descriptors will be added soon in the CCD section.
Monday, December 22, 2008
Mathias Lux is working on a summary tool, which extracts still images from a video. The goal of the tool is to find frames, which describe the image in an optimal way. Now it’s in a (rather) stable state and ready to release. For Windows users it should be a single click to start the thing, for Linux you need to install ffmpeg. Note that the tool is open source & the code is GPL-ed
Saturday, December 20, 2008
All of these options can be selected from the "Any content" drop down in the blue title bar on any search results page, or by selecting one of the "Content types" on the Advanced Image Search page. The good news: no extra typing! In all these examples our query remained exactly the same, we just restricted our results to different visual styles. So whether you're interested holiday wreaths, Celtic patterns, or office clip art, it just became a lot easier to find the images you're looking for.
Thursday, December 18, 2008
Original painting time 2hrs 30mins.
So what are the things to look for if you want to buy digital camera? To be able to answer these, there are 2 sets of information you have to know before you can decide. The first type of information is defining what YOU need and want in a digital camera. To do this, you can ask yourself the following questions:
What do you want to take with your digital camera? Before you buy digital camera, it is important to determine what kind of pictures you want to take with it. If you are a digital photography enthusiast, any digital camera will not just do. You have to look for features that can support the zooming you need, the resolution, etc.
How much is your budget? This is a very important question any person who intends to buy digital camera should ask. Because no matter what your needs and wants are for the device, your financial resource will play a huge part in dictating the type of digital camera you will buy.
What are you resources? When you buy digital camera, sometimes the spending does not end there. You also have to consider the capacity and the power of the computer and the printer you will be hooking your camera with for your editing and printing needs. Editing software are already included when you buy digital camera but other devices aren’t. Aside from a printer, ink and paper for printing, you might also need additional memory cards for your camera and a more powerful computer to support image editing and image storage and retrieval.
Wednesday, December 17, 2008
Recently, standard benchmark databases and evaluation campaigns have been created allowing a quantitative comparison of CBIR systems. These benchmarks allow the comparison of image retrieval systems under different aspects: usability and user interfaces, combination with text retrieval, or overall performance of a system.
1. WANG database
The WANG database is a subset of 1,000 images of the Corel stock photo database which have been manually selected and which form 10 classes of 100 images each. The WANG database can be considered similar to common stock photo retrieval tasks with several images from each category and a potential user having an image from a particular category and looking for similar images which have e.g. cheaper royalties or which have not been used by other media. The 10 classes are used for relevance estimation: given a query image, it is assumed that the user is searching for images from the same class, and therefore the remaining 99 images from the same class are considered relevant and the images from all other classes are considered irrelevant
2. The MIRFLICKR-25000 Image Collection
Access to the collection is simple and reliable, with image copyright clearly established. This is realized by selecting only images offered under the Creative Commons license. See the copyright section below.
Images are also selected based on their high interestingness rating. As a result the image collection is representative for the domain of original and high-quality photography.
In particular for the research community dedicated to improving image retrieval. We have collected the user-supplied image Flickr tags as well as the EXIF metadata and make it available in easy-to-access text files. Additionally we provide manual image annotations on the entire collection suitable for a variety of benchmarks.
MIRFLICKR-25000 is an evolving effort with many ideas for extension. So far the image collection, metadata and annotations can be downloaded below. If you enter your email address before downloading, we will keep you posted of the latest updates.
3. UW database
The database created at the University of Washington consists of a roughly categorized collection of 1,109 images.These images are partly annotated using keywords. The remaining images were annotated by our group to allow the annotation to be used for relevance estimation; our annotations are publicly available10.The images are of various sizes and mainly include vacation pictures from various locations. There are 18 categories,for example “spring ﬂowers”, “Barcelona”, and “Iran”. Some example images with annotations are shown in Figure 2. The complete annotation consists of 6,383 words with a vocabulary of 352 unique words. On the average, each image has about 6 words of annotation. The maximum number of key-words per image is 22 and the minimum is 1. The database is freely available11. The relevance assessment for the experiments with this database were performed using the annotation: an image is considered to be relevant w.r.t. a given query image if the two images have a common keyword in the annotation. On the average, 59.3 relevant images correspond to each image. The keywords are rather general; thus for example images showing sky are relevant w.r.t. each other,which makes it quite easy to ﬁnd relevant images (high precision is likely easy) but it can be extremely diﬃcult to obtain a high recall since some images showing sky might have hardly any visual similarity with a given query.This task can be considered a personal photo retrieval task,e.g. a user with a collection of personal vacation pictures is looking for images from the same vacation, or showing the same type of building.
4. IRMA-10000 database
5. ZuBuD database
The “Zurich Buildings Database for Image Based Recognition”(ZuBuD) is a database which has been created by the Swiss Federal Institute of Technology in Zurich. The database consists of two parts, a training part of 1,005images of 201 buildings, 5 of each building and a query part of 115 images. Each of the query images contains one of the buildings from the main part of the database. The pictures of each building are taken from diﬀerent viewpoints and some of them are also taken under diﬀerent weather conditions and with two diﬀerent cameras. Given a query image, only images showing exactly the same building are considered relevant.
6. UCID database (Suggested)
The UCID database13 was created as a benchmark database for CBIR and image compression applications. This database is similar to the UW database as it consists of vacation images and thus poses a similar task.For 264 images, manual relevance assessments among all database images were created, allowing for performance evaluation. The images that are judged to be relevant are images which are very clearly relevant, e.g. for an image showing a particular person, images showing the same person are searched and for an image showing a football game, images showing football games are considered to be relevant. The used relevance assumption makes the task easy on one hand,because relevant images are very likely quite similar, but on the other hand, it makes the task diﬃcult, because there are likely images in the database which have a high visual similarity but which are not considered relevant. Thus, it can be diﬃcult to have high precision results using the given rel-evance assessment, but since only few images are considered relevant, high recall values might be rather easy to obtain.
<Yaroslav Bulatov> I've collected this dataset for a project that involves automatically reading bibs in pictures of marathons and other races. This dataset is larger than robust-reading dataset of ICDAR 2003 competition with about 20k digits and more uniform because it's digits-only. I believe it is more challenging than the MNIST digit recognition dataset.
I'm now making it publicly available in hopes of stimulating progress on the task of robust OCR. Use it freely, with only requirement that if you are able to exceed 80% accuracy, you have to let me know ;)
The dataset file contains raw data (images), as well as Weka-format ARFF file for simple set of features.
For completeness I include matlab script used to for initial pre-processing and feature extraction, Python script to convert space-separated output into ARFF format. Check "readme.txt" for more details.
- Database of thousands of weakly labelled, high-res images. Please, click here to download the database.
- Pixel-wise labelled image database v1 (240 images, 9 object classes). Please, click here to download the database. This database was used in paper 1 below and in the above demo video.
- Pixel-wise labelled image database v2(591 images, 23 object classes). Please, click here to download the database.
- Pixel-wise labelled image database of textile materials. Please, click here to download the database.
1. Deselaers, T., Keysers, D., and Ney, H. 2008. Features for image retrieval: an experimental comparison. Inf. Retr. 11, 2 (Apr. 2008), 77-107. DOI=http://dx.doi.org/10.1007/s10791-007-9039-3
2. S. A. Chatzichristofis, K Zagoris, Y. S. Boutalis and Nikolas Papamarkos, “ACCURATE IMAGE RETRIEVAL BASED ON COMPACT COMPOSITE DESCRIPTORS AND RELEVANCE FEEDBACK INFORMATION”, «International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) », to Appear, 2009
Monday, December 15, 2008
1. Spatial Fuzzy Brightness and Texture Directionality Descriptor (New Descriptor). This descriptor is suittable for Content Based Medical Image Retrieval.
2. New Quantization Tables for Brightness and Texture Directionality Descriptor
3. Gustafson Kessel Classifier for Color Reduction
1. Spatial Fuzzy Brightness and Texture Directionality Descriptor (New Descriptor). This descriptor is suittable for Content Based Medical Image Retrieval.
2. Get the Precision->Recall Graph after the retrieval procedure.
Download the latest version
Friday, December 12, 2008
And what's more, Multiple Image Resizer .NET is FREE for personal and educational use! Commercial users of Multiple Image Resizer .NET should buy a commercial use license from us.
Multiple Image Resizer .NET also has a completely customisable user interface that you can arrange to suit yourself.
Have a look at the features page for more information about what Multiple Image Resizer .NET is capable of.
Latest News, 3rd December 2008:
The software has been updated to support the Greek language.
Multiple Image Resizer .NET's user interface now supports 14 different languages. If you would like to see the software in your own language then why not provide a translation - see the translate page for more information.
Multiple Image Resizer .NET Version 184.108.40.206 is now available for download.
Images can be scenes; landmarks; objects; graphics; people or even personalities. Irrespective of the size of the image collection Imprezzeo Image Search helps users find the right image - fast. For a demonstration, visit www.imprezzeo.com.
The potential for this technology is huge. Stock photo libraries and news agencies can provide more relevant search results to image buyers (and sell content that might not have been found using traditional text-based search); search engines can provide users with a far more sophisticated image search experience than is currently available; photo sharing sites can offer search by example image, rather than search that solely relies on user’s tagging; consumers can organise their personal photo collections by content and person rather than by date; even retailers can recommend similar products for purchase (for example, if a consumer is searching for a ‘red handbag’, Imprezzeo could be used to find all similar products).
The proprietary search software is more sophisticated than any other image-based search technology on the market, combining content-based image retrieval (CBIR) and facial recognition technologies. Imprezzeo’s sophisticated image analytics ensures the Imprezzeo platform can deliver great results when applied to a whole range of different image content and when used in a whole range of applications.
The new technology generates image search results that closely match a sample image either chosen by the user from an initial set of search results that can then be refined, or from an image uploaded by the user.
Dermot Corrigan, CEO of Imprezzeo, says: “This will fundamentally change the way users and consumers expect to search for images, whether that’s in a photo stock library, the desktop or the web. We know that currently many image searches are abandoned at the first set of results, because the returned results are not what the user is looking for. This technology changes that.”
Thursday, December 11, 2008
Sunday, December 7, 2008
1. Tamura Texture Directionality Histogram (Bug Fixed)
2. Auto Correlograms using several methods and max distances
3. Color Histogram Crisp Linking
4. Brightness and Texture Directionality Descriptor (New Descriptor)
5. Scalable Fuzzy Brightness and Texture Directionality Descriptor (New Descriptor)
6. Spatial Color Layout (Beta Version) (New Descriptor)
7. Color Reduction Using Gustafson Kessel
8. Joint Composite Descriptor (Final Version) (New Descriptor)
9. Auto Descriptor Selector (Final Version)
10. Color Histograms (RGB)
11. Auto Correlogram
12. Tamura Texture
13. Evaluate retrieval results using ANMRR and/or Mean Average Precision
14. Faster creation of index files
15. .Net Framework 3.5 Support
16. Retrieve images form sketches using the beta version of "Spatial Color Layout" (New Descriptor)
17. Retrieve images from Sketches using "Color Layout Descriptor"
18. Retrieve images form sketches using the beta version of "Spatial Color Layout" (New Descriptor)
Thursday, December 4, 2008
The idea of ImageSorter is to find images of which you remember how they look but you forgot in which folder they were. If one or several folders are selected, all images from these folders will be visually arranged such that similar images are close to each other. In this sorted display it will be much easier to find a particular image. Selected images can be copied, moved or deleted (right mouse click).ImageSorter does cache thumbnails and sortings, therefore after images have been loaded once, everything will be much faster.
The current version of ImageSorter is 3.0 BETA 3 for Windows and V2.0.2 for Mac OS X. ImageSorter 3 introduced an Internet image search (Yahoo! and Flickr) and the possibility to search for similar images on the local disk or the Internet. The software profits from a greatly improved stability and run-time performance. Furthermore quite a few suggestions made in the forum have been included. See the change log for a detailed list of changes
Download the ImageSorter software
Tuesday, December 2, 2008
Monday, December 1, 2008
The proposed scope of CAIP09 includes, but not limited to, the following areas:
* 3D Vision
* 3D TV
* Color and texture
* Document analysis
* Graph-based Methods
* Image and video indexing and database retrieval
* Image and video processing
* Image-based modeling
* Kernel methods
* Medical imaging
* Mobile multimedia
* Model-based vision approaches
* Motion Analysis
* Non-photorealistic animation and rendering
* Object recognition
* Performance evaluation
* Segmentation and grouping
* Shape representation and analysis
* Structural pattern recognition
Invited speakers (incomplete yet)
David G. Stork (Ricoh Innovations and Stanford University, USA) Aljoscha Smolic (Fraunhofer Institute for Telecommunications, Germany)
Saturday, November 29, 2008
The first VideoTagGame ran back in summer of 2007 during a Yahoo! party in Amsterdam. Now they're ready to take their experiment to the public through the Yahoo! Sandbox so they can collect more statistics on its usage.
The objective of the VideoTagGame is to collect time-based annotations of the video which could then enable the retrieval of relevant parts in a video when a search is performed, rather than returning the entire video itself. These annotations are collected in the context of a multi-player game.
How To Play
To play the VideoTagGame, participants must sign in with their Yahoo! ID and join a new game. There will always be at least three players in each game. After a 3-second countdown, the video will begin to play. As it plays, participants enter tags that correspond to the various parts of the video. When two players agree on a tag (that is, they enter the same tag), they each get points. The closer together the tags were entered, the more points are rewarded. After the video ends, participants can then watch as it plays again, this time with the tags overlaid on top of the video.
Friday, November 28, 2008
Yottalook™ is based on core technologies developed by iVirtuoso to achieve optimized search results. First is automated analysis of the search term to understand what the radiolgist is trying to look for - this core technology is called "natural query analysis".
Yottalook™ has also developed a thesaurus of medical terminologies that not only identifies synonyms of terms but also defines relationships between terms. This second core technology is called "semantic ontology" and is based on existing medical ontologies that have been enhanced by iVirtuoso, such as RadLex - A Lexicon for Uniform Indexing and Retrieval of Radiology Information Resources developed by the Radiology Society of North America.
Third core technology is "relevance algorithm" for image search that differentiates medical terms from other words in text associated with medical images and uses them to create ranking for Yottalook image search.
The fourth core technology is a specialized content delivery system called "Yottalinks" that provides high yield content based on the search term. This content may also be provided by a third party vendor licensing Yottalook search. Yottalook™ can be integrated with any web based medical application so that context relevant information is provided to the physician at the point of care. http://www.yottalook.com/
Thursday, November 27, 2008
Tuesday, November 25, 2008
The new version features better visualization techniques for all the kinds of screens, plus, a new feature that preserves even better the correctly exposed image regions. PhotoEnhancer 2.2 is the most complete version of this project.
Monday, November 24, 2008
Jena is open source and grown out of work with the HP Labs Semantic Web Programme.
The Jena Framework includes:
A RDF API
Reading and writing RDF in RDF/XML, N3 and N-Triples
An OWL API
In-memory and persistent storage
SPARQL query engine
Sunday, November 23, 2008
CVPR 2009 will be held at the Fontainebleau Hotel in Miami, Florida.
Papers in the main technical program must describe high-quality, original research.
Topics of interest include all aspects of computer vision and pattern recognition (applied to images and video) including, but not limited to, the following areas:
Early and Biologically-inspired Vision
Color and Texture
Segmentation and Grouping
Computational Photography and Video
Motion and Tracking
Stereo and Structure from Motion
Illumination and Reflectance Modeling
Shape Representation and Matching
Object Detection, Recognition, and Categorization
Video Analysis and Event Recognition
Face and Gesture Analysis
Statistical Methods and Learning
Medical Image Analysis
Image and Video Retrieval
Vision for Graphics
Vision for Robotics
Vision for Internet
Applications of Computer Vision
Saturday, November 22, 2008
Topics of interest include, but are not limited to:
Multimedia indexing and retrieval (image, audio, video, text)
Matching and similarity search
Construction of high level indices
Multimedia content extraction
Identification and tracking of semantic regions in scenes
Multi-modal and cross-modal indexing
Multimedia data mining
Metadata generation, coding and transformation
Large scale multimedia database management
Summarisation, browsing and organization of multimedia content
Presentation and visualization tools
User interaction and relevance feedback
Personalization and content adaptation
Evaluation and metrics
Thursday, November 20, 2008
July 8-10, 2009, Santorini Island, Greece - http://www.civr2009.org/ -
Image and Video retrieval have now reached a state where successful techniques and applications start flourishing. The ACM International Conference on Image and Video Retrieval (ACM-CIVR) series of conferences is the ideal opportunity to present and encounter such developments. Originally set up to illuminate the state-of-the-art in image and video retrieval throughout the world, it is now a reference event in the field where researchers and practitioner exchange knowledge and ideas. CIVR2009 is seeking original high quality special sessions addressing innovative research in the broad field of image and video retrieval. We wish to highlight significant and emerging areas of the main problem of search and retrieval but also the equally important related issues of multimedia content management, user interaction and community-based management.
Example topics of interest include but are not limited to: social network information mining, unsupervised methods for data exploration, large scale issues for algorithms and data set generation.
Each special session will consist of 5 invited papers. The organizers’ role is to attract the speakers and chair the session itself. Proposals will be evaluated based on the timeliness of the topic, relevance to CIVR, the degrees to which they will bring together key researchers in the area, introduce the area to the larger research community, further develop the area, and potentials to establish a larger community around the area. Please note that all papers in the proposed session will undergo the same review process as regular papers. If after the reviewing process less than the necessary number of papers solicited for a special session are selected, the Special Session will be cancelled, and the solicited papers that passed review process will be presented within regular sessions of the conference.
Microsoft Live Labs has turned these research ideas into a streaming multi-resolution Web-based service called Photosynth.
You can also read about newer research we have been doing in this area at the University of Washington Photo Tourism project page.
Paper and video
Noah Snavely, Steven M. Seitz, and Richard Szeliski, Photo Tourism: Exploring photo collections in 3D," ACM Transactions on Graphics, 25(3), August 2006. (Video WMV), Video (MOV))Abstract
We have developed a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end, which automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo navigation tool uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, as well as to annotate image details, which are automatically transferred to other relevant images in the collection. We demonstrate our system on several large personal photo collections as well as images gathered from photo sharing Web sites on the Internet.
Wednesday, November 19, 2008
"Our goal is to develop an easy-to-use computaion and simulation platform with a C++ like syntax. We have adopted Matlab's structure philoshophy and C++ 's structured language syntax. There are various toolboxes (packages of functions relative to a certain scientific field), which depend on open-source libraries." EngLab Team
The EngLab distribution is available in two ways: there are two basic Englab releases, EngLab Console and EngLab GUI. EngLab Console allows EngLab's execution through the console(Linux or Windows). EngLab GUI gives the opportunity of using EngLab through a graphical user interface. EngLab GUI is implemented with the use of the open-source library wxWidgets 2.8, providing additional usability compared to EngLab Console edition. EngLab GUI is independent, so there is no need for EngLab Console to be installed, in order to properly install and execute EngLab GUI.
Toolboxes are distributed as seperate packages. Their installation is possible either through EngLab Console or EngLab GUI. The reason is that those toolboxes depend on open-source libraries that have to be previously installed. So as the user not to be forced to install those libraries directly, user can install packages and toolboxes at his/her own will.
For the time being, EngLab Console edition is available for Windows and Linux and Englab GUI is available for Linux only.
Until now EngLab has the following features :
- 16 types of variable declaration (int, float, ...)
- Variable declaration with unlimited number of dimensions.
- Loop structures (for, while, ...)
- Arithmetic, logical and binary operations
- Constant number declaration (pi, phi, ...)
- Graphical manipulation of variable values of any dimension (Englab GUI)
- Adjustable graphical environment (Englab GUI)
- Editor for writing *.eng functions (Englab GUI)
- Command history for the last 5 sessions
- Immediate access to variables, constants and functions (EngLab GUI)
- Recent files opened through EngLab (EngLab GUI)
Toolboxes that have been fully or partially implemented:
- a package containing fundamental functions of C (trigonemetric, hyperbolic trigonometrical, ...)
- a package containing some statistic functions
- a package containing functions that allow convertions of the variable type
All these toolboxes accompany the basic two EngLab editions, since they do not depend on another open-source library. Moreover, some other toolboxes have been partially implemented:
- a package that contains functions for the manipulation of 2-D matrices (determinant, inverse array, ...). This package depends on the open-source library NewMat10.
- a package that contains functions for image processing. This package depends on the open-source library CImg.
- a package that contains functions for image processing. This package depends on the open-source library OpenCV.
- a toolbox for visual data representation(plots etc)
- a toolbox that contains functins for manipulating polyonymials, root detection, computation of integrals and derivatives, special functions and more.
Tuesday, November 18, 2008
The technology works using 10 stock human emotions - for instance happiness, sadness, concern - that have been programmed into the robot.
The software then maps what it sees to Jules' face to combine expressions instantly to mimic those being shown by a human subject.
Controlled only by its own software, Jules can grin and grimace, furrow its brow, and 'speak' as the software translates real expressions observed through video camera 'eyes'.
If you want people to be able to interact with machines, then you've got to be able to do it naturally...When it moves it has to look natural in the same way that human expressions are, to make interaction useful.Chris Melhuish, head of the Bristol Robotics Laboratory
The robot - made by US roboticist David Hanson - then copies the facial expressions of the human by converting the video image into digital commands that make the robot's inner workings produce mirrored movements.
And it all happens in real time as Jules is bright enough to interpret the commands at 25 frames per second.
The project was developed over more than three years at the Bristol Robotics Laboratory, a lab run by the University of the West of England and the University of Bristol under the leadership of Chris Melhuish, Neill Campbell and Peter Jaeckel.
The aim of the developers was to make it easier for humans to interact with 'artificial intelligence', in other words to create a 'feelgood' factor.
The BRL's Peter Jaeckel said: ''Realistic, life-like robot appearance is crucial for sophisticated face-to-face robot - human interaction.
''Researchers predict that one day robotic companions will work, or assist humans in space, care and education. Robot appearance and behaviour need to be well matched to meet expectations formed by our social experience."
But a warning note has been sounded.
Kerstin Dautenhahn, a robotics researcher at the University of Herefordshire, believes that people may be disconcerted by humanoid automatons that simply look 'too human'.
''People might easily be fooled into thinking that this robot not only looks like a human and behaves like a human, but that it can also feel like a human. And that's not true," she pointed out.
Monday, November 17, 2008
Friday, November 14, 2008
1. Draw a sketch to retrieve similar images from our database. The method is based on a new spatial compact color descriptor.
2. Automatic keyword annotation. Select a combination of words and retrieve images. The method is based on a fuzzy support vector machine system. The network was trained using a combination of CEDD and FCTH descriptors
Thursday, November 13, 2008
Friendships are shown as dark gray lines, and common photo appearances are shown as a lighter gray line with a number in the center. The number indicates how many photos the two people appeared in together.
Personal vs. Friends social networks
* When launched from one's own profile, information about all of one's friends and their friendships is loaded.* When launched from another user's profile (Using the "TouchGraph Friends + Photos" link below their profile picture) only people tagged in their photos will appear in the graph.
One can not see another person's whole social network because Facebook only allows applications to get a list of one's own friends. For other users it is only possible to get a list of people that they appear in photos with. Perhaps Facebook's policy will change in the future.
The TouchGraph Facebook Browser determines the clusters/cliques to which one's Friends belong and uses different colors to show each clique. Cliques are characterized by having lots of friendships within a group of friends and few connections to members outside the group.
Friends are assigned a Rank so that one can reduce clutter by only showing a set of 'Top' friends. TouchGraph gives the highest rank to friends who are connectors between different cliques. Finding connecters involves a metric called Betweeness Centrality which is an established measure for a person's importance within a social network.
Wednesday, November 12, 2008
Sunday, November 9, 2008
PhotoEnhancer is an experimental image enhancement software, which employs the characteristics of the ganglion cells of the Human Visual System. Many times the image captured by a camera and the image in our eyes are dramatically different. Especially when there are shadows or highlights in the same scene. In these cases our eyes can distinguish many details in the shadows or the highlights, while the image captured by the camera suffers from loss of visual information in these regions.
PhotoEnhancer attempts to bridge the gap between "what you see" and "what the camera sees". It enhances the shadow or the highlight regions of an image, while keeping intact all the correctly exposed ones. The final result is a lot closer to the human perception of the scene, than the original captured image, revealing visual information that otherwise wouldn't be available to the human observer.
The latest version of PhotoEnhancer (2.4) has been released. Version 2.4 features a 'Batch Processing' mode, for the quick enhancement of many image files just with a few clicks, as well as an improved user interface.
The new version of PhotoEnhancer features the method of "Multi-Scale Image Contrast Enhancement". This additional algorithm enhances locally the contrast of images, maximizing the available visual information. It can be applied to foggy scenes, aerial or satellite images, images with smoke or medical images.
Bugs report and suggestions at: email@example.com
Saturday, November 8, 2008
Wednesday, November 5, 2008
X. Anguera, N. Oliver, and M. Cherubini. Multimodal and mobile personal image retrieval: A user study. In K. L. Chan, editor, Prooceeding of the International Workshop on Mobile Information Retrieval (MobIR’08), pages 17–23, Singapore, 20-24 July 2008. [PDF]
Mobile phones have become multimedia devices. Therefore it is not uncommon to observe users capturing photos and videos on their mobile phones. As the amount of digital multimedia content expands, it becomes increasingly difficult to find specific images in the device. In this paper, we present our experience with MAMI, a mobile phone prototype that allows users to annotate and search for digital photos on their camera phone via speech input. MAMI is implemented as a mobile application that runs in real-time on the phone. Users can add speech annotations at the time of capturing photos or at a later time. Additional metadata is also stored with the photos, such as location, user identification, date and time of capture and image-based features. Users can search for photos in their personal repository by means of speech without the need of connectivity to a server. In this paper, we focus on our findings from a user study aimed at comparing the efficacy of the search and the ease-of-use and desirability of the MAMI prototype when compared to the standard image browser available on mobile phones today.
In this article, a C# library for neural network computations is described. The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. The usage of the library is demonstrated on several samples:
Classification (one-layer neural network trained with perceptron learning algorithms);
Approximation (multi-layer neural network trained with back propagation learning algorithm);
Time Series Prediction (multi-layer neural network trained with back propagation learning algorithm);
Color Clusterization (Kohonen Self-Organizing Map);
Traveling Salesman Problem (Elastic Network).
The attached archives contain source codes for the entire library, all the above listed samples, and some additional samples which are not listed and discussed in the article.
The article is not intended to provide the entire theory of neural networks, which can be found easily on the great range of different resources all over the Internet, and on CodeProject as well. Instead of this, the article assumes that the reader has general knowledge of neural networks, and that is why the aim of the article is to discuss a C# library for neural network computations and its application to different problems.
Intelligent Decision Technologies (IDT) journal seeks original manuscripts
for a Special Issue on Advances in Medical Decision Support Systems scheduled to appear in Vol. 3, No. 2, 2009.The last few decades have witnessed significant advancements in intelligent computation techniques. Driven by the need to solve complex real-world problems, powerful and sophisticated intelligent data analysis technologies have been exploited or emerged, such as neural networks, support vector machines, evolutionary algorithms, clustering methods, fuzzy logic, particle swarm optimization, data mining, etc. In recent years, the volume of biological data has been increasing exponentially, thus, allowing significant learning and experimentation to be carried out using a multidisciplinary approach, which gives rise to many challenging problems. The foundation for any medical decision support is the medical knowledge base which contains the necessary rules and facts. This knowledge needs to be acquired from information and data in the fields of interest, such as medicine. Clinical decision-making is a challenging, multifaceted process. Its goals are precision in diagnosis and institution of efficacious treatment. Achieving these objectives involves access to pertinent data and application of previous knowledge to the analysis of new data in order to recognise patterns and relations. As the volume and complexity of data have increased, use of digital computers to support data analysis has become a necessity. In addition to computerisationof standard statistical analysis, several other techniques for computer-aided data classification and reduction generally referred to as intelligent systems, have evolved. This special issue will focus on illustrative and detailed information about medical intelligent decision support systems and feature extraction/selection for automated diagnostic systems.The focus of this special issue is on advances in medical intelligent decision support systems including determination of optimum classification schemes for the problem under study and also to infer clues about the extracted features. Topics include, but are not limited to, the following:
* Bioinformatics and Computational Biology
* Neural Networks, Fuzzy Logic Systems and Support Vector Machines in Biological Signal Processing
* Decision Support Systems and Computer Aided Diagnosis
* Biomedical Signal Processing
* Biomedical Imaging and Image Processing
* Modelling, Simulation, Systems, and Control
Paper submission: Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. As an author, you are responsible for understanding and adhering to our submission guidelines. You can access them from http://www.iospress.nl. Please thoroughly read these before submitting your manuscript. Each paper will go through a rigorous review process.
Please note the following important dates:
Paper submission for review: November 30, 2008 (Final deadline)
Review results: January 15, 2009
Revised Paper submission: February 20, 2009
Final acceptance: March 1, 2009
Manuscript delivery to the publisher: April 15, 2009
Interested authors should submit digital copies (PDF preferred) of their papers (suggested paper length: 15 pages), including all tables, diagrams, and illustrations, to the Guest Editor, Dr. Vassilis S. Kodogiannis, bye-mail.
Tuesday, November 4, 2008
Monday, October 27, 2008
For More Details click Here
Sunday, October 26, 2008
Picsearch image search technology has three main features that make it unique. It has a relevancy unrivalled on the web due to its patent-pending indexing algorithms. Also, Picsearch has a family friendliness that allows children to surf in safety as all offensive material is filtered out by our advanced filtering systems. The site is also very user friendly as it's designed to be simple, fast and accurate. Due to all of these features, Picsearch is perfect for fun, school, business and families!
Saturday, October 25, 2008
Just as you are familiar with entering text in a regular search engine such as Google to find web pages that contain that text, TinEye lets you submit an image to find web pages that contain that image
Every day TinEye's spiders crawl the web for additional images. Using sophisticated pattern recognition algorithms, TinEye creates a unique and compact digital signature or 'fingerprint' for each one and adds it to the index.
When you want to find out where an image is being used on the web, you submit it to TinEye. The attributes of the image are analyzed instantly, and its fingerprint is compared to the fingerprint of every single image in the TinEye search index. The result? A detailed list of any websites using that image, worldwide.
Use TinEye to find out where and how an image appears on the web, even if it has been cropped or heavily modified.
Friday, October 24, 2008
This conference is an international forum for researchers and practitioners interested in the advances in, and applications of, signal processing and pattern recognition. It is an opportunity to present and observe the latest research, results, and ideas in these areas. All papers submitted to this conference will be double-blind reviewed by at least two reviewers. Acceptance will be based primarily on originality and contribution. The conference chair makes the final decision on the acceptance or rejection of the paper.
SPPRA 2009 will be held in conjunction with the IASTED International Conferences on:
• Artificial Intelligence and Applications (AIA 2009)
• Software Engineering (SE 2009)
• Parallel and Distributed Computing and Networks (PDCN 2009)
• Computer Graphics and Imaging (CGIM 2009)
Innsbruck is nestled in the valley of the Inn River and tucked between the Austrian Alps and the Tuxer mountain range. It has twice hosted the Winter Olympics and is surrounded by the eight ski regions of the Olympic Ski World, including the Stubai Glacier, which offers skiing year round. Climbing the 14th century Stadtturm on Herzog Friedrich Strasse provides a stunning view of the town and the breathtaking scenery that surrounds it. Concerts at Ambras Castle provide listening pleasure in a beautiful renaissance setting. The sturdy medieval houses and sidewalk cafés of Old Town Innsbruck beckon you to sit for a while and watch people stroll by.
Innsbruck, with its unique blend of historical, intellectual, and recreational pursuits, offers something for every visitor. SPPRA 2009 will be held at the world-famous Congress Innsbruck, located in the heart of the city, near the historical quarter.
Submissions due November 27, 2008 (NEW)
Notification of acceptance December 12, 2008
Camera-ready manuscripts due January 6, 2009
I am planning to submit 2 papers in this conference. :)
Download Lire 0.7 and/or LireDemo 0.7
Screencast: Introduction to LireDemo
Sunday, October 12, 2008
- IEEE Trans. on Pattern Analysis and Mach. Int.
- IEEE Trans. on Image Processing
- Pattern Recognition
- CVGIP: Computer Vision and Image Understanding
- International Journal of Computer Vision
- Optical Engineering
- Image and Vision Computing
- Journal of Digital Imaging
- IEEE Trans. on Systems Man and Cybernetic- Part B
- Journal of Mathematical Imaging and Vision
- Journal of Electronic Imaging
- International Journal of Imaging Systems and Technology
- Pattern Analysis and Applications
- Pattern Recognition Letters
- Journal of Imaging Science and Technology
- Real-Time Imaging
- Engineering Applications of Artificial Intelligence
- International Journal of Pattern Recognition and Artificial Intelligence
- IEE Proceedings - Vision, Image and Signal Processing
- Neural Computing & Applications
- Imaging Science Journal
- Machine Vision and Applications
- International Journal on Document Analysis and Recognition
- International Journal of Image and Graphics
- Journal of Visual Communication and Image Representation
- Journal of Intelligent & Robotic Systems
- Electronic Letters
Saturday, October 11, 2008
A very interesting site about color models. This web site provides an annotated library of easily-downloadable standard data sets relevant to color and vision research.
Computer Vision Homepage
The Computer Vision Homepage was established at Carnegie Mellon University in 1994 to provide a central location for World Wide Web links relating to computer vision research. The emphasis of the Computer Vision Homepage is on computer vision research rather than on commercial products.
Color and Computers
Web site on color, color quantization, palettes
Computer Vision, University of Nevada
Useful material on Computer Vision
NeuQuant: Fast High-Quality Image Quantization
The NeuQuant Neural-Net image quantization algorithm is a replacement for the common Median Cut algorithm.
Intelligent Control Systems Laboratory, Georgia Tech
The Intelligent Control Systems Laboratory (ICSL) at Georgia Tech is the campus center for research and academic studies in soft computing for control applications. The lab is equipped with state-of-the-art control systems and modern software development packages that enable applied research in a number of application domains such as biomedical engineering, diagnostics and prognostics, and unmanned aerial vehicles.
The Face Recognition Home Page
Relevant information in the the area of face recognitionInformation pool for the face recognition communityEntry point for novices as well as a centralized information resource
Document Understanding and Character Recognition
This system serves as a repository for Document Image Understanding and Optical Character Recognition (OCR) information and resources.
Wavelet.org Amara's Wavelet Page Matlab Wavelet Toolbox (Rice University)
The fundamental idea behind wavelets is to analyze according to scale. Indeed, some researchers in the wavelet field feel that, by using wavelets.......
Wavelets at Imager
This site offers several services intended to foster the exchange of knowledge and viewpoints related to theory and applications of wavelets.
An Introduction to Wavelets
Here at Imager, we've been doing some work with wavelets, those clever little multiresolution basis functions.
Wavelets, Signal Processing Algorithms, Orthogonal Basis Functions, Wavelet Applications
Rainer Lienhart Home Page
Research Interests: Image processing, 3D reconstruction, object tracking
Filip Room Home Page
Research Interests include image/video/audio content analysis, machine learning, scalable signal processing, scalable learning, scalable and adaptive algorithms, ubiquitous and distributed media computing in heterogeneous networks, and peer-to-peer networking and mass media sharing.
Anca Doloc-Mihu Home Page
Research interests: Digital image processing and restoration
Computer Vision Bibliography
The pattern recognition files
Information on the pattern recognition research area.
The Lena Story
The Lenna (or Lena) picture is one of the most widely used standard test images used for compression algorithms.
Nikos Papamarkos current research interests are in digital image processing, computer vision, document processing, analysis and recognition, pattern recognition, neural networks, signal processing, filter design and optimization algorithms . He has published a number of Journal and Conferences. Also, he is author of three Greek books.
Homepage of Thomas Deselaers
Thomas Deselaers is a research and teaching assistant and PhD-student at the Human Language Technology and Pattern Recognition Group of the RWTH Aachen University.
Friday, October 3, 2008
The MLDM´2009 conference is the sixth event in a series of Machine Learning and Data Mining meetings, initially organised as international workshops. The aim of MLDM´2009 is to bring together from all over the world researchers dealing with machine learning and data mining, in order to discuss the recent status of the research in the field and to direct its further developments.
Basic research papers as well as application papers are welcome. All kinds of applications are welcome, but special preference will be given to multimedia related applications, biomedical applications, and webmining. Paper submissions should be related but not limited to any of the following topics:
applications of clustering
applications in medicine
aspects of data mining
annotation of media content
Bayesian models and methods
conceptional learning and clustering
case-based reasoning and learning
classification and interpretation of images,
text, video classification and model
estimation case-cased ... and more
We are pleased to announce that the Tenth International Conference on Document Analysis and Recognition (ICDAR'2009), sponsored by the International Association for Pattern Recognition (IAPR) TC-10 (Graphics Recognition) and TC-11 (Reading Systems) will be held at the Universitat Autònoma de Barcelona, Catalonia, Spain during July 26-29, 2009. ICDAR is an outstanding international forum for researchers and practitioners at all levels of experience for identifying, encouraging and exchanging ideas on the state-of-the-art in document analysis, understanding, retrieval, and performance evaluation, including various forms of multimedia documents.
The topics of interest include, but are not limited to:
Document Image Analysis
Document Analysis Systems
Basic Research and Methodologies for Document Processing
Camera-based Document Processing
Document Databases and Digital Libraries
Analysis of Historical Documents
Manuscripts of maximum five pages are encouraged to be submitted. Papers must describe original work on any of the ICDAR related topics. The format templates and instructions for paper submission will be available in the Conference web site. The deadline for paper submission is January 12, 2009.
The International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS) is one of the main international fora for the presentation and discussion of the latest technological advances in interactive multimedia services. The objective of the workshop is to bring together researchers and developers from academia and industry working in all areas of image, video and audio applications, with a special focus on analysis.
Topics of interest include, but are not limited to:
Multimedia content analysis and understanding
Content-based browsing, indexing and retrieval of images, video and audio
2D/3D feature extraction
Advanced descriptors and similarity metrics for audio and video
Relevance feedback and learning systems
Segmentation of objects in 2D/3D image sequences
Motion analysis and tracking
Video analysis and event recognition
Analysis for coding efficiency and increased error resilience
Analysis and tools for content adaptation
Multimedia content adaptation tools, transcoding and transmoding
Content summarization and personalization strategies
End-to-end quality of service support for Universal Multimedia Access
Semantic mapping and ontologies
Multimedia analysis for new and emerging applications
Multimedia analysis hardware and middleware
Semantic web and social networks
Advanced interfaces for content analysis and relevance feedback
The intention is to publish the proceedings in the Springer's Lecture Notes in Computer Science Series and to make them available in IEEExplore. The authors are requested to send their submissions (4 pages double column in English). All submissions will be peer reviewed by at least three members of the technical program committee.Accepted papers will be distributed via the IEEE Xplore™. Papers must be formatted according to the IEEE Computer Society standards and their length must not exceed 4 IEEE double column style pages including all figures, tables, and references.
Proposal for Special Session: November 21, 2008
Paper Submission: December 1, 2008
Notification of Acceptance: January 16, 2009
Camera-ready Papers: February 06, 2009
"We have recently completed the first round of development on the IAPR Pattern Recognition Education Resources web site:
This work was initiated by the Internation Association for Pattern Recognition (http://www.iapr.org/).
The goal was a web site that can support students, researchers and staff.
Of course, advances in pattern recognition and its subfields means that developing the site will be a never-ending process. However, we believe that the current site is
now well developed enough for general use.
What resources does the IAPR
Education web site have?
The most important resources are for students, researchers and educators. These include lists with URLs to:
- Tutorials and surveys
- Explanatory text
- Online demos
- Book lists
- Free code
- Course notes
- Lecture slides
- Course reading lists
- A list of course web pages at many universities
There are many areas for extension in the web pages, but they already link to more than 3000 resources.
These resources are subdivided into five areas. Of course, the boundaries are never distinct and we undoubtedly will also provoke a few dissenting opinions. However, we have tried to address the main work done by the IAPR community, as clustered into 3 core technology areas and 2 broad families of application areas:
1. Symbolic Pattern Recognition
2. Statistical Pattern Recognition
3. Machine Learning
4. 1D Signal Analysis
5. Computer vision/Image Processing/Machine Vision
Initial website development was by Christos Papadopoulos and Apostolos Antonacopoulos. Content entry was Edinburgh University PhD students Kisuh Ahn, Edwin Bonilla, Lei Chen, Tim Hospedales, Gail Sinclair, and Narayanan Unny E, supervised by Bob Fisher. Content advice supplied by by the 2006-8 Education Committee: Bruce Maxwell, Sudeep Sarkar, Xiaoyi Jiang, Laurent Heutte and
Sergios Theodoridis. Funding was provided by the EC funded euCognition network, The British Machine Vision Association and the UK's EPSRC. "
Prof. Robert B. Fisher, School of Informatics, Univ. of Edinburgh