GazoPa

GazoPa is a similar image search service on the web in private beta by Hitachi. Users can search images from the web based on user’s own photo, drawings, images found on the web and keywords. GazoPa enables users to search for a similar image from characteristics such as a color or a shape extracted from an image itself. There are abundant quantities of images on the web, however many of these simply cannot be described by keywords. Since GazoPa uses image features to search other similar images, a vast range of images can be retrieved from the web. GazoPa is a new visual search service that can navigate users to new territories on the web. http://www.gazopa.com/sign_in

Multimodal and Mobile Personal Image Retrieval: A User Study

These last months, I have been collaborating to a research project on Multimodal Information Retrieval of digital pictures collected through camera phones. Recently, one of the papers resuming the results of the research was presented at the International Workshop of Mobile Information Retrieval, held in conjunction with SIGIR in Singapore. Here goes the abstract and the URL to download the paper.
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.
Source

Adobe Photoshop Lightroom 2

Adobe Photoshop Lightroom 2 is best categorized as a Digital Processor. That is, from bringing images to your computer, cataloging them for later retrieval (and, if you want, backing them up to insure protection against accidental loss), enhancing and fine tuning your images, all the way to printing and/or digital distribution—one can do it all from Lightroom. However, one of the strongest reasons to use Lightroom is the opportunity for playing with images with no concern about how many versions and variations of the image you create without screwing up your original image. Any alteration you make on an image in Lightroom is only how Lightroom lets you "see" the image. Nothing is changed in the image itself unless you save a copy with those changes. The biggest negative about Lightroom is that the interface constantly changes, depending upon what you've clicked. This makes "hacking" the program a challenge, and working with Lightroom isn't helped by the manual that doesn't properly explain the conditions where you will see what is being explained. Despite the complex learning curve, there is much to like in Lightroom.

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Neural Networks on C#

It is known fact, that there are many different problems, for which it is difficult to find formal algorithms to solve them. Some problems cannot be solved easily with traditional methods; some problems even do not have a solution yet. For many such problems, neural networks can be applied, which demonstrate rather good results in a great range of them. The history of neural networks starts in 1950-ies, when the simplest neural network's architecture was presented. After the initial work in the area, the idea of neural networks became rather popular. But then the area had a crash, when it was discovered that neural networks of those times are very limited in terms of the amount of tasks they can be applied to. In 1970-ies, the area got another boom, when the idea of multi-layer neural networks with the back propagation learning algorithm was presented. From that time, many different researchers have studied the area of neural networks, what lead to a vast range of different neural architectures, which were applied to a great range of different problems. For now, neural networks can be applied to such tasks, like classification, recognition, approximation, prediction, clusterization, memory simulation, and many other different tasks, and their amount is growing.
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.
http://www.codeproject.com/KB/recipes/aforge_neuro.aspx

Special Issue: Advances in Medical Intelligent Decision Support Systems

Advances in Medical Intelligent Decision Support Systems.
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.

WordsEye

Create 3D scenes using language.Share them with others

Here is an example:

Text: "a tiny grey manatee is in the aquarium. it is facing right. the fishdead of the aquarium is invisible. the manatee is two inches above the tank_sand of the aquarium. the ground is tile. there is a large brick wall behind the aquarium."

Result:

http://www.wordseye.com/frontpage