Conditional Iterative Decoding of Two Dimensional Hidden Markov Models

Two Dimensional Hidden Markov Models (2D-HMMs) find many applications on the field of computer vision/image analysis. In this talk, I am going to explain a new algorithm for decoding of 2D-HMMs. The proposed algorithm consists of conditional iterative updates that "communicate" through the joint posterior state probabilities. We also show that the proposed algorithm gives promising results on both synthetic and deformable face recognition scenarios.

Benchmark for Segmentation Evaluation

It describes ongoing work on creating a benchmarking and validation dataset for biological image segmentation. While the primary target is biological images, we believe that the dataset would be of help to researchers working in image segmentation and tracking in general. The motivation for creating this resource comes from the observation that while there are a large number of effective segmentation methods available in the research literature, it is difficult for the application scientists to make an informed choice as to what methods would work for her particular problem. No one single tool exists that is effective on a diverse set of application contexts and different methods have their own strengths and limitations. We describe below three different classes of data, ranging in scale from subcellular to cellular to tissue level images, each of which pose their own set of challenges to image analysis. Of particular value to the image processing researchers is that the data comes with associated ground truth information that can be used to evaluate the effectiveness of different methods. The analysis and evaluation are also integrated into a database framework that is available online at http://dough.ece.ucsb.edu.

Our recent Workshop on "Bio-Image Informatics: Biological Imaging, Computer Vision and Data Mining" was a great success with over 150 attendees from bioimaging and related fields.

UCSB, Santa Barbara, CA, USA, January 17-18, 2008.