Prizes and Paper Awards to be Presented at ICPR 2010

K.S. Fu Prize

Horst Bunke Research Group on Computer Vision and Artificial Intelligence IAM University of Bern, Switzerland bunkeiam.unibe.ch Towards the Unification of Structural and Statistical Pattern Recognition Statistical pattern recognition is characterized by the use of feature vectors for pattern representation, while the structural approach is based on symbolic data structures, such as strings, trees, and graphs. Clearly, symbolic data structures have a higher representational power than feature vectors because they allows one to directly model relationships that may exist between the individual parts of a pattern. However, many operations that are needed in classification, clustering, and other pattern recognition tasks are not defined for graphs. Consequently, there has been a lack of algorithmic tools in the domain of structural pattern recognition since its beginning. This talk gives an overview of the development of the field of structural pattern recognition and shows various attempts to bridge the gap between statistical and structural pattern recognition, i.e. to make algorithmic tools originally developed for feature vectors applicable to symbolic data structures.

J.K. Aggarwal Prize

Antonio Torralba Computer Science and Artificial Intelligence Laboratory Dept. of Electrical Engineering and Computer Science MIT, USA torralbacsail.mit.edu Scene and Object Recognition in Context Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. Most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. However, in the real world, objects tend to co-vary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expect to be; this also increases performance, by rejecting image patterns that appear to look like the target object but that are in unlikely places. As the field moves into integrated systems that try to recognize many object classes and learn about contextual relationships between objects, the lack of large annotated datasets hinders the fast development of robust solutions. In this talk I will describe recent work on visual scene understanding that try to build integrated models for scene and object recognition, emphasizing the power of large database of annotated images in computer vision.

Piero Zamperoni Best Student Paper Award (PZBSPA)

Best Biometrics Student Paper Award (BBSPA)

IBM Best Student Paper Awards

Best Industry Related Paper Award (BIRPA)

Best Scientific Paper Awards