The grand goal of Computer Vision is to generate an automatic description of an image based on its visual content. Category level object detection is an important building block towards such capability. The first part of this talk deals with three established object detection techniques in Computer Vision, their shortcomings and how they are improved. i) Hough Voting methods efficiently handle the high complexity of multi-scale, category-level object detection in cluttered scenes.
However, the primary weakness of this approach is that mutually dependent local observations independently vote for intrinsically global object properties such as object scale. We model the feature dependencies by presenting an objective function that combines various intimately related problems in Hough Voting. ii) Shape is a highly prominent characteristic of objects that human vision utilizes for detecting objects. However, shape poses significant challenges for object detection in cluttered scenes: Object form is an emergent property that cannot be perceived locally but becomes available only once the whole object has been detected. Thus we address the detection of objects and assembling of their shape simultaneously in a Max-Margin Multiple Instance Learning framework, while avoiding fragile bottom-up grouping in query images altogether. iii) Chamfer matching is a widely used technique for detecting objects because of its speed. However, it treats objects as being a mere sum of the distance transformation of all their contour pixels. Also, spurious matches in background clutter is a huge problem for chamfer matching. We address these two issues by a) applying a discriminative approach to distance transformation computation in chamfer matching and b) estimating the accidentalness of a foreground template match by a small dictionary of simple background contours.
The second part of the talk explores the question: what insights can automatic object detection and intra-category object relationships bring to art historians ? It turns out that techniques from Computer Vision have helped the art historians in discovering different artistic workshops within an Upper German manuscript, understanding the variations of art within a particular school of design and studying the transitions across artistic styles by 1-d ordering of objects. Obtaining such insights manually is a tedious task and Computer Vision made the job of art historians easier.
1. Pradeep Yarlagadda and Björn Ommer From Meaningful Contours to Discriminative Object Shape, ECCV 2012.
2. Pradeep Yarlagadda, Angela Eigenstetter and Björn Ommer Learning Discriminative Chamfer Regularization, BMVC 2012.
3. Pradeep Yarlagadda, Antonio Monroy and Björn Ommer Voting by Grouping Dependent Parts, ECCV 2010.
4. Pradeep Yarlagadda, Antonio Monroy, Bernd Carque and Björn Ommer Recognition and Analysis of Objects in Medieval Images, ACCV (e-heritage) 2010.
5. Pradeep Yarlagadda, Antonio Monroy, Bernd Carque and Björn Ommer Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections, Computer Vision and Image Analysis of art SPIE, 2010.