[VASC Seminar] Combining Local Appearance and Motion Cues for Occlusion Boundary

Title: Combining Local Appearance and Motion Cues for Occlusion Boundary
Detection
Speaker: Andrew Stein

Abstract:
Building on recent advances in the detection of appearance edges from multiple local cues, we present an approach for detecting occlusion boundaries which also incorporates local motion information. We argue that these boundaries have physical significance which makes them important for many high-level vision tasks and that motion offers a unique, often critical source of additional information for detecting them. We provide a new dataset of natural image sequences with labeled
occlusion boundaries, on which we learn a classifier that leverages appearance cues along with motion estimates from either side of an edge. We demonstrate improved performance for pixelwise differentiation of occlusion boundaries from non-occluding edges by combining these weak local cues, as compared to using them separately. The results are suitable as improved input to subsequent mid- or high-level reasoning methods.


Related work

Title: Background and Scale Invariant Feature Transform (BSIFT)

Abstract:
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformations, such as rotation, scaling, or affine warping. When patches overlap object boundaries, however, errors in both detection and matching will almost certainly occur due to inclusion of unwanted background pixels. This is common in real images, which often contain significant background clutter, objects which are not heavily textured, or objects which occupy a relatively small portion of the image. We suggest improvements to the popular Scale Invariant Feature Transform (SIFT) which incorporate local object boundary information. The resulting feature detection and descriptor creation processes are invariant to changes in background. We call this method the Background and Scale Invariant Feature Transform (BSIFT). We demonstrate BSIFT's superior performance in feature detection and matching on synthetic and natural images.