An end-to-end trainable ConvNet architecture, that learns to harness the power of shape representation for matching disparate image pairs, is proposed. Disparate image pairs are deemed those that exhibit strong affine variations in scale, viewpoint and projection parameters accompanied by the presence of partial or complete occlusion of objects and extreme variations in ambient illumination. Under these challenging conditions, neither local nor global feature-based image matching methods, when used in isolation, have been observed to be effective. The proposed correspondence determination scheme for matching disparate images exploits high-level shape cues that are derived from low-level local feature descriptors, thus combining the best of both worlds. A graph-based representation for the disparate image pair is generated by constructing an affinity matrix that embeds the distances between feature points in two images, thus modeling the correspondence determination problem as one of graph matching. The eigenspectrum of the affinity matrix, i.e., the learned global shape representation, is then used to further regress the transformation or homography that defines the correspondence between the source image and target image. The proposed scheme is shown to yield state-of-the-art results for both, coarse-level shape matching as well as fine point-wise correspondence determination.
Image matching techniques in the research literature can be broadly categorized as global shape-based techniques @cite_1 @cite_7 @cite_0 or local point-based @cite_19 @cite_17 techniques. Global shape-based matching techniques rely on extracting the overall shape of an object (or structure) within the image. High-level shape cues are extracted from the underlying images to compute a degree of similarity. Global shape-based matching techniques are further subclassified as region-based @cite_29 @cite_24 or contour-based @cite_7 @cite_0 @cite_33 . Contour-based methods exploit peripheral information to augment the underlying shape-based features. Shape skeleton-based contour matching @cite_7 and dynamic programming @cite_35 are used to compute a similarity measure such as shape context @cite_1 , chamfer distance @cite_33 or set matching-based contour similarity @cite_0 . Other contour-based matching methods include segment-based matching techniques such as hierarchical Procrustes matching @cite_23 , shape tree-based matching @cite_31 and triangle area-based matching @cite_16 . However, the aforementioned contour-based matching techniques fall short when dealing with significant articulations in the object shapes. Global region-based matching approaches characterize the underlying shapes using global descriptors such as Zernike moments @cite_29 which are invariant to affine transformations. Skeleton-based shape descriptors @cite_27 @cite_24 are better at capturing shape articulations, but their performance diminishes with increasing shape complexity.
@cite_35 @cite_33 @cite_7 @cite_29 @cite_1 @cite_0 @cite_19 @cite_24 @cite_27 @cite_23 @cite_31 @cite_16 @cite_17
2166160268 2148821982 2168709984 2138948290
In this paper, we propose a novel framework for contour based object detection. Compared to previous work, our contribution is three-fold. 1) A novel shape matching scheme suitable for partial matching of edge fragments. The shape descriptor has the same geometric units as shape context but our shape representation is not histogram based. 2) Grouping of partial matching hypotheses to object detection hypotheses is expressed as maximum clique inference on a weighted graph. 3) A novel local affine-transformation to utilize the holistic shape information for scoring and ranking the shape similarity hypotheses. Consequently, each detection result not only identifies the location of the target object in the image, but also provides a precise location of its contours, since we transform a complete model contour to the image. Very competitive results on ETHZ dataset, obtained in a pure shape-based framework, demonstrate that our method achieves not only accurate object detection but also precise contour localization on cluttered background. This paper compares two methods for object localization from contours: shape context and chamfer matching of templates. In the light of our experiments, we suggest improvements to the shape context: shape contexts are used to find corresponding features between model and image. In real images it is shown that the shape context is highly influenced by clutters; furthermore, even when the object is correctly localized, the feature correspondence may be poor. We show that the robustness of shape matching can be increased by including a figural continuity constraint. The combined shape and continuity cost is minimized using the Viterbi algorithm on features, resulting in improved localization and correspondence. Our algorithm can be generally applied to any feature based shape matching method. Chamfer matching correlates model templates with the distance transform of the edge image. This can be done efficiently using a coarse-to-fine search over the transformation parameters. The method is robust in clutter, however, multiple templates are needed to handle scale, rotation and shape variation. We compare both methods for locating hand shapes in cluttered images, and applied to word recognition in EZ-Gimpy images. We introduce a shape detection framework called Contour Context Selection for detecting objects in cluttered images using only one exemplar. Shape based detection is invariant to changes of object appearance, and can reason with geometrical abstraction of the object. Our approach uses salient contours as integral tokens for shape matching. We seek a maximal, holistic matching of shapes, which checks shape features from a large spatial extent, as well as long-range contextual relationships among object parts. This amounts to finding the correct figure ground contour labeling, and optimal correspondences between control points on around contours. This removes accidental alignments and does not hallucinate objects in background clutter, without negative training examples. We formulate this task as a set-to-set contour matching problem. Naive methods would require searching over 'exponentially' many figure ground contour labelings. We simplify this task by encoding the shape descriptor algebraically in a linear form of contour figure ground variables. This allows us to use the reliable optimization technique of Linear Programming. We demonstrate our approach on the challenging task of detecting bottles, swans and other objects in cluttered images. We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation scheme is developed based on a tree matching approach. Instead of traditional concatenation-style image location-based feature encoding, we extract features adaptively in the context of human poses and train a kernel-SVM classifier to separate human nonhuman patterns. Specifically, the features are collected in the local context of poses by tracing around the estimated shape boundaries. We also introduce an approach to multiple occluded human detection and segmentation based on an iterative occlusion compensation scheme. The output of our learned generic human detector can be used as an initial set of human hypotheses for the iterative optimization. We evaluate our approaches on three public pedestrian data sets (INRIA, MIT-CBCL, and USC-B) and two crowded sequences from Caviar Benchmark and Munich Airport data sets.