Abstract. Methods based on local, viewpoint invariant features have proven capable of recognizing objects in spite of viewpoint changes, occlusion and clutter. However, these approaches fail when these factors are too strong, due to the limited repeatability and discriminative power of the features. As additional shortcomings, the objects need to be rigid and only their approximate location is found. We present an object recognition approach which overcomes these limitations. An initial set of feature correspondences is first generated. The method anchors on it and then gradually explores the surrounding area, trying to construct more and more matching features, increasingly farther from the initial ones. The resulting process covers the object with matches, and simultaneously separates the correct matches from the wrong ones. Hence, recognition and segmentation are achieved at the same time. Only very few correct initial matches suffice for reliable recognition. Experimental results on still images and television news broadcasts demonstrate the stronger power of the presented method in dealing with extensive clutter, dominant occlusion, large scale and viewpoint changes. Moreover non-rigid deformations are explicitly taken into account, and the approximative contours of the object are produced. The approach can extend any viewpoint invariant feature extractor. 1

Technical University. During my visit, a graduate student was kind enough to show me around Prague, including a visit to the Museum of Modern and Contemporary Art (Veletr˘zní Palác). It was there that I saw the sculpture

A method is presented for efficiently detecting bilateral symmetry on planar surfaces under perspective projection. The method is able to detect local or global symmetries, locate symmetric surfaces in complex backgrounds, and detect multiple incidences of symmetry. Symmetry is simultaneously evaluated across all locations, scales, orientations and under perspective skew. Feature descriptors robust to local affine distortion are used to match pairs of symmetric features. Feature quadruplets are then formed from these symmetric feature pairs. Each quadruplet hypothesises a locally planar 3D symmetry that can be extracted under perspective distortion. The method is posed independently of a specific feature detector or descriptor. Results are presented demonstrating the efficacy of the method for detecting bilateral symmetry under perspective distortion. Our unoptimised Matlab implementation, running on a standard PC, requires of the order of 20 seconds to process images with 1,000 feature points.

We propose an approach to identify and segment objects from scenes that a person (or robot) encounters in Activities of Daily Living (ADL). Images collected in those cluttered scenes contain multiple objects. Each image provides only a partial, possibly very different view of each object. An object instance discovery program must be able to link pieces of visual information from multiple images and extract the consistent patterns. Most papers on unsupervised discovery of object models are concerned with object categories. In contrast, this paper aims at identifying and extracting regions corresponding to specific object instances, e.g., two different laptops in the laptop category. By focusing on specific instances, we enforce explicit constraints on geometric consistency (such as scale, orientation), and appearance consistency (such as color, texture and shape). Using multiple segmentations as the basic building block, our program processes a noisy “soup ” of segments and extracts object models as groups of mutually consistent segments. Our approach was tested on three different types of image sets: two from indoor ADL environments and one from Flickr.com. The results demonstrate robustness of our program to severe clutter, occlusion, changes of viewpoint and interference from irrelevant images. Our approach achieves significant improvement over with two existing methods. 1.

We present a segmentation-based image descriptor and similarity measure and apply them for object recognition. A segmentation gives a middle-level image representation which is robust to the intensity and structure variations that cause problems for matching based on low-level descriptors. A segmentation descriptor can give more accurate and efficient matching of boundaries than methods based on boundary representations. We also propose to evaluate segmentation algorithms and metrics by their usefulness in recognition tasks. Our experiments on several standard databases show that with current segmentation techniques our segmentation descriptor and matching technique can provide robust matching results for recognition. 1.

� Combination with segmentation � New developments

We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the “central ” segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing and object detection perform competitively, and we also show promising results in shape based tracking. Index Terms shape matching, image segmentation, mutual information. I.