Jamie Daniel Joseph Shotton
The recognition of categories of objects in images has become a central topic in computer vision. Automatic visual recognition systems are rapidly becoming central to applications such as image search, robotics, vehicle safety systems, and image editing. This work ad- dresses three sub-problems of recognition: image classiﬁcation, object detection, and semantic segmentation. The task of classiﬁcation is to determine whether an object of a particular category is present or not. Object detection aims to localize any objects of the category. Semantic segmentation is a more complete image understanding, whereby an image is partitioned into coherent regions that are as- signed meaningful class labels. This thesis proposes novel discriminative learning approaches to these problems.
Our primary contributions are threefold. Firstly, we demonstrate that the contours (the outline and interior edges) of an object are, alone, sufﬁcient for accurate visual recognition. Secondly, we pro- pose two powerful new feature types: (i) a learned codebook of con- tour fragments matched with an improved oriented chamfer distance, and (ii) a set of texture-based features that simultaneously exploit local appearance, approximate shape, and appearance context. The efﬁcacy of these new features types is evaluated on a wide variety of datasets. Thirdly, we show how, in combination, these two largely orthogonal feature types can substantially improve recognition performance above that achieved by either alone