Shape Matching and Object Recognition

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45 % correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces

Skewed Mirror Symmetry for Depth Estimation in 3D Line-Drawings

We aim to reconstruct three-dimensional polyhedral solids from axonometric-like line sketches. A new approach is proposed to make use of planes of mirror symmetry detected in sketches. Taking into account mirror symmetry of such polyhedra can significantly improve the reconstruction process. Applying symmetry as a regularity in optimisation-based reconstruction is shown to be adequate by itself, without the need for other inflation techniques or regularities. Furthermore, we show how symmetry can be used to reduce the size of the reconstruction problem, leading to a reduction in computing time.This work was partially supported by Fundació Caixa Castelló-Bancaixa under the Universitat Jaume I program for Research Promotion (Project P1-1B2002-08, titled “From sketch to model: new user interfaces for CAD systems”), and by facilities provided by Cardiff University Computer Science Department during a visit by the first author

Part-Based Feature Synthesis for Human Detection

Abstract. We introduce a new approach for learning part-based object detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using a new feature selection algorithm for linear SVM, termed Predictive Feature Selection (PFS), which is governed by weight prediction. The algorithm makes it possible to choose from O(10 6) features in an efficient but accurate manner. We analyze the validity and behavior of PFS and empirically demonstrate its speed and accuracy advantages over relevant competitors. We present an empirical evaluation of our method on three human detection datasets including the current de-facto benchmarks (the INRIA and Caltech pedestrian datasets) and a new challenging dataset of children images in difficult poses. The evaluation suggests that our approach is on a par with the best current methods and advances the state-of-the-art on the Caltech pedestrian training dataset