We describe an object detection system based on mixtures of multiscale deformable part
models. Our system is able to represent highly variable object classes and achieves state-of …

Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

We introduce an exemplar model that can learn and generate a region of interest around
class instances in a training set, given only a set of images containing the visual class. The …

A bottom-up visual saliency detector is proposed, following a decision-theoretic formulation
of saliency, previously developed for top-down processing (object recognition)[5]. The …

A novel framework is proposed for the design of cost-sensitive boosting algorithms. The
framework is based on the identification of two necessary conditions for optimal cost …

We investigated the computational properties of natural object hierarchy in the context of
constellation object class models, and its utility for object class recognition. We first observed …

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 …

A decision-theoretic formulation of visual saliency, first proposed for top-down processing
(object recognition)(Gao & Vasconcelos,), is extended to the problem of bottom-up saliency …

We present a new method for training deformable models. Assume that we have training
images where part locations have been labeled. Typically, one fits a model by maximizing …

A standard approach to learning object category detectors is to provide strong supervision in
the form of a region of interest (ROI) specifying each instance of the object in the training …