This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative classifier. Text...
Object recognition technology has matured to a point at which exciting applications are becoming possible. Indeed, industry has created a variety of computer vision products and se...
We present a component-based system for object detection and identification. From a set of training images of a given object we extract a large number of components which are clust...
Bernd Heisele, Ivaylo Riskov, Christian Morgenster...
Abstract. Many applications of 3D object recognition, such as augmented reality or robotic manipulation, require an accurate solution for the 3D pose of the recognized objects. Thi...
We introduce a stochastic model to characterize the online computational process of an object recognition system based on a hierarchy of classifiers. The model is a graphical netwo...
Abstract. We propose a generative approach to the problem of labeling images containing configurations of objects from multiple classes. The main building blocks are dense statisti...
Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances ...
Jean Ponce, Tamara L. Berg, Mark Everingham, David...
Traditional approaches to object detection only look at local pieces of the image, whether it be within a sliding window or the regions around an interest point detector. However, ...
Kevin P. Murphy, Antonio B. Torralba, Daniel Eaton...
Abstract. We consider the problem of detecting a large number of different classes of objects in cluttered scenes. We present a learning procedure, based on boosted decision stumps...
Antonio B. Torralba, Kevin P. Murphy, William T. F...