Our objective is transfer training of a discriminatively trained object category detector, in order to reduce the number of training images required. To this end we propose three ...
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 sce...
We propose a novel statistical manifold modeling approach that is capable of classifying poses of object categories from video sequences by simultaneously minimizing the intra-cla...
Liang Mei, Jingen Liu, Alfred Hero, Silvio Savares...
In this paper we propose a framework for learning a regression function form a set of local features in an image. The regression is learned from an embedded representation that re...
This paper focuses on the problem of word detection and recognition in natural images. The problem is significantly more challenging than reading text in scanned documents, and h...
In this paper, we describe an interest point detector using edge foci. Unlike traditional detectors that compute interest points directly from image intensities, we use normalized...
We address the problem of large-scale annotation of web images. Our approach is based on the concept of visual synset, which is an organization of images which are visually-simila...
David Tsai, Yushi Jing, Yi Liu, Henry Rowley, Serg...
We describe a scalable approach to 3D smooth object retrieval which searches for and localizes all the occurrences of a user outlined object in a dataset of images in real time. T...
We describe a method for generating N-best configurations from part-based models, ensuring that they do not overlap according to some user-provided definition of overlap. We ext...