In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization p...
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 study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method ...
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev...
We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to d...
: The network management area has some proposals to use XML to encode information models and managed object instances. In this paper we present a solution to dynamically create SNM...
Testing object-oriented (OO) software is critical because OO languages are commonly used in developing modern software systems. In testing OO software, one important and yet chall...
Hojun Jaygarl, Sunghun Kim, Tao Xie, Carl K. Chang
Learning a new object class from cluttered training images is very challenging when the location of object instances is unknown. Previous works generally require objects covering a...
SystemC-Plus from the ODETTE project provides the ability to simulate and synthesise object-oriented specifications into hardware. The current ODETTE compiler translates each obj...
Abstract. Most current approaches to recognition aim to be scaleinvariant. However, the cues available for recognizing a 300 pixel tall object are qualitatively different from tho...
We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object inst...
Alexander Thomas, Vittorio Ferrari, Bastian Leibe,...