Most of the work on 3-D object recognition from range data has used an alignment-verification approach in which a specific 3-D object is matched to an exact instance of the same o...
Salvador Ruiz-Correa, Linda G. Shapiro, Marina Mei...
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
Object recognition is challenging due to high intra-class
variability caused, e.g., by articulation, viewpoint changes,
and partial occlusion. Successful methods need to strike a...
Object search in a visual scene is a highly challenging and computationally intensive task. Most of the current object detection techniques extract features from images for classi...
We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with a...