Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can w...
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
We present a novel method for unsupervised classification, including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, som...
Leonid Karlinsky, Michael Dinerstein, Dan Levi, Sh...
This paper argues that tracking, object detection, and model-building are all similar activities. We describe a fully automatic system that builds 2D articulated models known as pi...
In this paper the problem of obtaining 3D models from image sequences is addressed. The proposed method deals with uncalibrated monocular image sequences. No prior knowledge about...