Many image retargeting algorithms, despite aesthetically carving images smaller, pay limited attention to image browsing tasks where tiny thumbnails are presented. When applying t...
Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new r...
Background modelling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent resear...
Rui Caseiro, João F. Henriques, Pedro Martins, Jo...
Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dic...
Drawing a box around an intended segmentation target has become both a popular user interface and a common output for learning-driven detection algorithms. Despite the ubiquity of...
Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accom...
Aurelien Lucchi, Yunpeng Li, Xavier Boix, Kevin Sm...
This paper presents a novel method for feature description based on intensity order. Specifically, a Local Intensity Order Pattern(LIOP) is proposed to encode the local ordinal i...
We propose a novel patch-based image representation that is useful because it (1) inherently detects regions with repetitive structure at multiple scales and (2) yields a paramete...
Lena Gorelick, Andrew Delong, Olga Veksler, Yuri B...
We describe an approach to category-level detection and viewpoint estimation for rigid 3D objects from single 2D images. In contrast to many existing methods, we directly integrat...
Daniel Glasner, Meirav Galun, Sharon Alpert, Ronen...