Object detection in cluttered, natural scenes has a high
complexity since many local observations compete for object
hypotheses. Voting methods provide an efficient solution
to ...
In this paper, we propose a learning-based demosaicing and a restoration error detection. A Vector Quantization (VQ)based method is utilized for learning. We take advantage of a s...
In this work we construct scale invariant descriptors (SIDs) without requiring the estimation of image scale; we thereby avoid scale selection which is often unreliable. Our start...
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the not...
This paper revisits the classical problem of detecting interest points, popularly known as "corners," in 2D images by proposing a technique based on fitting algebraic sh...