We present an approach to visual object-class recognition and segmentation based on a pipeline that combines multiple, holistic figure-ground hypotheses generated in a bottom-up,...
We introduce an approach to accurately detect and segment partially occluded objects in various viewpoints and scales. Our main contribution is a novel framework for combining obj...
Scalability of object detectors with respect to the number of classes is a very important issue for applications where many object classes need to be detected. While combining sin...
Most successful object recognition systems are based on a visual alphabet of quantised gradient orientations. Here, we introduce two richer image feature alphabets for use in obje...
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...