—Participants learned through feedback to haptically classify the identity of upright versus inverted versus scrambled faces depicted in simple 2D raised-line displays. We invest...
Tara A. McGregor, Roberta L. Klatzky, Cheryl L. Ha...
In this paper, we propose an approach to accurately localize detected objects. The goal is to predict which features pertain to the object and define the object extent with segme...
This paper describes an object detection framework that learns the discriminative co-occurrence of multiple features. Feature co-occurrences are automatically found by Sequential F...
We present a new technique for extracting local features from images of architectural scenes, based on detecting and representing local symmetries. These new features are motivate...
We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature red...
Bernd Heisele, Thomas Serre, Sayan Mukherjee, Toma...