Dr. Jason Lawrence is an Assistant Professor in the Department of Computer Science. He is a lead researcher on an NSF-funded project that is investigating a radically different approach to image processing tasks. While many important image processing tasks involve solving some type of inverse problem (e.g., eliminating noise, enhancing a black-and white image with plausible colors, increasing t...

In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document de...

Exemplar-based learning or, equally, nearest neighbour methods have recently gained interest from researchers in a variety of computer science domains because of the prevalence of large amounts of accessible data and storage capacity. In computer vision, these types of technique have been successful in several problems such as scene recognition, shape matching, image parsing, character recognit...