The ultimate goal of most image understanding systems is to produce an unambigu ous D representation of the local visual environment This representation can then be employed by robotic systems to e ect some meaningful action A great deal of research e ort is concerned with the development of visual and manipulative representations and their generative processes which allow for the e ective link...

1. Introduction Scene or image understanding deals with the problem of making a computer " understand " the world behind the image. This can be done in a number of different ways. In this project, we will deal with a kind of problem of scene understanding, semantic image segmentation or pixel labeling. Multi-class image segmentation or pixel labeling does more than the task of object recognitio...

Benchmarks and test suites are an essential element of the architectural evaluation process. At the conclusion of the last DARPA workshop on vision benchmarks to test the performance of parallel architectures, it was recommended that the DARPA Image Understanding Benchmark [Weems, 1991] be extended with a second level task to add motion and tracking to the original task. We have now developed t...

This book, according to its preface, is aimed mainly at researchers in ireas such as remote sensing, medical image processing, robot \ ision. military reconnaissance, Lartography and geology who are Lonsidering the use of neural networks for their processing tasks. To this end, the book describes neural network architectures and algorithms as applied to basic image processing tasks, and gives e...

Performance Characterization of Image Understanding Algorithms by Visvanathan Ramesh Chairperson of Supervisory Committee: Professor Robert M Haralick Department of Electrical Engineering Image Understanding (IU) Systems are complex and they are composed of di erent algorithms applied in sequence. A system for model-based recognition has three essential components: feature extraction, grouping ...

In Document Image Understanding, one of the fundamental tasks is that of recognizing semantically relevant components in the layout extracted from a document image. This process can be automatized by learning classifiers able to automatically label such components. However, the learning process assumes the availability of a huge set of documents whose layout components have been previously manu...

The 16th Conference on Medical Image Understanding and Analysis (MIUA) was hosted in Swansea University in July 2012. MIUA is the principal UK forum for communicating research progress within the community interested in image analysis applied to medicine and related biological science. It is a single-track conference with oral and poster presentations. Authors were asked to submit 6-page techni...

The VEIL system is pioneering two thrusts within the field of image understanding that until now have received relatively little attention. First, VEIL reasons at a semantic level about scene information, using a knowledge base that augments an original image understanding (IU) model (representing the output of a lower level IU system) with structural and functional information. VEIL can apply ...

Learning is becoming a central problem in trying to understand intelligence and in trying to develop intelligent machines. This paper describes some recent work on developing machines that learn in the domains of vision and graphics. We will introduce an underlying theory which connects function approximation techniques, neural network architectures and statistical methods. While these techniqu...