| The UMass RADIUS Project: A System for Automated Site Model Acquisition and Extension |
MAY 1999 |
31 pages |
| Authors:
Robert C. Collins; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
|
 | Under the DARPA RADIUS program, the University of Massachusetts (UMass) developed techniques to automatically populate a site model with 3-D building models extracted from multiple, overlapping images. The Automated Site Construction, Extension, Detection and Refinement (ASCENDER) system incorporatesseveral key ideas. First, 3-D reconstruction is based on geometric features thatremain stable under a wide range of viewing and lighting conditions. Second, rigorous photogrammetric camera models are used to describe the relationship ... |
|
| The UMASS RADIUS Project |
MAR 1998 |
27 pages |
| Authors:
Robert T. Collins; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
|
| The Automated Site Construction, Extension, Detection, and Refinement system (ASCENDER) has been developed to automatically populate a site model with buildings extracted from multiple overlapping views. Version 1.0 of the system has been delivered for evaluation on classified imagery. Evaluation results on an unclassified Fort Hood, TX data set are presented here. Extensions to the system that allow it to detect a wide range of building classes, including peaked roof ... |
|
| Learning Object and Scene Recognition Strategies |
MAR 1998 |
63 pages |
| Authors:
Edward M. Riseman; Allen R. Hanson; Bruce Draper; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
|
| This report summarizes activities performed as follows: (1) development of the Schema Learning System (SLS) from Draper thesis; (2) learning multi-variate decision trees for object classification; (3) real-time color classification in U6V domain via table look-up; (4) interactive real-time classification visualization for training data specification; (5) new formulation of approach to learning via Markov decision processes. |
|
| High Speed Computation for Visualization |
07 MAR 96 |
16 pages |
| Authors:
Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
|
| This document is the final report on contract number DAAH04-95-1-0068 awarded to the Computer Vision laboratory at the University of Massachusetts for the purchase of a high performance visualization workstation. It describes the equivalent purchased under this contract and contains brief status summaries of two DOD sponsored research programs making the heaviest use of the equipment. In the first of these, the Daedalus Battlefield Visualization System is being designed to ... |
|
| Automated Acquisition of Object Recognition Strategies for Image Exploitation |
AUG 95 |
63 pages |
| Authors:
Bruce A. Draper; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This effort attempts to solve a crucial problem of knowledge-based scene interpretation by building proper, more efficient, recognition strategies. The proposed system will automatically learn object recognition strategies with the goal of learning how to recognize objects from a combination of training images and a library of visual sources. This project will incorporate two types of learning techniques, Hypothesis Generation Learning, and Hypothesis Verification. Recognition graphs will represent three control ... |
|
| Image Understanding Architecture Prototype Evaluation and Development |
JUN 1993 |
331 pages |
| Authors:
Charles C. Weems; Edward M. Riseman; Allen R. Hanson; James Burrill; Martin Herbordt; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The primary goal of this research was to complete the development and proof-of-concept prototype of a 1/64th slice of a parallel architecture to support image understanding, and to test and evaluate the architecture so the next generation Image Understanding Architecture (IUA) can be specified. The majority of the hardware effort has taken place at Hughes Research Laboratories, Malibu, California, although U Mass has principle responsibility for designing the IUA architecture ... |
|
| Motion Analysis and Object Recognition for Autonomous Navigation |
APR 92 |
41 pages |
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The research in computer vision described in this final report is directed towards the achievement of autonomous vehicle navigation using passive visual sensing. For a modeled environment, we have implemented a navigation system incorporating reactive planning, and based on the identification of known landmarks in the 3D scene. Robust algorithms have been demonstrated for the recovery of pose--the position and orientation of the camera--from model matching between the image and ... |
|
| Motion Analysis and Object Recognition for Autonomous Navigation |
OCT 91 |
|
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
 | The research in computer vision described in this final report is directed towards the achievement of autonomous vehicle navigation using passive visual sensing. Navigation in both modeled and unknown environments has beer investigated; in addition, research has been carried out in related areas of static image interpretation. For a modeled environment, we have implemented a navigation system incorporating reactive planning, and based on the identification of known landmarks in the ... |
|
| Image Understanding Architecture |
SEP 91 |
63 pages |
| Authors:
Charles C. Weems; Edward M. Riseman; Allen R. Hanson; James Burrill; Martin Herbordt; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The primary goal of the Image Understanding Architecture (IUA) project was to build a proof-of-concept prototype of a 1/64th slice of a parallel architecture to support real-time, knowledge-based image understanding, and develop the software support environment that will be needed to utilize the hardware. The unique feature of the IUA is that it tightly couples three distinctly different parallel architectures whose capabilities are matched to the computational requirements of the ... |
|
| Final Report on Contract F30602-91-C-0037 (Massachusetts University) |
91 |
54 pages |
| Authors:
Bruce A. Draper; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST
|
| Over the past twenty-plus years of computer vision research, a wide variety of algorithms have been developed to solve many visual subproblems, ranging from edge extraction to vanishing point analysis to geometric model matching. Despite these advances, however, very few systems have been built that exploit the information in images to solve practical problems. The problem is a lack of understanding of how these algorithms (and representations) should be combined; ... |
|
| Dynamic Image Interpretation for Autonomous Vehicle Navigation |
AUG 89 |
61 pages |
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This report presents the results of the Dynamic Image Interpretation for the Autonomous Vehicle Navigation project for the time period 2.26.85 to 7/ 12/89. The purpose of the project is to develop algorithms and tools to enable a robotic ground vehicle to navigate autonomously through realistic landscapes. In this final report, we summarize our accomplishments in constructing robust algorithms to be used for vehicle navigation as well as tools that ... |
|
| Dynamic Image Interpretation For Autonomous Vehicle Navigation |
AUG 89 |
60 pages |
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This report presents the results of the Dynamic Image Interpretation for the Autonomous Vehicle Navigation project for the time period 2/26/85 to 7/ 12/89. The purpose of the project is to develop algorithms and tools to enable a robotic ground vehicle to navigate autonomously through realistic landscapes. In this final annual report, we summarize our accomplishments in constructing robust algorithms to be used for vehicle navigation as well as tools ... |
|
| The Image Understanding Architecture Project |
MAR 89 |
70 pages |
| Authors:
Charles C. Weems; Steven P. Levitan; Allen R. Hanson; Edward M. Riseman; David Shu; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The primary goal of the Image Understanding Architecture (IUA) project is to build a proof-of-concept prototype of a 1/64th slice of a next generation vision architecture, and develop the software support environment that will be needed to utilize the hardware. The second year of this program has focussed on extensions to the IUA software simulator programming environment, the development of library routines and demonstration software for the IUA, construction of ... |
|
| Dynamic Image Interpretation for Autonomous Vehicle Navigation |
SEP 88 |
33 pages |
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This report presents the results of the project on Dynamic Image Interpretation for Autonomous Land Vehicle (ALV) Navigation for the time period 2/26/87--2/25/88. The purpose of the ALV project is to develop algorithms and tools to enable a vehicle to navigate autonomously through realistic landscapes. Contents: Visual Motion Analysis- Computation of the Optical Flow Field; The Recovery of Environmental Motion and Structure from a Mobile Vehicle; Alternatives to General Motion ... |
|
| Recognizing 3 D Objects from 2D Images Using Structural Knowledge Base of Genetic Views |
31 AUG 88 |
95 pages |
| Authors:
Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| Model-based object recognition is an essential task for mobile robotics and assembly. Given an image of a scene containing one or more objects from unknown viewpoints, the goal is to efficiently recognize those objects for which there is sufficient evidence. At the University of Massachusetts, we are developing a model-based object recognition system which is capable of recognizing objects from a large data base of models and from arbitrary viewpoints. ... |
|
| The Image Understanding Architecture Project |
APR 88 |
76 pages |
| Authors:
Charles C. Weems; Steven P. Levitan; Allen R. Hanson; Edward M. Riseman; David B. Shu; J. G. Nash; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This report presents the results of the Image Understanding Architecture (IUA) project for the first year of its two-year contract period. The purpose of the IUA project is to design and construct a next-generation parallel processor that specifically addresses the needs of real-time computer vision applications. The current effort involves the construction of a proof-of- concept, 1/64th scale prototype IUA system (hardware and software) that will serve as the basis ... |
|
| Representation and Control in the Interpretation of Complex Scenes |
87 |
|
| Authors:
Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The system being developed, called VISIONS, is an investigation into issues of general computer vision. The goal is to provide an analysis of color images of outdoor scenes, from segmentation through symbolic interpretation. The output of the system is intended to be a symbolic representation of the three- dimensional world depicted in the two-dimensional image, including the naming of objects, their placement in three-dimensional space, and the ability to predict ... |
|
| Dynamic Image Interpretation for Autonomous Vehicle Navigation |
SEP 86 |
|
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The contractor's Autonomous Land Vehicle Project has been concerned with a variety of problems associated with sensor momotion analysis and dynamic image interpretation of autononmous navigation. Ling-range research goals include: 1. Determine the motion parameters of a senor relative to the static environment. 2. Distinguish moving objects from the static environment and determine their motion parameters. 3. Develop algorithms for tracking and predicting the motion and environmental location of the ... |
|
| Image Understanding Research at the University of Massachusetts, |
JUN 1983 |
|
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The major focus of the DARPA funded research program revolves around issues of dynamic image processing. The authors have been examining techniques for recovery of environmental information, such as depth maps of the visible surfaces, from a sequence of images produced by a sensor in motion. Algorithms that appear robust have been developed for constrained sensor motion such as pure translation, pure rotation, and motion constrained to a plane. Interesting ... |
|
| Processing Cones: A Computational Structure for Image Analysis |
DEC 1981 |
|
| Authors:
Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| A layered hierarchical parallel array architecture for image analysis applications, referred to as a processing cone, is described and sample algorithms are presented. A fundamental characteristic of the structure is its hierarchical organization into two-dimensional arrays of decreasing resolution. In this architecture, a protypical function is defined on a local window of data and applied uniformly to all windows in a parallel manner. Three basic modes of processing are supported ... |
|
| Studies in Global and Local Histogram-Guided Relaxation Algorithms |
06 AUG 1980 |
|
| Authors:
Paul A. Nagin; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
|
| Edge Relaxation and Boundary Continuity. |
MAY 1980 |
|
| Authors:
Allen R. Hanson; Edward M. Riseman; Frank C. Glazer; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| Many image analysis tasks require the construction of a boundary representation as a means of partitioning an image. This paper develops a parallel relaxation algorithm for updating initial heuristic estimates of the likelihood of edges so that continuous boundaries are formed. Bayesian probability theory is used to analyze the probability updating of a single edge based upon the joint probabilities of the edges in its local surrounding context. The relationships ... |
|
| Experiments in Schema-Driven Interpretation of a Natural Scene. |
APR 1980 |
110 pages |
| Authors:
Cesare C. Parma; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The system under development, VISIONS, is an investigation into general issues in the construction of computer vision systems. The goal is to provide an analysis of color images of outdoor scenes, from segmentation (or partitioning) of an image through the final stages of symbolic interpretation of that image. The output of the system is intended to be a symbolic representation of three-dimensional world depicted in the two-dimensional image, including the ... |
|
| Region Extraction and Description through Planning. |
MAY 1977 |
|
| Authors:
Paul A. Nagin; Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This paper examines several image segmentation algorithms which have been explored in the development of the VISIONS system. Each of these algorithms can be viewed as a variation on a basic theme: the clustering of activity in feature space via histogram analysis, mapping these clusters back onto the image, and then isolating regions by analysis of the spatial relationships of the cluster labels. It is shown that the interaction between ... |
|
| A Progress Report on VISIONS: Representation and Control in the Construction of Visual Models. |
JUL 1976 |
|
| Authors:
Allen R. Hanson; Edward M. Riseman; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| This report is an interim progress report on the evolving structure of VISIONS, a computer system for general visual perception. The goal of the system is the segmentation and interpretation of a digitized color image of natural outdoor scenes. The report outlines the multi-level data structures used for representing both a visual model of the scene and the semantic data base of stored knowledge about the world. A flexible modular ... |
|
| A Contextual Postprocessing System for Error Detection and Correction in Character Recognition. |
OCT 1972 |
|
| Authors:
Edward M. Riseman; Allen R. Hanson; MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
|
| The paper is an examination of the effectiveness of various forms of contextual information in a postprocessing system for detection and correction of errors in words. Various algorithms using context are considered, from a dictionary algorithm which has available the maximum amount of information, to a set of contextual algorithms using binary n-gram statistics. The latter information differs from the usual n-gram letter statistics in that the probabilities are position-dependent ... |
|
| ADAPTIVE SYSTEMS FOR PREDICTION PROBLEMS, |
SEP 1969 |
|
| Authors:
Allen R. Hanson; CORNELL UNIV ITHACA N Y CENTER FOR APPLIED MATHEMATICS
|
| The paper investigates classes of adaptive systems used as prediction machines in certain simple games. Ideally the machine should approach a state which will maximize its expected gain. The simplest machine takes the form of an urn, similar to the urn models of Polya and Friedman, but modified so as to form an adaptive system. The machine is characterized by learning parameters and a reinforcement scheme. The simplest machine is ... |
|
Reports by Author
