| Multimodal Human Identification for Computer Security |
15 MAR 2005 |
84 pages |
| Authors:
Sohail Nadimi; Edward Hong; Bir Bhanu; CALIFORNIA UNIV RIVERSIDE BOURNS COLL OF ENGINEERING
|
 | (a) A cooperative coevolutionary approach for object detection is developed. It fuses the scene contextual information with the available statistical and prediction information available from color and infrared sensors. The sensor fusion system maintains high detection rates under a variety of environmental conditions. The results are shown for a full 24 hour diurnal cycle. (b) An agent-based intrusion detection system, where evolutionary computational techniques, similar to those discussed in (a) ... |
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| Automatic Design and Synthesis of Automatic Target Recognition (ATR) systems Using Learning Paradigms |
OCT 2003 |
148 pages |
| Authors:
Bir Bhanu; Yingqiang Lin; Krzysztof Krawiec; CALIFORNIA UNIV RIVERSIDE BOURNS COLL OF ENGINEERING
|
| This report investigates evolutionary computational techniques such as genetic programming (GP), coevolutionary genetic programming (CGP), linear genetic programming (LGP) and genetic algorithms (GA) to automate the synthesis and analysis of object detection and recognition systems. It shows the efficacy of evolutionary computation in synthesizing effective composite operators and composite features from domain-independent primitive image processing operations and primitive features for object detection and recognition. Smart crossover, smart mutation and a ... |
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| Learning Integrated Recognition for Image Exploitation |
30 SEP 2003 |
11 pages |
| Authors:
Bir Bhanu; CALIFORNIA UNIV RIVERSIDE CENTER FOR RESEARCH IN INTELLIGENT SYSTEMS
|
| The overall goals of the proposed learning integrated object recognition for image exploitation research effort at the Center for Research in Intelligent Systems of the University of California, Riverside are to improve the performance and reliability of automated systems that can recognize objects in reconnaissance imagery acquired under dynamically changing conditions and for systems that can efficiently extract information from enormous image databases. This requires innovative techniques developed through fundamental ... |
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| Learning Integrated Visual Database for Image Exploitation |
25 NOV 2002 |
22 pages |
| Authors:
Bir Bhanu; CALIFORNIA UNIV RIVERSIDE CENTER FOR RESEARCH IN INTELLIGENT SYSTEMS
|
| The research summarized in this report is aimed at developing image understanding (IU) algorithms and systems that have performance prediction and learning capabilities and that can improve their performance with experience, in terms of quality of results, processing speed and matching with the user's perception. The following scientific problems are addressed: (a) Fundamental theory for predicting the performance of object recognition systems and its validation on SAR images, (b) Automatic ... |
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| Multistategy Learning for Computer Vision |
28 SEP 1998 |
11 pages |
| Authors:
Bir Bhanu; CALIFORNIA UNIV RIVERSIDE COLL OF ENGINEERING
|
| Current IU algorithms and systems lack the robustness to successfully process imagery acquired under real-world scenario. They do not provide the necessary consistency, reliability and predictability of results. Robust 3-D object recognition, in practical applications, remains one of the important but elusive goals of IU research. With the goal of achieving robustness, our research at UCR is directed towards learning parameters, feedback, contexts, features, concepts, and strategies of IU algorithms ... |
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| Multistrategy Learning for Computer Vision |
31 MAR 97 |
172 pages |
| Authors:
Bir Bhanu; CALIFORNIA UNIV RIVERSIDE COLL OF ENGINEERING
|
| Current IU algorithms and systems lack the robustness to successfully process imagery acquired under real-world scenario. They do not provide the necessary consistency, reliability and predictability of results. Robust 3-D object recognition, in practical applications, remains one of the important but elusive goals of IU research. With the goal of achieving robustness, our research at UCR is directed towards learning parameters, feedback, contexts, features, concepts, and strategies of IU algorithms ... |
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| Mobile Testbed for Experiments in Machine Perception and Learning |
15 AUG 96 |
22 pages |
| Authors:
Bir Bhanu; CALIFORNIA UNIV RIVERSIDE DEPT OF GEOGRAPHY
|
| One of the major goals of our research in image understanding is to test and evaluate algorithms under real world situations. To accomplish this goal, we are developing a mobile platform equipped with sensors and on-board computers. use an off the shelf electric cart that has been modified and equipped with a pan-tilt camera unit and other hardware for steering and speed control. We have also developed dedicated mechanical and ... |
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| Multistrategy Learning for Image Understanding |
15 FEB 95 |
205 pages |
| Authors:
Bir Bhanu; CALIFORNIA UNIV RIVERSIDE
|
| Current Image Understanding (IU) algorithms and systems lack the flexibility and robustness to successfully handle complex real-world situations. Robust 3-D object recognition, in real-world applications operating under changing environmental conditions, remains one of the important but elusive goals of IU research. We believe that an innovative combination of IU and Machine Learning (ML) techniques will lead to the advancement of the IU filed in general. IU itself has come to ... |
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| THREE-POINT SEED METHOD FOR THE EXTRACTION OF PLANAR FACES FROM RANGE DATA |
MAY 82 |
|
| Authors:
Thomas C. Henderson; Bir Bhanu
|
 | a method is given for representing a three-dimensional (3-d) object as a set of planar faces. the points representing the complete surface of the 3- d object are obtained by combining the object points from a sequence of range data images corresponding to various views of the object. the planar faces are then determined by sequentially choosing three very close non-collinear points (the 3-point seed) and investigating the set of ... |
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| Shape Matching and Image Segmentation Using Stochastic Labeling |
AUG 1981 |
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| Authors:
Bir Bhanu; UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES IMAGE PROCESSING INST
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| New results are presented in the areas of shape matching of nonoccluded and occluded objects in two dimensions, surface approximation by polygons, shape matching of objects in three dimensions, and segmentation of images having unimodal distributions. The same stochastic labeling technique is used in both shape matching and segmentation with various extensions. Shape matching is viewed as a segment matching problem. Unlike the previous work in shape matching of 2-D ... |
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