Showing papers on "Object-class detection published in 1987"

Edge Detection for Object Recognition in Aerial Photographs

Abstract: An important objective in computer vision research is the automatic understanding of aerial photographs of urban and suburban locations. Several systems have been developed to begin to recognize man-made objects in these scenes. A brief review of these systems is presented. This paper introduces the Pennsylvania Landscan recognition system. It is performing recognition of a scale model of the University of Pennsylvania campus. The LandScan recognition system uses features such as shape and height to identify objects such as sidewalks and buildings. Also, this work includes extensive study of edge detection for object recognition Two statistics, edge pixel density and average edge extent, are developed to differentiate between object border edges, texture edges and noise edges. The Quantizer Votes edge detection algorithm is developed to find high intensity, high frequency edges. Future research directions concerniiig recognition system development, and edge qualities and statistics are motivated by the results of this research. Acknowledgement: This work was in part supported by: DARPAIONR grant N001485-K-0807, NSF grant DCR-84 1077 1, Air Force grant AFOSR F49620-85-K-00 18, Army/DAAG29-84-K-0061, NSF-CERlDCR82-19196 Ao2, NIH grant NS-10939 -1 1 as part of Cerebro Vascular Research Center, NIH 1-R01-NS-23636-01 ,NSF INT85-14199,NSF DMC8517315, ARPA N0014-85-K-0807, by DEC Corp., IBM Corp. and LORD Corp.

Object Detection Using Their Multispectral Properties

Abstract: Detection of objects is an important task of computer vision systems. In this paper we present the development of an object detection system to be useful while analyzing multispectral images. In this formulation general knowledge about spectral characteristics of the objects to be detected is utilized in the search for their location in an image. Efficiency of the system is derived by using a hierarchical framework with pyramid data structure to store multiresolution, multispectral copies of an image. At every level of processing a fuzzy cluster analysis algorithm is utilized to uncover the membership of individual picture elements. These membership values are used with the general knoweldge of spectral properties of objects to guide the search for their locations. The methodology is tested using several experiments involving multispectral satellite and aerial images. The system is shown to be successful in efficient detection of objects such as rivers, roads, and various types of buildings.

Knowledge-based vision for space station object motion detection, recognition, and tracking

Abstract: Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed.

Security Applications Of Computer Motion Detection

Abstract: An important area of application of computer vision is the detection of human motion in security systems This paper describes the development of a computer vision system which can detect and track human movement across the international border between the United States and Mexico Because of the wide range of environmental conditions, this application represents a stringent test of computer vision algorithms for motion detection and object identification The desired output of this vision system is accurate, real-time locations for individual aliens and accurate statistical data as to the frequency of illegal border crossings Because most detection and tracking routines assume rigid body motion, which is not characteristic of humans, new algorithms capable of reliable operation in our application are required Furthermore, most current detection and tracking algorithms assume a uniform background against which motion is viewed - the urban environment along the US-Mexican border is anything but uniform The system works in three stages: motion detection, object tracking and object identi-fication We have implemented motion detection using simple frame differencing, maximum likelihood estimation, mean and median tests and are evaluating them for accuracy and computational efficiency Due to the complex nature of the urban environment (background and foreground objects consisting of buildings, vegetation, vehicles, wind-blown debris, animals, etc), motion detection alone is not sufficiently accurate Object tracking and identification are handled by an expert system which takes shape, location and trajectory information as input and determines if the moving object is indeed representative of an illegal border crossing

Knowledge Based Sar Images Exploitation

Abstract: One of the basic functions of SAR images exploitation system is the detection of man-made objects. The perfor-mance of object detection is strongly limited by perfor-mance of sementation modules. This paper presents a detection paradigm composed of an adaptive segmentation algorithm based on a priori knowledge of objects followed by a top-down hierarchical detection process that generates and evaluates object hypotheses. Shadow information and inter-object relationships can be added to the knowledge base to improve performance over that of a statistical detector based only on the attributes of individual objects.

Knowledge based object detection using SAR images

Abstract: One of the basic research problems in analyzing synthetic aperture radar (SAR) imagery is the detection of man-made objects. This paper presents a detection paradigm composed of an adaptive segmentation algorithm based on a priori knowledge of objects followed by a top-down hierarchical detection process that generates and evaluates object hypotheses. At the end of this process the most likely hypothesis for the object is selected. The hierarchical structure allows the use of shadow information and inter-object relationships to improve performance over that of a statistical detector based only on the properties of individual objects.