An approach to the segmentation of textured dynamic scenes

Jayaramamurthy, S.N.; Jain, Ramesh

doi:10.1016/s0734-189x(83)80039-5

Cited by 18 publications

(3 citation statements)

References 12 publications

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“…Various approaches have been proposed to deal with different phases of analysis. These include the segmentation of moving human body from the background and extraction of low-level features A hybrid architecture design ( Jayaramamuthy and Jain, 1983;Thompson and Barnard, 1981), body part location which includes moving parts tracking and labeling of the extracted regions (Akita, 1984;Leung and Yang, 1995), and motion pattern analysis (Ferrigno et al, 1990;Rashid, 1980).…”

Section: Related Work 21 Current Security Monitoring Systemsmentioning

confidence: 99%

A hybrid architecture design for security monitoring via the Internet

Fong

Hui²

2003

Kybernetes

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We present the development of a Web-based remote-monitoring system as an attractive alternative to the manual task of security surveillance. The system has built-in human motion analysis that can alert the security personnel when suspicious activities are detected at the monitored sites. In this paper, our focus is on the architectural design of such a system. We discuss the relative strengths and weaknesses of three different architectural approaches: centralized, distributed and hybrid, and conclude that a hybrid approach is most appropriate. Our Web-based system has been implemented for monitoring elevators (lifts) within a university environment. Objective and subjective tests have validated the effectiveness of our system. The Web-based implementation enhances ease of operation and allows users to connect to the system via a Web browser.

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Section: Related Work 21 Current Security Monitoring Systemsmentioning

confidence: 99%

A hybrid architecture design for security monitoring via the Internet

Fong

Hui²

2003

Kybernetes

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“…Also, the occlusion problem cannot be handled. Jayaramamurthy and Jain [5] developed an algorithm to extract the masks of moving textured objects against a textured background. Their approach consists of three stages: extraction of the active regions, estimation of the motion parameters of the active regions, and estimation of the object masks.…”

Section: Frame-by-frame Analysismentioning

confidence: 99%

Perceptual Organization Based Computational Model for Robust Segmentation of Moving Objects

Sarkar

Majchrzak

Korimilli

2002

Computer Vision and Image Understanding

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The role of perceptual organization in motion analysis has heretofore been minimal. In this work we present a simple but powerful computational model and associated algorithms based on the use of perceptual organizational principles, such as temporal coherence (or common fate) and spatial proximity, for motion segmentation. The computational model does not use the traditional frame by frame motion analysis; rather it treats an image sequence as a single 3D spatio-temporal volume. It endeavors to find organizations in this volume of data over three levels-signal, primitive, and structural. The signal level is concerned with detecting individual image pixels that are probably part of a moving object. The primitive level groups these individual pixels into planar patches, which we call the temporal envelopes. Compositions of these temporal envelopes describe the spatio-temporal surfaces that result from object motion. At the structural level, we detect these compositions of temporal envelopes by utilizing the structure and organization among them. The algorithms employed to realize the computational model include 3D edge detection, Hough transformation, and graph based methods to group the temporal envelopes based on Gestalt principles. The significance of the Gestalt relationships between any two temporal envelopes is expressed in probabilistic terms. One of the attractive features of the adopted algorithm is that it does not require the detection of special 2D features or the tracking of these features across frames. We demonstrate that even with simple grouping strategies, we can easily handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of training and specific object or illumination models. We present results on a large variety of motion sequences to demonstrate this robustness. c 2002 Elsevier Science (USA)

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“…A moving object and the background in a scene is usually segmented by the differencing technique that subtracts the gray-level values of coincidence pixels in present image frame and a targetfree background reference frame. This approach has been adopted by Jain and Nagel [13], Yachida et al [31], Jayaramamurthy and Jain [14] and Mecocci [21] for detecting moving targets.…”

Section: Previous Researchmentioning

confidence: 99%

A three-dimensional machine-vision approach for automatic robot programming

Tsai

1995

J Intell Robot Syst

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Abstract. This research investigates a novel robot-programming approach that applies machine-vision techniques to generate a robot program automatically. The hand motions of a demonstrator are initially recorded as a long sequence of images using two CCD cameras. Machine-vision techniques are then used to recognize the hand motions in three-dimensional space including open, closed, grasp, release and move. The individual hand feature and its corresponding hand position in each sample image is translated to robot's manipulator-level instructions. Finally a robot plays back the task using the automatically generated program.A robot can imitate the hand motions demonstrated by a human master using the proposed machine-vision approach. Compared with the traditional leadthrough and structural programming-language methods, the robot's user will not have to physically move the robot arm through the desired motion sequence and learn complicated robot-programming languages. The approach is currently focused on the classification of hand features and motions of a human arm and, therefore, is restricted to simple pick-and-place applications. Only one arm of the human master can be presented in the image scene, and the master must not wear long-sleeved clothes during demonstration to prevent false identification. Analysis and classification of hand motions in a long sequence of images are time-consuming. The automatic robot programming currently developed is performed off-line.

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An approach to the segmentation of textured dynamic scenes

Cited by 18 publications

References 12 publications

A hybrid architecture design for security monitoring via the Internet

A hybrid architecture design for security monitoring via the Internet

Perceptual Organization Based Computational Model for Robust Segmentation of Moving Objects

A three-dimensional machine-vision approach for automatic robot programming

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