Applying perceptual grouping to content-based image retrieval: building images

Qasim, Iqbal; Aggarwall, J.K.

doi:10.1109/cvpr.1999.786915

Cited by 57 publications

(33 citation statements)

References 22 publications

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“…Thus, this paper adopts human perception principles to recognize relations between objects. The engineering applications of gestalt psychology [15,16] allow for the detection of unique objects based on the strength of the relation. Hence, this paper adopts an engineering application of the gestalt psychology to detect latent interobject relations.…”

Section: Detection Of Neighboring Objects and Interobjectmentioning

confidence: 99%

Visual support system for remote control by adaptive ROI selection of monitoring robot

2018

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A visual assistance system has become attractive as a technique to improve the efficiency and stability of remote control. While an operator controls a working robot, another autonomous monitoring robot evaluates a suitable viewpoint to observe the work site, and dynamically moves to the optimal viewpoint for monitoring. Choosing the observation region (ROI: region of interest) is equivalent to deciding the action of the following autonomous monitoring system. Therefore, we focus on ROI detection in our visual support system. We propose an ROI selection method to identify the most suitable observation point and interobject relations. The monitoring robot detects a gestalt of the scene in order to identify the relations between objects. Such an adaptive ROI in real time improves the efficiency of the remote control. The experimental results indicate the effectiveness of the proposed system in terms of execution time and number of errors.

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Section: Detection Of Neighboring Objects and Interobjectmentioning

confidence: 99%

Visual support system for remote control by adaptive ROI selection of monitoring robot

2018

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“…The advantage of using structure in such queries was demonstrated by analyzing an image database of monocular grayscale outdoor images to retrieve images ARO Center for Imaging Sciences The University of Texas at Austin, J. K. Aggarwal, PIcontaining buildings [8]. A methodology based on the principles of perceptual grouping in a Bayesian framework has been developed [9]. Higher-level and lower-level vision methodologies have been combined for enhanced performance [10].…”

Section: D Reconstruction Of An Urban Scene From Synthetic Fish-eve mentioning

confidence: 99%

A Research Center in the Scientific Foundation of Image Representation and Analysis (CIS)

Miller¹

2001

PsycEXTRA Dataset

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Public Reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comment regarding this burden estimates or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and The views, opinions and/or findings contained in this teport are thcJ&e^pf the authors) and should not be construed as an official Department of the Army position, policy or decision, unless so designated by other documentation. The efforts of CIS during the 1999-2001 years built on the mathematical foundations that have been emerging over the past several decades. Examining a broad class of remote sensing problems, we have been establishing the fundamental framework for the inference and representation of structures in complex systems and scenes of complex shapes proceeding from the representation of complex scenes, to image formation and sensor modeling, and culminating in the development of the fundamental underpinnings for optimal decision/recognition strategies in image understanding and ATR. Within this framework, we are establishing the methodology for establishing the limits of performance of detection, identification and recognition algorithms solving remote sensing problems involving data from multiple active and passive sensors. The Web site continues to receive 5000-7000 visitors a month and has distributed databases to an average of 500 unique visitors each month. CIS members have submitted for publication 6 papers, published 34 papers, gave 32 talks, and have been involved in numerous meetings with the Army and DOD. CIS has a publications index of 253 papers in the areas of target recognition, deformable geometry, image formation and sensor modeling, tomographic imaging, computational vision, inference and optimization, adaptation and learning, multiple sensor fusion, clutter modeling, and performance bounds. SUBJECT TERMS Automatic Target Recognition Summary of the most important resultsThe major results in the Center: 1. Performance bounds and metrics: We have established Performance Bounds and Metrics for pose estimation and identification. We have established for pose the MMSE estimator, and established curves of performances for multiple sensors, FLIR, LADAR, HRR, VIDEO.We have established the tight connection between estimator bounds and ID bounds. Exponential ID bounds are determined by estimator accuracy bounds. Sensor fusion:We have established the optimum methodology for combining information optimally from multiple sensors. Estimation and ID bounds have been calculated for combinations of HRR, LADAR, and FLIR.3. FLIR signature variation: We have established a methodology for accommodating the infinit...

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“…There are three CBIR methods that seem most related to our own: Iqbal and Aggarwals's approach [7] to building recognition using perceptual grouping (rectangles), Zhou, Rui, and Huang's water-filling algorithm [11] for extracting edge-map features, and Vailaya, Jain, and Zhang's edgedirection-histogram (EDH) features [10] for classifying city vs. landscape images. Because the emphasis in [7] and [11] was quite different from our own and since several early reviewers of our paper suggested comparing our features to the EDH features, we implemented EDH and tested it on classifying building vs. nonbuilidng images, using C4.5 and cross validation as above.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Because the emphasis in [7] and [11] was quite different from our own and since several early reviewers of our paper suggested comparing our features to the EDH features, we implemented EDH and tested it on classifying building vs. nonbuilidng images, using C4.5 and cross validation as above. On the same test set, the EDH method had an average error of 16.5% compared to CLC's 5.8%.…”

Section: Experimental Evaluationmentioning

confidence: 99%