scispace - formally typeset
Search or ask a question

Showing papers on "Change detection published in 1988"


Journal ArticleDOI
TL;DR: For instance, this article found that the detection of change when one display of familiar objects replaces another display might be based purely upon visual codes, or also on identity information (i.e., knowing what was present where in the initial display).
Abstract: Detection of change when one display of familiar objects replaces another display might be based purely upon visual codes, or also on identity information (i.e., knowingwhat was presentwhere in the initial display). Displays of 10 alphanumeric characters were presented and, after a brief offset, were presented again in the same position, with or without a change in a single character. Subjects’ accuracy in change detection did not suggest preservation of any more information than is usually available in whole report, except with the briefest of offsets (under 50 msec). Stimulus duration had only modest effects. The interaction of masking with offset duration followed the pattern previously observed with unfamiliar visual stimuli (Phillips, 1974). Accuracy was not reduced by reflection of the characters about a horizontal axis, suggesting that categorical information contributed negligibly. Detection of change appears to depend upon capacity-limited visual memory; (putative) knowledge of what identities are present in different display locations does not seem to contribute.

981 citations


Journal ArticleDOI
TL;DR: A tentative general framework for change detection in signals and systems is presented, based upon a non-exhaustive survey of available methods, which are presented according to the increasing order of complexity of the change problem.

877 citations



Proceedings ArticleDOI
11 Apr 1988
TL;DR: A dynamic reference frame is used which allows the system to adapt to changing backgrounds and produces an unambiguous target silhouette for classification and adaptive normalization is used to maintain constant image contrast.
Abstract: Describes a family of adaptive change detectors for detecting and segmenting moving objects in video scenes. These change detectors employ several techniques to allow them to adjust to changing image conditions. One of these is a dynamic reference frame which allows the system to adapt to changing backgrounds and produces an unambiguous target silhouette for classification. Another is a scheme to estimate system noise and adjust detection thresholds accordingly to satisfy a chosen performance criterion. Finally, adaptive normalization is used to maintain constant image contrast. >

74 citations


Proceedings ArticleDOI
12 Sep 1988

68 citations


Proceedings ArticleDOI
20 Apr 1988
TL;DR: It is shown how autofocus measurements can be used to measure the aircraft track, and provide phase corrections to the raw SAR data, and correctly focussed imagery, free of any azimuth distortions, is produced.
Abstract: Sources of defocusing and azimuth distortion in synthetic-aperture radar (SAR) imagery are outlined. It is shown how autofocus measurements can be used to measure the aircraft track, and provide phase corrections to the raw SAR data. When the phase-corrected data is processed, correctly focussed imagery, free of any azimuth distortions, is produced. For low look-angle systems, range distortion due to terrain height variation is generally negligible, and therefore the system described produces distortion-free SAR imagery. The change detection problem is also considered: given two SAR images of the same area, locate changes that have occurred in the time between the images being obtained. Two approaches to this problem are described. The first applies a simple featuring operation to the two images, followed by hard limiting and image differencing. This is found to detect changed targets well. In the second method, the two images are segmented, and the segmentation descriptions are compared for inconsistencies indicating changes. This approach allows one to identify changes in targets having distinctive characteristics, such as shape or size. >

7 citations


01 Jan 1988
TL;DR: This paper describes several algorithms for the detection of small changes between images in nois environment, and a nonlinear image processin procedure for reduction and/or suppression of bacigrcund (clutter), and the enhancement of the small, difference between two images.
Abstract: This paper describes several algorithms for the detection of small changes between images in nois environment. Specifically two procedures for suc! image processing are introduced. The first is, a nonlinear image processin procedure for reduction and/or suppression of bacigrcund (clutter), and the enhancement of the small, difference between two images. This procedure is based on the newly developed Pontryagin filter and concepts of adaptive noise cancellin and ima e-sequence anal sis. Another rocedure is ayso introiuced but it is Sinear. It is lased on a lattice filter structure. The performacne is evaluated through computer simulations and real images.

3 citations


Proceedings ArticleDOI
11 Apr 1988
TL;DR: A real-time optical device for vehicle detection and recognition on roads operating in real- time with image scanning rates of about 100 Hz, solved the problem of high data rates by reducing the sensor's field of view down to two parallel receptor columns with a well known spacing.
Abstract: We describe a real-time optical device for vehicle detection and recognition on roads. With vehicle traffic on public roads steadily increasing, there is a growing need for efficient monitoring and control of traffic flow. There is need for traffic counting (vehicle detection), estimation of vehicle speed and for vehicle classification (i.e. passenger car, truck). An optical sensor is very well suited to this problem giving detailed information from vehicles seen from a position beside or above the road. We implemented a device for this task operating in real-time with image scanning rates of about 100 Hz. Such rates are necessary for vehicle speeds up to 100 km/h. We solved the problem of high data rates by reducing the sensor's field of view down to two parallel receptor columns with a well known spacing and by sampling the grayvalues from the columns with a rate well fitted to the minimum of the required information. The principal idea of the applied image processing is to use change detection by means of difference pictures, embedded in a dynamic scene driven control. For the reliable detection and segmentation of moving vehicles a simple vehicle model and some rough estimates of the traffic flow are used. The segmented image data of the vehicles together with the derived information are collected for further inspection and processing. The system is realized on the Visual Interpretation System for Technical Applications (VISTA) developed by the Fraunhofer Institute IITB /1/. Most data processing is performed by software. The system achieves well the picture scanning rate of 100 Hz. For experimental purposes all image processing data are continuously displayed on a video monitor.

3 citations


Proceedings ArticleDOI
01 Jan 1988
TL;DR: This paper compares the performances of two image change detection procedures, the first uses an order recursive lattice filter as change detector, while the second one is based on the two dimensional least mean square algorithm.
Abstract: In this paper, we compare the performances of two image change detection procedures. The first uses an order recursive lattice filter as change detector, while the second one is based on the two dimensional least mean square algorithm. Both procedures have the ability to track the nonstationary image signals and suppress noise and clutter in image sequences. The differences that occur between adjacent images of the same scene can easily be spotted, enabling an efficient detection of changes in sequentially scanned environments. The performances of the proposed detection procedures are evaluated by using both computer generated and recorded images.

2 citations


Proceedings ArticleDOI
22 Aug 1988
TL;DR: A technique for detecting objects in noisey range imagery and within a cluttered background is discussed and demonstrated on real data and an approach to object orientation estimation is also discussed.
Abstract: A technique for detecting objects in noisey range imagery and within a cluttered background is discussed and demonstrated on real data. The approach is based on a simple region growing technique which uses the range difference between neighboring pixels in the image as the similarity measure in the growing process. A region list is generated by the process which includes several region properties. Some of these properties are used to validate regions as detections based on a priori knowledge of general object structure. The performance of the techique is tested at several spacial resolutions and with ambiguous range data. Results of varying the similarity measure threshold is also shown. An approach to object orientation estimation is also discussed.

2 citations


Proceedings ArticleDOI
12 Sep 1988

Proceedings ArticleDOI
A. Makki1, A. El-Fishawy, A. Abutaleb, C. Hansen, J. Siegel, S. Kesler 
10 Mar 1988
TL;DR: Two algorithms for the detection of small changes between images in a noisy environment are described, based on the recently developed Pontryagin filter and concepts of adaptive noise cancelling and image-sequence analysis.
Abstract: Two algorithms for the detection of small changes between images in a noisy environment are described. The first is a nonlinear image processing procedure for reduction and/or suppression of background (clutter), and the enhancement of the small difference between two images. This procedure is based on the recently developed Pontryagin filter and concepts of adaptive noise cancelling and image-sequence analysis. The second procedure is linear and is based on a lattice filter structure. The performance is evaluated through computer simulations and real images. >

Patent
11 Jan 1988
TL;DR: In this article, a changing picture element detection part detects a change in picture elements with the aid of a change detection lattice made of 128 X 128 picture elements at every four picture elements in the frame memory.
Abstract: PURPOSE:To facilitate operations for recognizing the details of an image by providing a frame memory and a means synthesizing identification elements with output video signals so that said elements are superimposed in a specific position on a display screen and holding a current image varying when an image sensor part detects a change in the frame memory CONSTITUTION:A changing picture element detection part 2 detects a change in picture elements with the aid of a change detection lattice made of 128 X 128 picture elements at every four picture elements in the frame memory 13 When a reference image update condition judgement circuit 37 judges that a reference image agrees with a reference image update condition RR, a reference image update circuit 14 resamples a background image from the frame memory 13 with the aid of the change detection lattice, and stores it in a reference image memory 15 A reference image comparator 16 compares the difference(absolute value) between picture elements in the change detection lattice in the frame memory 13 and picture elements in the corresponding reference image memory 15 with a changing picture element decision condition RP, decides a case where the magnitude of the change is larger than the RP to be a changing picture element, writes the magnitude of the change and other cases as zero picture elements in a difference image memory 18

Journal Article
TL;DR: This paper reports research conducted on the problem of change detection in digital imagery based on the adaptive learning networks which are an implementation of the N-tuple method of pattern recognition.

Book ChapterDOI
28 Mar 1988
TL;DR: In this paper, the adaptive learning networks are used for change detection in digital images, which are an implementation of the N-tuple method of pattern recognition, and they can adapt to varying data trends.
Abstract: This paper reports research conducted on the problem of change detection in digital imagery. The detection of changes is very important in any applications which require comparison of many images of the same scene. The problem requires an approach which is flexible and can adapt to varying data trends. The system is based on the adaptive learning networks which are an implementation of the N-tuple method of pattern recognition.