Image-based target tracking through rapid sensor orientation change

Tsao, Thomas R.

doi:10.1117/1.1431555

Cited by 15 publications

(11 citation statements)

References 6 publications

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“…Inspired by [18], we extract the feature from intensity map of edges and the feature is given by ) ,..., , ( 2) ET is robust to the registration noise. The maximum value of ET will not change its relative position when the edge changes within a limited range.…”

Section: The Principle Of Edge Tokenmentioning

confidence: 99%

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Structure change detection of images based on biological vision principle

et al. 2005

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Conventional methods to detect changes between temporal images are subject to the effect of illumination variance and registration noise. The method proposed in this paper uses the edge structure information in image to detect changes. A new conception based on biological vision principle, named Edge Token, is introduced to describe the edge structure, which is extracted by using a set of Gabor functions on the intensity map of gradient image. Correlation is used to compare the similarity of two Edge Token vectors. In order to reduce the false alarm, a suppression factor is taken to reduce the effect of weak edges. According to the result of the correlation process, decision rule can be made to locate the outline of changed area. The Edge Token based change detection is robust to illumination variance and registration noise. Experiments on simulated data and remote sensing images are presented.

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Section: The Principle Of Edge Tokenmentioning

confidence: 99%

“…Vector T is called in [18] as Place Token. In fact, it describes the feature of image at the position ) , ( y x .…”

Section: The Principle Of Edge Tokenmentioning

confidence: 99%

Structure change detection of images based on biological vision principle

et al. 2005

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“…Ibarra-Castanedo et al's work [13] discusses the difficulties in analyzing infrared data with a focus toward non-destructive testing. In [14], the problems of detecting point targets in IR images are examined. The research effort summarized herein has shown that it is relatively difficult to detect, extract, or highlight features in IR images.…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filtering Approach to 3-D IR Scene Prediction using Single-Camera Range Video

Celenk

Graham

Venable

et al. 2007

2007 IEEE International Conference on Image Processing

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This paper presents a Kalman filtering approach to predicting 3-D video infrared (IR) scenes as a CMOS multi-coordinate axis sensory-camera mounted on a mobile vehicle moves forward in a controlled environment. Potential applications of this research can be found in indoor/outdoor heat-change based range measurement, synthetic IR scene generation, rescue missions, and autonomous navigation. Experimental results reported herein dictate that linear Kalman filtering based scene prediction accurately estimates future frames in range and intensity sensing. The low least mean square error (LMSE), on the average of 1%, proves the reliability of the approach to IR scene prediction. Currently, the proposed method is devised for piecewise linear motion of the sensory system as it navigates in hallway or corridor.

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“…Examples of work in this category include face identification, 1 automatic target recognition, 2 target tracking, 3 automatic vehicle detection, 4 remote sensing, 5 detection and identification of targets in background clutter, 6,7 and automated terrain analysis. 8 Note that the position-invariant pattern recognition and position estimation achieved in this paper are different from such operations performed on conventional images 9 in that here we work not on direct ''photographic'' images of the targets obtained by some kind of imaging system, but rather on angular intensity scans obtained by rotating a point sensor.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors

Aytaç

Barshan

2004

Opt. Eng

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Abstract. We investigate the use of low-cost infrared (IR) sensors for the simultaneous extraction of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We propose the use of angular intensity scans and present an algorithm to process them to determine the geometry and the surface type of the target and estimate its position. The method is verified experimentally with planes, 90-deg corners, and 90-deg edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1 deg, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99 and 81%, respectively, which shows that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple IR sensors, when coupled with appropriate processing, can be used to extract substantially more information than that for which such devices are commonly employed.

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Image-based target tracking through rapid sensor orientation change

Cited by 15 publications

References 6 publications

Structure change detection of images based on biological vision principle

Structure change detection of images based on biological vision principle

A Kalman Filtering Approach to 3-D IR Scene Prediction using Single-Camera Range Video

Simultaneous extraction of geometry and surface properties of targets using simple infrared sensors

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