Task Specific Complexity Metrics For Electronic Vision

Carlson, Jeffrey J.; Jordan, Jay B.; Flachs, Gerald M.

doi:10.1117/12.944702

Cited by 4 publications

(2 citation statements)

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“…Based on TIR and TBIR, two new measures: TIR squared (T IR 2 ) and TBIR squared (T BIR 2 ) are developed respectively in [22]. Garlson et al [30] observed that there must be measureable differences between the feature distributions of the target areas and of the background areas when a feature-based target recognizer works well. The author posited that the extent to which the distributions are not different, determines the complexity of the task.…”

Section: Global Metricsmentioning

confidence: 99%

Image Sequence Measures for Automatic Target Tracking

Diao¹,

Mao²,

Zheng³

et al. 2012

PIER

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Abstract-In the field of automatic target recognition and tracking, traditional image metrics focus on single images, ignoring the sequence information of multiple images. We show that measures extracted from image sequences are highly relevant concerning the performances of automatic target tracking algorithms. To compensate the current lack of image sequence characterization systems from the perspective of the target tracking difficulties, this paper proposes three new metrics for measuring image sequences: inter-frame change degree of texture, inter-frame change degree of target size and inter-frame change degree of target location. All are based on the fact that inter-frame change is the main cause interfering with target tracking in an image sequence. As image sequences are an important type of data in the field of automatic target recognition and tracking, it can be concluded that the work in this paper is a necessary supplement for the existing image measurement systems. Experimental results reported show that the proposed metrics are valid and useful.

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Section: Global Metricsmentioning

confidence: 99%

Image Sequence Measures for Automatic Target Tracking

Diao¹,

Mao²,

Zheng³

et al. 2012

PIER

View full text Add to dashboard Cite

show abstract

“…The pattern would then represent the laser beam immediately after leaving the focusing optics and before the atmosphere has altered its wavefront. Comparison of imagery from each location with imagery from the far-field could be performed to ensure that data (7) collected from each imager using the TCC image processors is properly registered. Each imager would then have common LOSs, and the imager FOVs would subtend a common window in the target plane.…”

Section: Applicationsmentioning

confidence: 99%

Characterizing atmospheric effects on target contrast

Watkins

Crow

Kantrowitz³

1991

Opt. Eng.

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Feature space mapping for sensor fusion

et al. 1990

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In the context of a random process scene environment model, a method is presented for fusing data from multiple sensors into a simplified, ordered space for performing electronic vision tasks. The method is based on a new discriminating measure called the tie statistic that is introduced to quantify sensor/feature performance and to provide a mapping from sensor/feature measurement space to a simplified and ordered decision space. The mapping process uses the tie statistic to measure the closeness of an unknown sample probability density function (pdf) to a known pdf for a decision class. Theorems presented in this article relate the tie statistic to minimum probability of error decision making and to the well known Kolmogorov‐Smirnov distance. As examples of the sensor/feature fusion method, the tie mapping process is applied to the object location (cueing) and the texture recognition problems.

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Task Specific Complexity Metrics For Electronic Vision

Cited by 4 publications

References 0 publications

Image Sequence Measures for Automatic Target Tracking

Image Sequence Measures for Automatic Target Tracking

Characterizing atmospheric effects on target contrast

Feature space mapping for sensor fusion

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