Quaternion phase-correlation-based clutter metric for color images

Yang, Chao; Zhang, Jianqi; Xu, Xing; Chang, Honghua; He, Guojing

doi:10.1117/1.2823489

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…[10]. The images [11] used in the experiment are shown in figure 1 and figure 2, in which the first two images are source images, and the last two images are fused images obtained by different methods.…”

Section: Resultsmentioning

confidence: 99%

Target-Acquisition-Performance-Model-Based Quality Metric for Multisensor Image Fusion

Yang

et al. 2010

2010 International Conference on Computational Intelligence and Security

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For applications concerning target detection and tracking, an image fusion quality metric that considers only the information preservation from the source images is not enough. A new quality metric for image fusion that combines the structural information preservation from the source images and the influence of the fusion process on the detection probability is proposed. Thus, the results of the proposed measure are more consistent with subject evaluations.

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Section: Resultsmentioning

confidence: 99%

Target-Acquisition-Performance-Model-Based Quality Metric for Multisensor Image Fusion

Yang

et al. 2010

2010 International Conference on Computational Intelligence and Security

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“…The Search_2 dataset consists of a set of 44 high-resolution digital color images of different complex natural scenes including a search target (a military vehicle), together with the detection times and detection probability obtained from a visual search and detection experiment in which 62 observers participated. The Search_2 dataset, which is described in detail elsewhere [26], has been extensively used in different studies in the literature, ranging from studies evaluating target detectability and clutter metrics, to eye movement studies and attempts to model the human visual system [1,[27][28][29][30][31][32][33][34]. Similar to Chang and Zhang [1,2], we converted the original Search_2 images to grayscale and reduced their resolution with a factor 2, resulting in an image size of 3072 Â 2048 pixels.…”

Section: Methodsmentioning

confidence: 99%

Structural similarity determines search time and detection probability

Toet¹

2010

Infrared Physics & Technology

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“…The Search_2 dataset consists of a set of 44 high-resolution digital color images of different complex natural scenes including a search target (a military vehicle), together with the detection times and detection probability obtained from a visual search and detection experiment in which 62 observers participated. The Search_2 dataset, which is described in detail elsewhere 28 , has been extensively used in different studies in the literature, ranging from studies evaluating target detectability and clutter metrics, to eye movement studies and attempts to model the human visual system 4,[10][11][12][13][14][15][16]37 . Similar to Chang and Zhang 9,10 , we converted the original Search_2 images to grayscale and reduced their resolution with a factor 2, resulting in an image size of 3072x2048 pixels.…”

Section: Methodsmentioning

confidence: 99%

Matched filtering determines human visual search in natural images

Toet¹

2011

SciVee

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The structural image similarity index (SSIM), introduced by Wang and Bovik (IEEE Signal Processing Letters 9-3, pp. 81-84, 2002) measures the similarity between images in terms of luminance, contrast en structure. It has successfully been deployed to model human visual perception of image distortions and modifications in a wide range of different imaging applications. Chang and Zhang (Infrared Physics & Technology 51-2, pp. 83-90, 2007) recently introduced the target structural similarity (TSSIM) clutter metric, which deploys the SSIM to quantify the similarity of a target to its background in terms of luminance, contrast en structure. They showed that the TSSIM correlates significantly with mean search time and detection probability. However, it is not immediately obvious to what extent each of the three TSSIM components contributes to this correlation. Here we evaluate the TSSIM by deploying it to a set of natural images for which human visual search data are available: the Search_2 dataset. By analyzing the predictive performance of each of the three TSSIM components, we find that it is predominantly the structural similarity component which determines human visual search performance, whereas the luminance and contrast components of the TSSIM show no relation with human performance. Since the structural similarity component of the TSSIM is equivalent to a matched filter, it appears that matched filtering predicts human visual performance when searching for a known target.

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Quaternion phase-correlation-based clutter metric for color images

Cited by 20 publications

References 15 publications

Target-Acquisition-Performance-Model-Based Quality Metric for Multisensor Image Fusion

Target-Acquisition-Performance-Model-Based Quality Metric for Multisensor Image Fusion

Structural similarity determines search time and detection probability

Matched filtering determines human visual search in natural images

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