Representation of Cross-Frequency Spatial Phase Relationships in Human Visual Cortex

Henriksson, Linda; Hyvärinen, Aapo; Vanni, Simo

doi:10.1523/jneurosci.3136-09.2009

Cited by 49 publications

(38 citation statements)

References 55 publications

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“…That is, at points of high phase congruency (PC) we can extract highly informative features. Such a conclusion has been further corroborated by some recent studies in neurobiology using functional magnetic resonance imaging (fMRI) [20]. Therefore, PC is used as the primary feature in computing FSIM.…”

Section: Introductionmentioning

confidence: 55%

“…Based on the physiological and psychophysical evidences, the PC theory provides a simple but biologically plausible model of how mammalian visual systems detect and identify features in an image [16][17][18][19][20]. PC can be considered as a dimensionless measure for the significance of a local structure.…”

Section: A Phase Congruency (Pc)mentioning

confidence: 99%

“…Since human visual cortex is sensitive to phase congruent structures [20], the PC value at a location can reflect how likely it is a perceptibly significant structure point. Intuitively, for a given location x, if anyone of f 1 (x) and f 2 (x) has a significant PC value, it implies that this position x will have a high impact on HVS in evaluating the similarity between f 1 and f 2 .…”

Section: A the Fsim Indexmentioning

confidence: 99%

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FSIM: A Feature Similarity Index for Image Quality Assessment

Zhang

Mou

et al. 2011

IEEE Trans. on Image Process.

4,349

2,099

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Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural-similarity (SSIM) index brings IQA from pixel-based stage to structure-based stage. In this paper, a novel feature-similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

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Section: Introductionmentioning

confidence: 55%

Section: A Phase Congruency (Pc)mentioning

confidence: 99%

Section: A the Fsim Indexmentioning

confidence: 99%

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FSIM: A Feature Similarity Index for Image Quality Assessment

Zhang

Mou

et al. 2011

IEEE Trans. on Image Process.

4,349

2,099

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show abstract

“…The authors [10] discovered that local energy contained in points with consistent phase was concentrated, and local energy is an important property of visual cortex cognition.…”

Section: ) Brightnessmentioning

confidence: 99%

Aptive Image Segmentation based on Saliency Detection

Shui

2015

International Journal on Smart Sensing and Intelligent Systems

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Abstract-in this article, we propose an adaptive image segmentation method based on saliency. First of all, we obtain the saliency map of an image via four bottom-layer feature tunnels, i.e. color, intensity, direction and energy. The energy tunnel helps to describe the outline of objects better in the saliency map. Then, we construct the target detection masks according to the greyness of pixels in the saliency map. Each mask is applied to the original image as the result of pre-segmentation, then corresponding image entropy is calculated. Predict the expected entropy according to maximum entropy criteria and select the optimal segmentation according to the entropies of pre-segmented images and the expected entropy. A large number of experiments have proved the effectiveness and advantages of this algorithm..

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“…For example, edges convey more crucial information than smooth areas. Specifically, a human is sensitive to phase congruent structures, 39 and thus the larger PCðx; yÞ value between f 1 and f 2 implies a higher impact on evaluating the similarity between f 1 and f 2 at location ðx; yÞ. Therefore, we use PC max ðx; yÞ ¼ max½PC 1 ðx; yÞ; PC 2 ðx; yÞ to weigh the importance of S L ðx; yÞ in formulating the overall similarity.…”

Section: Objective Evaluation Indexmentioning

confidence: 99%

Qualitative and quantitative comparisons of multispectral night vision colorization techniques

Zheng

Dong²,

Blasch

2012

Opt. Eng

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Abstract. Multispectral images enable robust night vision (NV) object assessment over day-night conditions. Furthermore, colorized multispectral NV images can enhance human vision by improving observer object classification and reaction times, especially for low light conditions. NV colorization techniques can produce the colorized images that closely resemble natural scenes. Qualitative (subjective) and quantitative (objective) comparisons of NV colorization techniques proposed in the past decade are made and two categories of coloring methods, color fusion and color mapping, are discussed and compared. Color fusion directly combines multispectral NV images into a color-version image by mixing pixel intensities at different color planes, of which a channel-based color fusion method is reviewed. Color-mapping usually maps the color properties of a false-colored NV image (source) onto that of a truecolor daylight target picture (reference). Four coloring-mapping methods-statistical matching, histogram matching, joint histogram matching, and look-up table (LUT)-are presented and compared, including a new color-mapping method called joint-histogram matching (JHM). The experimental NV imagery includes visible (Red-Green-Blue), image-intensified, near infrared, and long-wave infrared images. The qualitative evaluations are conducted by visual inspections of the colorized images, whereas the quantitative evaluations are achieved by a newly proposed metric, objective evaluation index. From the experimental results according to both qualitative and quantitative evaluations, the following conclusions can be drawn: the segmentation-based colorization method produces very impressive and realistic colors but requires intense computations; color fusion and LUT-based methods run very fast but with less realistic results; the statistic-matching method always provides acceptable results; histogram matching and joint-histogram matching can generate impressive and vivid colors when the color distributions between source and target are similar; and the statistic-matching then joint-histogram matching (SM-JHM) method is a reliable and efficient method recommended from both qualitative and quantitative evaluations.

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Representation of Cross-Frequency Spatial Phase Relationships in Human Visual Cortex

Cited by 49 publications

References 55 publications

FSIM: A Feature Similarity Index for Image Quality Assessment

FSIM: A Feature Similarity Index for Image Quality Assessment

Aptive Image Segmentation based on Saliency Detection

Qualitative and quantitative comparisons of multispectral night vision colorization techniques

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