HVS-based structural similarity for image quality assessment

Wang, Bo; Wang, Zhibing; Liao, Yupeng; Lin, Xiao

doi:10.1109/icosp.2008.4697344

Cited by 40 publications

(24 citation statements)

References 4 publications

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“…Dual-threshold algorithm applies 2 thresholds T � and T � to non-maximum suppression image, and T � � 2T � , so 2 threshold edge image N � �i, j� and N � �i, j� are obtained. Because N � �i, j� is obtained using higher threshold, it contains very few false edge, but contains discontinuity (non-closure) [5]. Dual-threshold algorithm will connect edges to contour in N � �i, j�, when arriving at end points of the contour, the algorithm will locate the edge able to connect the contour at the location of 8 neighbouring points of N � �i, j�, as such, the algorithm continuously collects edge in N � �i, j� till connecting with N � �i, j� .…”

Section: Canny Principle and Realization Procedures Of Canny Algorithmmentioning

confidence: 99%

The Application Study on the Improved Canny Algorithm for Edge Detection in Strain Gauge Image

Qin

Juan

2017

MATEC Web Conf.

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Abstract. In order to realize automatic cutting of strain gauge diaphragm, it is needed to detect and identify strain gauge edge. Though classic canny algorithm can detect the edge of strain gauge, it has such problems as low accuracy, being easily influenced by light, random noise. This paper proposes an improved canny algorithm, replacing Gauss filtering of classic canny algorithm with median filtering, improving gradient magnitude calculation method, and selecting threshold with iteration. Compared to classic canny algorithm the method can improve the ability to resist random noise while detecting strain gauge edge more accurately. Application of the method to strain gauge edge detection will lay foundation for realization of strain gauge automatic cutting.

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Section: Canny Principle and Realization Procedures Of Canny Algorithmmentioning

confidence: 99%

The Application Study on the Improved Canny Algorithm for Edge Detection in Strain Gauge Image

Qin

Juan

2017

MATEC Web Conf.

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“…The purpose of the system is to provide a similarity measure between them [20]. To measure similarity between them the task is separated into three comparisons: luminance, contrast and structure.…”

Section: Structure Similarity Index Metrics (Ssim)mentioning

confidence: 99%

“…First, the luminance of each signal is compared. The mean intensity of discrete signal is as follow [20]:…”

Section: Structure Similarity Index Metrics (Ssim)mentioning

confidence: 99%

“…So, this SSIM values can be used to measure the similarity between the structure of test image and stored database training images for recognizing the images. Brief overview of this similarity measurement system is as follows [20]: Figure: 3 Diagram of the proposed similarity measurement system [21].…”

Section: Structure Similarity Index Metrics (Ssim)mentioning

confidence: 99%

“…These dependencies carry important information about the structure of the objects in the visual scene [20]. SSIM is full-reference image quality assessment methods; the quality of a test image is evaluated by comparing it with a reference image that is assumed to have perfect quality [21].…”

Section: Structure Similarity Index Metrics (Ssim)mentioning

confidence: 99%

See 2 more Smart Citations

Image Recognition using Coefficient of Correlation and Structural SIMilarity Index in Uncontrolled Environment

Kaur¹,

Kaur²,

Gupta³

2012

IJCA

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The goal of this paper is to analyse and improve the performance of metrics like Coefficient of Correlation (CoC) and Structural Similarity Index (SSIM) for image recognition in real-time environment. The main novelties of the methods are; it can work under uncontrolled environment and no need to store multiple copies of the same image at different orientations. The values of CoC and SSIM get changed if images are rotated or flipped or captured under bad/highly illuminated conditions. To increase the recognition accuracy, the input test image is pre-processed. First, discrete wavelet transform is applied to recognize the image captured under bad illuminated and dull lightning conditions. Second, to make the method rotation invariant, the test image is compared against the stored database image without and with rotations in the horizontal, vertical, diagonal, reverse diagonal and flipped directions. The image recognition performance is evaluated using the Recognition Rate and Rejection Rate. The results indicate that recognition performance of Correlation Coefficient and SSIM gets improved with rotations and discrete wavelet transform. Also it was observed that CoC with proposed modifications yield better results as compared to state of the art enhanced Principal Component Analysis and Enhanced Subspace Linear Discriminant Analysis.

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HVS-Inspired Dimensionality Reduction Model Based on Factor Analysis

Shang

Dong

2016

Bio-Inspired Computing – Theories and Applications

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HVS-based structural similarity for image quality assessment

Cited by 40 publications

References 4 publications

The Application Study on the Improved Canny Algorithm for Edge Detection in Strain Gauge Image

The Application Study on the Improved Canny Algorithm for Edge Detection in Strain Gauge Image

Image Recognition using Coefficient of Correlation and Structural SIMilarity Index in Uncontrolled Environment

HVS-Inspired Dimensionality Reduction Model Based on Factor Analysis

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