Non-parametric texture defect detection using Weibull features

Timm, Fabian; Barth, Erhardt

doi:10.1117/12.872463

Cited by 52 publications

(24 citation statements)

References 12 publications

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“…where is the scale parameter, is the shape parameter, and is the origin of the contrast distribution. For natural images (as the case in this study) the origin is usually close to zero; however, this parameter is eliminated by stretching the contrast [14].…”

Section: The Extracted Featuresmentioning

confidence: 93%

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Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Abdalmajeed

Jiao

2015

Mathematical Problems in Engineering

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Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.

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Section: The Extracted Featuresmentioning

confidence: 93%

“…Also the spatial structure of uniform textures of many different origins can be completely characterized by Weibull distribution parameters [13]. In addition Weibull distribution was used for defect detection in textures [14]. In [15] Weibull distribution was used to construct the learning mapping.…”

Section: Introductionmentioning

confidence: 99%

Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Abdalmajeed

Jiao

2015

Mathematical Problems in Engineering

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“…To detect the center of the pupil, the Fabian Timm and Erhardt Barth method [6] is used, which allows real-time monitoring of the centers of the pupils on a video stream.…”

Section: Fig 3 Proposed Algorithm For Point Of Interest Detectionmentioning

confidence: 99%

Head-mounted eye tracker based on android smartphone

2019

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This paper presents a wearable eye tracker that tracks points of interests of user at videostream showed at smartphone screen. The system consists head-mounted case for smartphone, point of interest detection algorithm, the software developed for this purposes, and Android smartphone used to show videostream, estimate point of interests at video, and log estimated data into device internal memory.

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“…The statistical approach analyses the spatial distribution values in texture images using various representations, say, auto-correlation function, cooccurrence matrices, histogram statistics (mean, standard deviation, median, etc. ), Weibull distribution (Gururajan et al, 2008;Ghazini et al, 2009;Lin et al, 2007;Latif-Amet et al, 2000;Iivarinent, 2000;Timm et al, 2011), etc.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based Defect Detection System for Grey-level Texture Images

Vaidelienė

Valantinas

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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In this study, a new wavelet-based approach (system) to the detection of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete wavelet transform (DWT) and generates statistically-based parameterized defect detection criteria. The introduced system's parameter provides the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The developed defect detection system was implemented using discrete Haar and Le Gall wavelet transforms. For the experimental part, samples of ceramic tiles, as well as glass samples, taken from real factory environment, were used.

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Non-parametric texture defect detection using Weibull features

Cited by 52 publications

References 12 publications

Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Head-mounted eye tracker based on android smartphone

Wavelet-based Defect Detection System for Grey-level Texture Images

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