• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.1035-1041
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• 2016

Internal cracks in products are invisible and can lead to fatal crashes or damage. Since X-rays can penetrate materials and be attenuated according to the material’s thickness and density, they have rapidly become the accepted technology for non-destructive inspection of internal cracks. This paper presents a robust crack filter based on local gray level variation and multiscale analysis for automatic detection of cracks in X-ray images. The proposed filter takes advantage of the image gray level and its local variations to detect cracks in the X-ray image. To overcome the problems of image noise and the non-uniform intensity of the X-ray image, a new method of estimating the local gray level variation is proposed in this paper. In order to detect various sizes of crack, this paper proposes using different neighboring distances to construct an image pyramid for multiscale analysis. By use of local gray level variation and multiscale analysis, the proposed crack filter is able to detect cracks of various sizes in X-ray images while contending with the problems of noise and non-uniform intensity. Experimental results show that the proposed crack filter outperforms the Gaussian model based crack filter and the LBP model based method in terms of detection accuracy, false detection ratio and processing speed.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.420-438
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• 2004

Artificially or naturally contained texts in the natural images have significant and detailed information about the scenes. If we develop a method that can extract and recognize those texts in real-time, the method can be applied to many important applications. In this paper, we suggest a new method that extracts the text areas in the natural images using the low-level image features of color continuity. gray-level variation and color valiance and that verifies the extracted candidate regions by using the high-level text feature such as stroke. And the two level features are combined hierarchically. The color continuity is used since most of the characters in the same text lesion have the same color, and the gray-level variation is used since the text strokes are distinctive in their gray-values to the background. Also, the color variance is used since the text strokes are distinctive in their gray-values to the background, and this value is more sensitive than the gray-level variations. The text level stroke features are extracted using a multi-resolution wavelet transforms on the local image areas and the feature vectors are input to a SVM(Support Vector Machine) classifier for the verification. We have tested the proposed method using various kinds of the natural images and have confirmed that the extraction rates are very high even in complex background images.

• Agricultural and Biosystems Engineering
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• v.5 no.1
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• pp.5-9
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• 2004

This study was carried out to develop methods of reducing the effect of solar illumination on pixel gray-levels of machine vision for agricultural field use. Two kinds of monochrome CCD cameras with manual and auto-iris lenses were used to take pictures within a range of 15 to 120 klux of solar illumination. A camera having more precise automatic control functions gave much better result. Four kinds of indices using pixel gray-level of the $99\%$ white DRS (diffuse reflectance standard) as a reference were tried to compensate pixel gray-levels of an image for variable illumination. Coefficients of variation of the indices within a range of illumination were used as a criterion for comparison. The study concluded that an index of (A+B)/A, where A is gray-level of the $99\%$ DRS and B is gray-level of the tested material, gave the best consistency in the range of solar illumination.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.8
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• pp.640-646
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• 2003

We presents an edge-based point correlation algorithm for fast and stable visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties in applying automated inspection systems to real factory environment. First of all, NGC algorithms involve highly complex computation and thus require high performance hardware for realtime process. In addition, lighting condition in realistic factory environments is not stable and therefore intensity variation from uncontrolled lights gives many troubles for applying NGC directly as pattern matching algorithm. We propose an algorithm to solve these problems, using thinned and binarized edge data, which are obtained from the original image. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the computational complexity. Matching edges instead of using original gray-level image pixels overcomes problems in NGC method and pyramid of edges also provides fast and stable processing. All proposed methods are proved by the experiments using real images.

• Journal of Korea Foundry Society
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• v.9 no.3
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• pp.221-227
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• 1989

The effect of sulfur content and inoculant on solidification microstructure and mechanical properties of gray cast iron have been investigated. The main results obtained are as follows, 1. In the FeSi3Ca1Ba inoculated irons, with the variation of sulfur content, low sulfur levels (${\sim}0.03%$) yield low chill depth, high tensile strength, good wear resistance and type A graphite with a pearlite matrix. High sulfur levels( >0.08%) provide high chill depth, low mechanical proper ties and type D graphite with small amount ferrite. 2. In case of inoculant variation with normal FeSi, FeSi3Ca1Ba, 30CaSi and uninoculation at 0.03%S level, lower chill depth and higher tensile strength was obtained in the order ; 30CaSi, FeSi3Ca1Ba normal FeSi, uninoculation.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• Journal of the Optical Society of Korea
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• v.10 no.3
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• pp.118-123
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• 2006

We present an optical security method for binary data information by using 4-step phase-shifting digital holography and we analyze tolerance error for the decrypted data. 4-step phase-shifting digital holograms are acquired by moving the PZT mirror with equidistant phase steps of ${\pi}/2$ in the Mach-Zender type interferometer. The digital hologram in this method is a Fourier transform hologram and is quantized with 256 gray level. The decryption performance of the binary data information is analyzed. One of the most important errors is the quantization error in detecting the hologram intensity on CCD. The greater the number of quantization error pixels and the variation of gray level increase, the more the number of error bits increases for decryption. Computer experiments show the results for encryption and decryption with the proposed method and show the graph to analyze the tolerance of the quantization error in the system.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.244-251
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• 2011

We propose a new 2-step phase-shifting digital holographic optical encryption technique and analyze tolerance error for this cipher system. 2-step phase-shifting digital holograms are acquired by moving the PZT mirror with phase step of 0 or ${\pi}$/2 in the reference beam path of the Mach-Zehnder type interferometer. Digital hologram with the encrypted information is Fourier transform hologram and is recorded on CCD camera with 256 gray-level quantized intensities. The decryption performance of binary bit data and image data is analyzed by considering error factors. One of the most important errors is quantization error in detecting the digital hologram intensity on CCD. The more the number of quantization error pixels and the variation of gray-level increase, the more the number of error bits increases for decryption. Computer experiments show the results to be carried out encryption and decryption with the proposed method and the graph to analyze the tolerance of the quantization error in the system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.204-209
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• 2013

The image processing has been applied to various fields along with development of visual media. The boundary parts in which brightness of image dramatically changes are important factors in order to analysis characteristics of image because edge contains important information and significant features. A number of researches for detecting these edges have been conducted and conventional edge detection methods using relationship between adjacent pixels are that operation speed is superior, but the edge detection characteristics are insufficient because they use fixed mask without considering gray-level variation. In this paper, the novel algorithm using grey-level variation of image in mask is proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.50-57
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• 2005

We present a simple and effective method of face and facial feature detection under pose variation of user face in complex background for the human-robot interaction. Our approach is a flexible method that can be performed in both color and gray facial image and is also feasible for detecting facial features in quasi real-time. Based on the characteristics of the intensity of neighborhood area of facial features, new directional template for facial feature is defined. From applying this template to input facial image, novel edge-like blob map (EBM) with multiple intensity strengths is constructed. Regardless of color information of input image, using this map and conditions for facial characteristics, we show that the locations of face and its features - i.e., two eyes and a mouth-can be successfully estimated. Without the information of facial area boundary, final candidate face region is determined by both obtained locations of facial features and weighted correlation values with standard facial templates. Experimental results from many color images and well-known gray level face database images authorize the usefulness of proposed algorithm.