• Smart Media Journal
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• v.1 no.4
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.248-253
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• 2008

In this paper, a novel vehicle detection algorithm is proposed that utilizes the color histogram of the image. The color histogram is used to search the image for regions with shadow, block symmetry, and block non-homogeneity, thereby detecting the vehicle region. First, an integral histogram of the input image is computed to decrease the amount of required computation time for the block color histograms. Then, shadow detection is performed and the block symmetry and block non-homogeneity are checked in a cascade manner to detect the vehicle in the image. Finally, the proposed scheme is applied to both still images taken in a parking lot and an on-road video sequence to demonstrate its effectiveness.

• IE interfaces
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• v.8 no.1
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• pp.5-13
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• 1995

Automated vision inspection has become a vital part of computer related industries. Most of the existing inspection systems mainly utilize black and white images. In this paper, we consider an application of automated vision inspection in which cable color has to be recognized in order to detect the quality status of assembled wire harness. A back-propagation neural network is proposed to classify seven different cable colors. To represent a single point in image space, we use the ($L^*,\;a^*,\;b^*$) model which is one of commonly used color-coordinate systems in image processing. After training the neural network with ($L^*,\;a^*,\;b^*$) data obtained from color image, we tested its performance. The results show that the neural network is able to classify cable colors with high performance.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.2
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• pp.55-68
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• 2000

Joint photographic experts group (JPEG) is a standard still-image compression technique, established by the international organization for standardization (ISO) and international telecommunication standardization sector (ITUT). The standard is intended to be utilized in the various kinds of color still imaging systems as a standard color image coding format. Because JPEG is a lossy compression, the decompressed image pixel values are not the same as the value before compression. Various distortions of JPEG compression and JPEG recompression has been reported in various papers. The Image compressed by JPEG is often recompressed by same type compression method in JPEG. In general, JPEG is a lossy compression and the quality of compressed image is predicted that is varied in according to recompression Q-factor. In this paper, four difference color samples(photo image, gradient image, gradient image, vector drawing image, text image) were compressed in according to various Q-factor, and then the compressed images were recompressed according to various Q-factor once again. As the result, this paper evaluate the variation of image quality and file size in JPEG recompression and recommed the optimum recompression factor.

• Proceedings of the Korean Printing Society Conference
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• 2000.04a
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• pp.35-44
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• 2000

Because Image on the CRT change under different illuminants, human is difficult to see original color of object. If what is information of used illuminant on capturing object know, image can be transformed according to viewing condition using the linear matrix method. To know information of used illuminant at an image, the spectral radiance of illuminant can be estimated using the linear model of Maloney and Wandell form an image. And then image can be properly transformed it using color appearance model. In this paper, we predict the spectral radiance of illuminant using spectral power distribution of specular light and using surface spectral reflectance at maximum gray area. and then we perform visual experiments for the corresponding color reproduction according to viewing condition. In results, we ensure that the spectral radiance of illuminant at an image can be well estimated using above algorithms and that human visual system is 70% adapted to the monitor's white point and 30% to ambient light when viewing softcopy images.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.3
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• pp.156-184
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• 2022

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.431-438
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• 1999

The environmental control based on interactive thermoregulatory behavior for swine production has many advantages over the conventional temperature-based control methods. Therefore, this study was conducted to compare various feature selection methods using postural images of growing pigs under various environmental conditions. A color CCD camera was used to capture the behavioral images which were then modified to binary images. The binary images were processed by thresholding, edge detection, and thinning techniques to separate the pigs from their background. Following feature were used for the input patterns to the neural network ; \circled1 perimeter, \circled2 area, \circled3 Fourier coefficients (5$\times$5), \circled4 combination of (\circled1 ＋ \circled2), \circled5 combination of (\circled1 ＋ \circled3), \circled6 combination of (\circled2 ＋ \circled3), and \circled7 combination of (\circled1 ＋ \circled2 ＋ \circled3). Using the above each input pattern, the neural network could classify training images with the success rates of 96%, 96%, 96%, 100%, 100%, 96%, 100%, and testing images with those of 88%, 86%, 93%, 96%, 91%, 90%, 98%, respectively. Thus, the combination of perimeter, area and Fourier coefficients of the thinning images as neural network features gave the best performance (98%) in the behavioral classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.213-231
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• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• Architectural research
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• v.18 no.3
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• pp.83-90
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• 2016

Senescent effects are the gradual deterioration of function caused by biological aging. Senescent effects on color vision are not clearly understood even after considerable researches. Part of the reason is that the color vision is a complex phenomenon resulting from various factors such as organic systems, and the physical (neuro-optical) and the psychological (experiential) processes of color perception. We performed a field experiment on color perceptional differences due to aging vision. Our experiment was applied to two different groups in South Korea: an experimental group (46 subjects of over the age of 61 years) and a control group (49 subjects in their twenties). The experimental tools are comprised of (1) six gradual yellowing detector board (40%, 50%, 60%, 70%, 80%, 90%); (2) pairs of vivid-strong, vivid-deep, grayish-deep, deep-dull, and bright-light tones of Blue (B) and Purple (P) colors; (3) Red (R), Yellow (Y), Green (G), Blue (B), and Purple (P) colors of dull-tones and pale-tones; and (4) a questionnaire on the semantic differential scales of the color images and color differences. A diagnosis system of gradual yellow vision, developed by the authors for this study, was adapted to generate the color detecting boards. The results are as follows. (1) There are significant differences between the two groups in detecting colors that simulate 40% and 50% of yellow vision. (2) As to the color difference detecting ability between similar tones, the experimental group shows difficulties in pairs of vivid-strong tones and deep-dull tones of the B color. And (3), the emotional responses to the dull tone and the pale tone are not stable in the red, the yellow, blue, and purple. Thus, we empirically demonstrate the specific differences in color perception between the old and young groups.