• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.345-352
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• 2014

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert' operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.579-586
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• 2002

The edge descriptor proposed by MPEG-7 standard is a representative approach for the contents-based image retrieval using the edge information. In the edge descriptor, the edge information is the edge histogram derived from a gray-level value image. This paper proposes a new method which extracts color edge information from color images and a new approach for the contents-based image retrieval based on the color edge histogram. The poposed method and technique are applied to image retrieval of the e-catalogue. For the evaluation, the results of image retrieval using the proposed approach are compared with those of image retrieval using the edge descriptor by MPEG-7 and the statistics shows the efficiency of the proposed method. The proposed color edge model is made by combining the R,G,B channel components vectorially and by characterizing the vector norm of the edge map. The color edge histogram using the direction of the color edge model is subsequently used for the contents-based image retrieval.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.1-8
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• 2002

The color interpolation scheme can play an important role in overcoming the physical limitation of the CCD image sensor and in increasing the resolution of color signals, while most conventional approaches result in blurred edges and false color artifacts. In this paper, we have proposed an improved edge-adaptive color interpolation scheme for a progressive scan CCD image sensor with RGB color filter array The edge indicator function proposed utilizes not only the within-channel correlation but also the cross-channel correlation, and reflects the edge characteristics of an image adaptively. The color components unavailable for at each channel are interpolated along the edge direction, not across the edges, so that aliasing artifacts are supressed. Furthermore, we eliminated false color artifacts resulting from the color image formation model in the edge-adaptive color interpolation scheme by adopting the switching algorithm based on the color edge detection. Simulation results of the proposed algorithm indicate that the improved edge-adaptive color interpolation scheme produces quantitatively better and visually more pleasing results than conventional approaches.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.846-849
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• 2004

Compared to the bar code which is being widely used for commercial products management, color code is advantageous in both the outlook and the number of combinations. And the color code has application areas complement to the RFID's. However, due to the severe distortion of the color component values, which is easily over $50{\%}$ of the scale, color codes have difficulty in finding applications in the industry. To improve the accuracy of recognition of color codes, it'd better to statistically process an entire color region and then determine its color than to process some samples selected from the region. For this purpose, we suggest a technique to detect edges between color regions in this paper, which is indispensable for an accurate segmentation of color regions. We first transformed RGB color image to HSI and YIQ color models, and then extracted I- and Y-components from them, respectively. Then we performed Canny edge detection on each component image. Each edge image usually had some edges missing. However, since the resulting edge images were complementary, we could obtain an optimal edge image by combining them.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.12
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• pp.1659-1664
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• 2013

The objectives of this study were to investigate color patterns of shell and mantle edge pigmentation of a Pacific oyster, C. gigas, and to estimate variance components of the two colors. A sample of 240 F0 oysters was collected from six aquaculture farms in Tongyeong, Korea to measure shell color and mantle edge pigmentation. Among the F0s, male and female individuals with black (white) shell and black (white) mantle edge were selected and mated to generate three F1 full-sib black (white) cross families (N = 265). Two and four F2 cross families (N = 286) were also produced from black and white F1 selected individuals, respectively. Variance component estimates due to residuals and families within color were obtained using SAS PROC VARCOMP procedures to estimate heritability of shell and mantle edge pigmentation. In the F0 generation, about 29% (11%) had black (white) color for both shell and mantle edge. However, in the F1 and F2 black (white) cross families, 75% (67%) and 100% (100%) of oysters had black (white) shell colors, and 59% (23%) and 79% (55%) had black (white) mantle edge, respectively. Spearman correlation coefficients between shell and mantle edge color were 0.25, 0.74, and 0.92 in F0, F1, and F2 generations, respectively, indicating that, with generations of selection process, an individual with black (white) shell color is more likely to have black (white) mantle edge pigmentation. This suggests that shell color could be a good indicator trait for mantle edge pigmentation if selection of both the colors is implemented for a couple of generations. Estimates of heritability were 0.41 and 0.77 for shell color and 0.27 and 0.08 for mantle edge pigmentation in the F1 and F2 generations, respectively, indicating that, in general, significant proportions of phenotypic variations for the shell and mantle edge colors are explained by genetic variations between individuals. These results suggest that the two color traits are inheritable and correlated, enabling effective selection on shell and mantle edge color.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.944-945
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• 2017

Color image edge detection is an important operation in many image processing areas. This paper presents a new method for edge detection based on the Bhattacharyya distance that can handle arbitrary boundaries by exploring several edge patterns. Experiments show promising results compared to some existing methods.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.39-42
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• 2003

In this paper, we propose a background segmentation method in low resolution color image. A segmentation algorithm is based on color and edge information. In edge image, adaptive and local thresholds are applied to suppress paint boundaries. Through our experiments, the proposed algorithm efficiently segments background from objects.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.589-601
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• 2014

The performance of edge detection often relies on its ability to correctly determine the dissimilarities of connected pixels. For grayscale images, the dissimilarity of two pixels is estimated by a scalar difference of their intensities and for color images, this is done by using the vector difference (color distance) of the three-color components. The Euclidean distance in the RGB color space typically measures a color distance. However, the RGB space is not suitable for edge detection since its color components do not coincide with the information human perception uses to separate objects from backgrounds. In this paper, we propose a novel method for color edge detection by taking advantage of the HSV color space and the Mahalanobis distance. The HSV space models colors in a manner similar to human perception. The Mahalanobis distance independently considers the hue, saturation, and lightness and gives them different degrees of contribution for the measurement of color distances. Therefore, our method is robust against the change of lightness as compared to previous approaches. Furthermore, we will introduce a noise-resistant technique for determining image gradients. Various experiments on simulated and real-world images show that our approach outperforms several existing methods, especially when the images vary in lightness or are corrupted by noise.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.619-630
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• 2013

In this paper, we propose an edge directed color demosaicing algorithm considering color channel correlation. The proposed method consists of local region classification step and edge directional interpolation step. In the first step, each region of a given Bayer image is classified as normal edge, pattern edge, and flat regions by using intra channel and inter channel gradients. Especially, two criteria and verification process for the normal edge and pattern edge classification are used to reduce edge direction estimation error, respectively. In the second step, edge directional interpolation process is performed according to characteristics of the classified regions. For horizontal and vertical directional interpolations, missing color components are obtained from interpolation equations based on intra channel and inter channel correlations in order to improve the performance of the directional interpolations. The simulation results show that the proposed algorithm outperforms conventional approaches in both objective and subjective terms.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.105-113
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• 2007

In this paper, an image segmentation method which is based on edge salience map and region merging is presented. The edge salience map is calculated by combining a texture edge map with a color edge map. The texture edge map is computed over multiple spatial orientations and frequencies by using Gabor filter. A color edge is computed over the H component of the HSI color model. Then the Watershed transformation technique is applied to the edge salience map to and homogeneous regions where the dissimilarity of color and texture distribution is relatively low. The Watershed transformation tends to over-segment images. To merge the over-segmented regions, first of all, morphological operation is applied to the edge salience map to enhance a contrast of it and also to find mark regions. Then the region characteristics, a Gabor texture vector and a mean color, in the segmented regions is defined and regions that have the similar characteristics, are merged. Experimental results have demonstrated the superiority in segmentation results for various images.