• Korean Journal of Poultry Science
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• v.49 no.3
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• pp.157-165
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• 2022

Chickens are a species of vertebrate with varying colors. Various colors of chickens must be classified to find color-related genes. In the past, color scoring was performed based on human visual observation. Therefore, chicken colors have not been measured with precise standards. In order to solve this problem, a computer vision approach was used in this study. Image quantization based on k-means clustering for all pixels of RGB values can objectively distinguish inherited colors that are expressed in various ways. This study was also conducted to determine whether plumage color differences exist in the reciprocal cross lines between two breeds: black Yeonsan Ogye (YO) and White Leghorn (WL). Line B is a crossbred line between YO males and WL females while Line L is a reciprocal crossbred line between WL males and YO females. One male and ten females were selected for each F₁ line, and full-sib mating was conducted to generate 883 F₂ birds. The results indicate that the distribution of light and dark colors of k-means clustering converged to 7:3. Additionally, the color of Line B was lighter than that of Line L (P<0.01). This study suggests that the genes underlying plumage colors can be identified using quantification values from the computer vision approach described in this study.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.117-129
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• 2018

In this paper, we suggest a new system that detects abnormal region of skim. First, an illumination elimination algorithm which uses LAB color model is processed on input facial image to obtain robust facial image for illumination, and then gabor filter is processed to detect the reactivity of discontinuity. And last, the density-based spatial clustering of applications with noise(DBSCAN) algorithm is processed to classify areas of wrinkles, dots, and other skin diseases. This method allows the user to check the skin condition of the images taken in real life.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.5
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• pp.455-463
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• 2008

The shadows, which is generated when acquiring data using optical sensor, mutilates consistency of brightness for same objects in the images. Hence, it makes a trouble to interpret the ground information. This study is focused on detecting the shadowing area in the images. And only single image is used without any other data which is acquired from different source. Also, This study presents the method using HSI color model, especially, using I(intensity) information, and the intensity clustering algorithm. Then, we illuminate the effects of shadow by FFT(Fast Fourier Transform).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.190-207
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• 2015

In the combustion engineering, to decrease pollution and increase production efficiency, and to optimally keep solid burning material amount constant in a burner online, it needs a smart method to detect the amount variation of the burning materials in a high temperature environment. This paper presents an online machine vision system for automatically measuring and detecting the burning material amount inside a burner or a boiler. In the camera-protecting box of the system, a sub-system for cooling is constructed by using the cooling water circulation techqique. In addition, the key and intelligent step in the system is to detect the pile profile of the variable burning material, and the algorithm for the pile profile tracing was studied based on the combination of the gey level (color) discontinuity and similarity based image segmentation methods, the discontinuity based sub-algorithm is made on the quaternion convolution, and the similarity based sub-algorithm is designed according to the region growing with multi-scale clustering. The results of the two sub-algoritms are fused to delineate the final pile profile, and the algorithm has been tested and applied in different industrial burners and boilers. The experiements show that the proposed algorithm works satisfactorily.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.293-300
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• 2018

We propose a moving object extraction and tracking method for improvement of animal identification and tracking technology. First, we propose a method of merging separated moving objects into a moving object by using FCM (Fuzzy C-Means) clustering algorithm to solve the problem of moving object loss caused by moving object extraction process. In addition, we propose a method of extracting data from a moving object and a method of counting moving objects to determine the number of clusters in order to satisfy the conditions for performing FCM clustering algorithm. Then, we propose a method to continuously track merged moving objects. In the proposed method, color histograms are extracted from feature information of each moving object, and the histograms are continuously accumulated so as not to react sensitively to noise or changes, and the average is obtained and stored. Thereafter, when a plurality of moving objects are overlapped and separated, the stored color histogram is compared with each other to correctly recognize each moving object. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.6
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• pp.649-656
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• 2014

In this paper, we address the problem of center lane detection algorithm for autonomous driving. Color information for center lane is gathered by analyzing a row line color distribution of road in front of a vehicle. The candidate pixels for center lane are extracted from the histogram of road colors. Morphological filtering and clustering process are applied to the candidate pixels to extract the exact center lane. We predict a expected area of center lane and search only the regions in subsequent frames, that reduces the time required for center lane detection.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.39-48
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• 2006

This paper is proposed the wavelet morphology algorithm with the spatial auto-object segmentation concept and the clustering concept. When it is segmented the color face by using the proposed algorithm, it is made to the simple image. Also, it is used the spatial quality in order to segment and detect the image as a real time without the user's manufacturing. This removed a small part that is regarded as a noise in image by HSV color model and applied the wavelet morphology to remove a part excepting for the face image. In this paper, it is made a comparison between the wavelet morphology algorithm and the morphology algorithm. And It is showed to accurately detect the face object parts in the image appled to HSV color space model.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.188-194
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• 2008

We propose the new method for the traffic signs recognition that is one of the DAS(Driving assistance system) in the intelligent vehicle. Our approach estimates a varied degree by using a geometric method from the varied traffic signs in noise, rotation and size, and extracts the recognition symbol from the compensated traffic sign for a recognition by using the sequential color-based clustering. This proposed clustering method classify the traffic sign into the attention, regulation, indication, and auxiliary class. Also, The circular tracing method is used for the final traffic sign recognition. To evaluate the effectiveness of the proposed method, varied traffic signs were built. As a result, The proposed method show that the 95 % recognition rate for a single variation, and 93 % recognition rate for a mixed variation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1100-1105
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• 2006

Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.