• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1653-1662
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• 2012

Recently, Organic Light Emitting Diode (OLED) that is broadly applied as next generation display has various advantages. However, OLED display causes unbalanced color tone due to the difference of luminance efficiency among luminous elements. In this paper, we propose adaptive color shifter (ACS) to resolve the RGB channel unbalance and to have wide color range of a relatively weak channel using the image processing method. proposed ACS system was simulated using a variety of image. Also, we numerically analyzed using hue histogram, CIE-1931 xyz color space.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.47-52
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• 2006

This paper proposes a method of content-based image retrieval using region feature vector in order to overcome disadvantages of existing color histogram methods. The color histogram methods have a weak point that reduces accuracy because of quantization error, and more. In order to solve this, we convert color information to HSV space and quantize hue factor being purecolor information and calculate histogram and then use thus for retrieval feature that is robust in brightness, movement, and rotation. Also we solve an insufficient part that is the most serious problem in color histogram methods by dividing an image into sixteen regions and then comparing each region. We improve accuracy by edge and DC of DCT transformation. As a result of experimenting with 1,000 color images, the proposed method has showed better precision than the existing methods.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.39-47
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• 2019

In this paper, we proposed extracting modified Histogram of Oriented Gradients (HOG) features using background removal when tracking pedestrians in real time. HOG feature extraction has a problem of slow processing speed due to large computation amount. Background removal has been studied to improve computation reductions and tracking rate. Area removal was carried out using S and V channels in HSV color space to reduce feature extraction in unnecessary areas. The average S and V channels of the video were removed and the input video was totally dark, so that the object tracking may fail. Histogram equalization was performed to prevent this case. HOG features extracted from the removed region are reduced, and processing speed and tracking rates were improved by extracting clear HOG features. In this experiment, we experimented with videos with a large number of pedestrians or one pedestrian, complicated videos with backgrounds, and videos with severe tremors. Compared with the existing HOG-SVM method, the proposed method improved the processing speed by 41.84% and the error rate was reduced by 52.29%.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.111-120
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• 2016

Wire recognition is one of the important tasks in chip reverse engineering since connectivity comes from wires. Recognized wires are used to recover logical or functional representation of the corresponding circuit. Though manual recognition provides accurate results, it becomes impossible, as the number of wires is more than hundreds of thousands. Wires on a chip usually have specific intensity or color characteristics since they are made of specific materials. This paper proposes two stage wire recognition scheme; image binarization and then the process of determining whether regions in binary image are wires or not. We employ existing techniques for two processes. Since the second process requires the characteristics of wires, the users needs to select the typical wire region in the given image. The histogram characteristic of the selected region is used in calculating histogram similarity between the typical wire region and the other regions. The first experiment is to select the most appropriate binarization scheme for the second process. The second experiment on the second process compares three proposed methods employing histogram similarity of grayscale or HSV color since there have not been proposed any wire recognition method comparable by experiment. The best method shows more than 98% of true positive rate for 25 test examples.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.537-544
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• 2006

Image Search is one of the most exciting and fast growing research areas in the filed of multimedia technology. This paper conducts an empirical evaluation of color descriptor that uses the information of color distribution in color images, which is the most basic element for image search. With the experimental results, we observe that in the top 10% of precision, HSV, Daubechies 9/7 and 2 level decomposition have little better than others. Also histogram quadratic metrics outperform the Minkowski form distance metrics in similarity measurements, but spend more than 20 in computational times.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.62-69
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• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.741-747
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• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.930-938
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• 2005

We propose a content-based image retrieval(CBIR) method based on an efncient combination of a color feature and multiresolution texture features. As a color feature, a HSV autocorrelograrn is chosen which is blown to measure spatial correlation of colors well. As texture features, BDIP and BVLC moments are chosen which is hewn to measure local intensity variations well and measure local texture smoothness well, respectively. The texture features are obtained in a wavelet pyramid of the luminance component of a color image. The extracted features are combined for efficient similarity computation by the normalization depending on their dimensions and standard deviation vectors. Experimental results show that the proposed method yielded average $8\%\;and\;11\%$ better performance in precision vs. recall than the method using BDIPBVLC moments and the method using color autocorrelograrn, respectively and yielded at least $10\%$ better performance than the methods using wavelet moments, CSD, color histogram. Specially, the proposed method shows an excellent performance over the other methods in image DBs contained images of various resolutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.211-227
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• 2011

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.