• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.12
• /
• pp.168-175
• /
• 2013

Color consistency in stereoscopic contents is important for 3D display systems. Even with a stereo camera of the same model and with the same hardware settings, complex color discrepancies occur when acquiring high quality stereo images. In this paper, we propose an integrated color matching method that use cumulative histogram in global matching and estimated 3D-distance for the stage of local matching. The distance between the current pixel and the target local region is computed using depth information and the spatial distance in the 2D image plane. The 3D-distance is then used to determine the similarity between the current pixel and the target local region. The overall algorithm is described as follow; First, the cumulative histogram matching is introduced for reducing global color discrepancies. Then, the proposed local color matching is established for reducing local discrepancies. Finally, a weight-based combination of global and local matching is computed. Experimental results show the proposed algorithm has improved global and local error correction performance for stereoscopic contents with respect to other approaches.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.07a
• /
• pp.80-82
• /
• 2010

본 논문에서는 깊이 정보를 이용하여 영상을 색상 분포가 각각 다른 객체 영상으로 분리하고 개별적으로 히스토그램 매칭 기법을 적용하는 조명 보상 기법을 제안한다. 서로 위치가 다른 다시점 카메라의 경우, 다시점 비디오 부호화(multi-view video coding)의 성능을 저하시키는 인접 시점 영상 간 조명 불일치 현상이 발생한다. 이러한 조명 불일치를 보상하기 위한 히스토그램 매칭(histogram matching)을 이용한 전처리 기법이 제안되었다. 모든 시점의 다시점 영상 히스토그램은 정해진 참조 시점 영상의 히스토그램으로 매칭되어 조명 불일치와 다시점 비디오 부호화의 성능을 개선할 수 있다. 하지만 일반적인 영상은 색상 분포와 깊이 정보가 상호 독립적인 객체들로 구성되어 있다. 또한 다시점 비디오는 시점에 따라 획득된 영상 간에 동일 객체의 위치와 깊이가 서로 달라 정해진 참조 시점의 히스토그램으로 매칭하는 기존의 방법은 적합하지 않다. 본 논문에서는 주어진 영상 내에서 깊이 정보를 이용하여 객체를 먼저 분리하고, 객체 영상별로 히스토그램 매칭 기법을 적용하여 색상 보상을 수행하는 새로운 기법을 제안한다. 실험을 통해 제안하는 객체 단위의 조명 보상 기법이 향상된 다시점 비디오 부호화 효율을 보이는 것을 확인하였다.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.1
• /
• pp.87-96
• /
• 2018

In this paper, we propose a method to determine whether there is a defect by using the similarity between ROIs (Region of Interest) of the standard image and ROIs of the image which is corrected in position and rotation after capturing the vehicle headlight. The degree of similarity is determined by the template matching based on the histogram of image, which is a some modification of the method provided by OpenCV where template matching is performed on the raw image not the histogram. The proposed method is compared with the basic method of OpenCV for performance analysis. As a result of the analysis, it was found that the proposed method showed better performance than the OpenCV method, showing the accuracy close to 100%.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.4
• /
• pp.46-51
• /
• 2002

In this paper, I proposed a eyebrow detection algorithm in human face, that is important element in facial recognition. The proposed algorithm consists of four processes: face region detection using color region segmentation. eye detection by template matching, eyebrow candidate region detection in detected eye region, and eyebrow detection by thresholding using the modified histogram that gets luminance value in the candidate region. The test results show that the proposed algorithm can detect eyebrow region very effectively in facial image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.286-289
• /
• 2009

This paper presents an effective approach to minimize recursive computations for balancing stereo pairs by using disparity vector errors and its directional histogram. A stereo balancing function is computed from the correspondent pixels between two images, and a simple approach is to find the matching blocks of two images. However, this procedure requires recursive operation, and its computation cost is very high. Therefore, in this paper, we propose an efficient balance method using structural similarity index and a partial re-searching scheme to reduce the computation cost considerably. For this purpose, we determine if re-searching for each block is necessary or not by using the errors and the directional histogram of disparity vectors. Experiment results show that the performance of the proposed approach can save the computations significantly with ignorable image quality degradation compared with full re-search approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.363-366
• /
• 2016

In this paper, we propose a method for detecting pedestrian, problem-solving situations that occur in a cross. When a pedestrian crossing and other, there occurs a problem of detecting the other pedestrians for detecting a specific pedestrian in the image. The proposed method for solving the problem is as follows. First, select a specific pedestrian detected by bounding box, and extracts the area as a template. Detecting a pedestrian from the image using the HOG, and designated as a candidate region. The final choice of the pedestrian detected by comparison with a candidate pedestrian with the specific pedestrian extracted for template. In comparison, using the Template matching, Histogram comparison and LBP.

• Journal of Korea Multimedia Society
• /
• v.11 no.6
• /
• pp.764-776
• /
• 2008

Image identification is the process of checking whether the query image is the transformed version of the specific original image or not. In this paper, image identification method based on feature composition is proposed. Used features include color distance, texture information and average pixel intensity. We extract color characteristics using color distance and texture information by Modified Generalized Symmetry Transform as well as average intensity of each pixel as features. Individual feature is quantized adaptively to be used as bins of histogram. The histogram is normalized according to data type and it is used as the signature in comparing the query image with database images. In matching part, Manhattan distance is used for measuring distance between two signatures. To evaluate the performance of the proposed method, independent test and accuracy test are achieved. In independent test, 60,433 images are used to evaluate the ability of discrimination between different images. And 4,002 original images and its 29 transformed versions are used in accuracy test, which evaluate the ability that the proposed algorithm can find the original image correctly when some transforms was applied in original image. Experiment results show that the proposed identification method has good performance in accuracy test. And the proposed method is very useful in real environment because of its high accuracy and fast matching capacity.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.11
• /
• pp.73-79
• /
• 2009

In this paper, a method is proposed that enhances the performance of the facial expression recognition using template matching of Local Binary Pattern(LBP) histogram. In this method, the face image is segmented into blocks, and the LBP histogram is constructed to be used as the feature of the block. Block dissimilarity is calculated between a block of input image and the corresponding block of the model image. Image dissimilarity is defined as the weighted sum of the block dissimilarities. In conventional methods, the block weights are assigned by intuition. In this paper a new method is proposed that optimizes the weights from training samples. An experiment shows the recognition rate is enhanced by the proposed method.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.376-379
• /
• 2008

This paper presents an image retrieval algorithm based on feature vector using color of histogram refinement for a faster and more efficient search in the process of content based image retrieval. First, we segment each of R, G, and B images from RGB color image and extract their respective histograms. Secondly, these histograms of individual R, G and B are divided into sixteen of bins each. Finally, we extract the maximum pixel values in each bins' histogram, which are calculated, compared and analyzed, Now, we can perform image retrieval technique using these maximum pixel value. Hence, the proposed algorithm of this paper effectively extracts features by comparing input and database images, making features from R, G and B into a feature vector table, and prove a batter searching performance than the current algorithm that uses histogram matching and ranks, only.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.1
• /
• pp.137-144
• /
• 2014

In this paper, we propose the histogram matching-based power reduction technique considering the perceptual image quality. The conventional methods cannot analyze the clipping error of an image, and hence, they significantly degrade the image quality when pixels with the clipping error are concentrated on small area. The proposed method generates histograms for various images with different characteristics, and it calculates and stores the optimal clipping rate in a database. Then, it compares the histograms with that of an input image, and selects the histogram and clipping rate with the minimum difference to prevent the image quality degradation. In the experimental results, the proposed method improved the average PSNR and SSIM by up to 15.795 dB and 0.036, compared with the conventional methods.