• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.63-66
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• 1996

In this paper, we propose a new content-based color image retrieval algorithm. The algorithm makes use of two features; colors as global features and block classification results as local features. More specifically, we obtain R, G, B color histograms and classify nonoverlapping small image blocks into texture, monotone, and various edges, then using these histograms and classification results were make a similarity measure. Experimental results show that retrieval rate of the proposed algorithm is higher than the previous method.

• Proceedings of the KSR Conference
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• 2002.10a
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• pp.566-573
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• 2002

In this paper, is propose to the object(human) dection method using geometrical structures and projection histograms in the image. The problem of existing methods for objects tracking of background subtracted is resulted from uncertainty at background unfixed. In this paper, two methods are applied to solve problem. This problems are proved by method. This problems is demonstrated by using this methods and applied to the train railroad crossing. Therefore, this paper aims that this contributes to improve the accident of the train railroad crossing.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.805-814
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• 2013

This paper proposes a robust hand segmentation method using view-invariant characteristic of a wrist-mounted camera, and deals with a hand shape recognition system based on segmented hand information. We actively utilize the advantage of the proposed camera device that provides view-invariant images physically, and segment hand region using a Bayesian rule based on adaptive histograms. We construct HSV histograms from RGB histograms, and update HSV histograms using hand region information from a current image. We also propose a user adaptation method by which hand models gradually approach user-dependent models from user-independent models as the user uses the system. The proposed method was evaluated using 16 Korean manual alphabet, and we obtained increases of 27.91% in recognition success rate.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.447-458
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• 2012

We present a method to recognize hand gestures using skeletal joint data obtained from Microsoft's Kinect sensor. We propose a combination feature of multi-angle histograms robust to orientation variations to represent the observation sequence of skeletons. The proposed feature efficiently represents the orientation variations of gestures that can be occurred according to person or environment by combining the multiple angle histograms with various angular-quantization levels. The gesture represented as combination of multi-angle histograms and random decision forest classifier improve the recognition performance. We conduct the experiments in hand gesture dataset obtained from a kinect sensor and show that our method outperforms the other methods by comparing the recognition performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.97-105
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• 2016

We propose a face recognition method using the histograms of multi-resolution segments in order to effectively utilize the local information of faces. Since the variations in faces can occur in various sizes, the DFD method, which uses the histograms from the sub-regions of the same size, is not effective for obtaining local information of faces. In this paper, we first divide an image into several sub-regions and extract the DFD(Discriminant Face Descriptor) from each sub-region. By dividing each sub-region into several segments with multi-resolution and extracting histograms for each segment, we reduce the loss of local information in the process of recognition. The experimental results for the Yale B, AR, CAS-PEAL-R1 databases show that the proposed method improves the recognition performance compared to the existing DFD based method.

• The Mathematical Education
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• v.62 no.2
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• pp.237-255
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• 2023

The objective of this study is to explore the meaning generated through discourse in three different types of 1st-grade middle school textbooks in Korea and CMP textbook in the United States, specifically focusing on histograms. Through a discursive perspective, the study aims to analyze the characteristics of questioning within the stages of statistical problem-solving found in histogram tasks. The findings highlight several significant points. Firstly, variations exist in the definitions of histograms between Korean and US CMP textbooks. Secondly, diverse discursive structures contribute to the interpretation and understanding of histograms in textbooks. Thirdly, limitations are observed in the stages of statistical problem-solving reflected in histogram tasks. Lastly, distinctions are identified in the types of questioning employed in histogram tasks between Korean and US CMP textbooks. Building on these insights, the study suggests concrete ideas for enhancing the process of defining histograms and refining the questioning in histogram tasks.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1030-1035
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• 2014

Machine vision inspection systems have replaced human inspectors in defect inspection fields for several decades. However, the inspection results of machine vision are often affected by small changes of illumination. When small changes of illumination appear in image histograms, the influence of illumination can be decreased by transformation of the histogram. In this paper, we propose an enhanced histogram matching algorithm which corrects distorted histograms by variations of illumination. We use the resolution resizing method for an optimal matching of input and reference histograms and reduction of quantization errors from the digitizing process. The proposed algorithm aims not only for improvement of the accuracy of defect detection, but also robustness against variations of illumination in machine vision inspection. The experimental results show that the proposed method maintains uniform inspection error rates under dramatic illumination changes whereas the conventional inspection method reveals inconsistent inspection results in the same illumination conditions.

• Journal of Veterinary Clinics
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• v.40 no.3
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• pp.225-229
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• 2023

In three dogs showing cerebellar ataxia, the onset of clinical signs varied from a young age of five months to age 13 years. Qualitative magnetic resonance imaging (MRI) revealed various degrees of cerebellar atrophy, and a tentative diagnosis of cerebellar cortical degeneration was made. Quantitative analysis using the brainstem to the cerebellar cross-sectional area ratio (BS:CBM ratio) and T2-signal intensity histograms were obtained to perform an objective evaluation. These techniques have the advantage of being easy and fast to evaluate. These quantitative analyses revealed the severity of cerebellar cortical degeneration in the three dogs as mild, moderate, and severe. Dogs 2 and 3 were identified as abnormal on the relative cerebrospinal fluid (CSF) space using T2-signal intensity histograms but were normal on the BS:CBM ratio. This suggests that the T2-signal intensity histograms may have higher sensitivity than BS:CBM ratio.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.21-28
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• 2008

This paper proposes a novel reduced-reference assessment method for video quality assessment, in which one-dimensional (1-D) histograms of motion vectors (MVs) are used as features of videos. The proposed method is more efficient than the conventional methods in view of computation time, because the proposed quality metric decodes MVs directly from video stream in the parsing process instead of reconstructing the distorted video at the receiver. Moreover, in view of data size, the propose method is efficient because a sender transmits 1-D histograms of MVs accumulated over whole input video sequences. Here, we use 1-D histograms of MVs accumulated over the whole video sequences, which is different from the conventional methods that assessed each image independently. For testing the similarity between histograms, we use histogram intersection and histogram difference methods. We compare the proposed method with the conventional methods for 52 video clips, which are coded under varying bit rate, image size, and frame rate. Experimental results show that the proposed method is more efficient than the conventional methods and that the proposed method is more similar to the mean opinion score (MOS) than conventional algorithms.

• ETRI Journal
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• v.24 no.1
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• pp.23-30
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• 2002

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.