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Kang-Hyun Jo

Bio: Kang-Hyun Jo is an academic researcher from University of Ulsan. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 22, co-authored 384 publications receiving 2515 citations. Previous affiliations of Kang-Hyun Jo include Tongji University & Korea University.


Papers
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BookDOI
TL;DR: In this article, emerging intelligent computing technology and applications, Emerging Intelligent Computing Technology and Applications (EICTA), emerging intelligent Computing technologies and applications (EICTA) are discussed.
Abstract: Emerging Intelligent Computing Technology and Applications , Emerging Intelligent Computing Technology and Applications , دانشگاه تهران

102 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed vehicle license plate detection (VLPD) method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.
Abstract: Detecting the region of a license plate is the key component of the vehicle license plate recognition (VLPR) system. A new method is adopted in this paper to analyze road images which often contain vehicles and extract LP from natural properties by finding vertical and horizontal edges from vehicle region. The proposed vehicle license plate detection (VLPD) method consists of three main stages: (1) a novel adaptive image segmentation technique named as sliding concentric windows (SCWs) used for detecting candidate region; (2) color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively; and (3) finally, decomposing candidate region which contains predetermined LP alphanumeric character by using position histogram to verify and detect vehicle license plate (VLP) region. In the proposed method, input vehicle images are commuted into grey images. Then the candidate regions are found by sliding concentric windows. We detect VLP region which contains predetermined LP color by using HSI color model and LP alphanumeric character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.

85 citations

Journal ArticleDOI
TL;DR: A smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information and was tested on multiple video sequences and demonstrated appropriate processing time for a realistic range of frame sizes.
Abstract: Smoke detection is a key component of disaster and accident detection. Despite the wide variety of smoke detection methods and sensors that have been proposed, none has been able to maintain a high frame rate while improving detection performance. In this paper, a smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information. The method takes advantage of the use of a stationary camera by using a background subtraction method to detect changes in the scene. The color of the smoke is used to assess the probability that pixels in the scene belong to a smoke region. Due to the variable density of the smoke, not all pixels of the actual smoke area appear in the foreground mask. These separate pixels are united by morphological operations and connected-component labeling methods. The existence of a smoke region is confirmed by analyzing the roughness of its boundary. The final step of the algorithm is to check the density of edge pixels within a region. Comparison of objects in the current and previous frames is conducted to distinguish fluid smoke regions from rigid moving objects. Some parts of the algorithm were boosted by means of parallel processing using compute unified device architecture graphics processing unit, thereby enabling fast processing of both low-resolution and high-definition videos. The algorithm was tested on multiple video sequences and demonstrated appropriate processing time for a realistic range of frame sizes.

66 citations

Journal ArticleDOI
TL;DR: A feature description using variant-scale block based Histograms of Oriented Gradients features is introduced and the speed of classification using a cascade boosting approach is doubled comparing to that of the non-cascade one.

65 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: This paper proposes detection and recognition algorithm for restricting, warning and information road signs using RGB color segmentation with two restriction rules which provides good detection results in images with good lighting condition and allows sign detection in dark images.
Abstract: This paper proposes detection and recognition algorithm for restricting, warning and information road signs. Sign detection is based on color analysis. In actual, traffic signs have specific color information like red border for warning and restricting signs or blue background for information signs. However in images obtained by camera mounted in the car color information was changed due to lighting and weather conditions such as dark illumination, rainy and foggy weather etc. To solve that problem we use RGB color segmentation with two restriction rules: first rule is bounding constraints for each color component which provides good detection results in images with good lighting condition; second rule is using normalized color information and allows sign detection in dark images. Structure of information signs is differs from structure of warning and restricting signs hence recognition process is also different. The meaning of traffic sign lies in shape of symbols inside of it. Recognition process is based on shape analysis. For warning and restricted signs recognition process consists of two stages. We extract sign candidate from image and classify sign as a circle or triangle using background shape histograms. Then we convert the inner part of sign into binary mask and apply template matching algorithm. To understand the meaning of information sign we separate it into basic components: arrows and text, and then analyze positional relationship between those segments. Detection of arrowheads is based on morphological operations such and analysis of spatial features like area and direction. Result of recognition is name of sign for warning and restricting signs and set of pairs direction - place for information signs

61 citations


Cited by
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Journal ArticleDOI
TL;DR: A literature review on the second research direction, which aims to capture the real 3D motion of the hand, which is a very challenging problem in the context of HCI.

901 citations

Journal ArticleDOI
01 Nov 2011
TL;DR: As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial imageAnalysis, are also highlighted.
Abstract: Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

895 citations

Journal ArticleDOI
TL;DR: This paper categorizes different ALPR techniques according to the features they used for each stage, and compares them in terms of pros, cons, recognition accuracy, and processing speed.
Abstract: Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

682 citations

Book ChapterDOI
17 Mar 1999
TL;DR: A survey on recent vision-based gesture recognition approaches is given and methods of static hand posture and temporal gesture recognition are reviewed, along with some thoughts about future research directions.
Abstract: The use of gesture as a natural interface serves as a motivating force for research in modeling, analyzing and recognition of gestures. In particular, human computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent vision-based gesture recognition approaches is given in this paper. We shall review methods of static hand posture and temporal gesture recognition. Several application systems of gesture recognition are also described in this paper. We conclude with some thoughts about future research directions.

620 citations

Dissertation
01 Jan 2002

570 citations