• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.187-192
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• 2023

With development of information technology and necessity for high security, using different identification methods has become very important. Each biometric feature has its own advantages and disadvantages and choosing each of them depends on our usage. Retinal scanning is a bio scale method for identification. The retina is composed of vessels and optical disk. The vessels distribution pattern is one the remarkable retinal identification methods. In this paper, a new approach is presented for identification via retinal images using LBP and hog methods. In the proposed method, it will be tried to separate the retinal vessels accurately via machine vision techniques which will have good sustainability in rotation and size change. HOG-based or LBP-based methods or their combination can be used for separation and also HSV color space can be used too. Having extracted the features, the similarity criteria can be used for identification. The implementation of proposed method and its comparison with one of the newly-presented methods in this area shows better performance of the proposed method.

• Journal of Korea Game Society
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• v.10 no.2
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• pp.49-56
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• 2010

In this paper, we propose a control method of virtual hand by the recognition of a user's hand in the virtual reality game environment. We display virtual hand on the game screen after getting the information of the user's hand movement and the direction thru input images by camera. We can utilize the movement of a user's hand as an input interface for virtual hand to select and move the object. As a hand recognition method based on the vision technology, the proposed method transforms input image from RGB color space to HSV color space, then segments the hand area using double threshold of H, S value and connected component analysis. Next, The center of gravity of the hand area can be calculated by 0 and 1 moment implementation of the segmented area. Since the center of gravity is positioned onto the center of the hand, the further apart pixels from the center of the gravity among the pixels in the segmented image can be recognized as fingertips. Finally, the axis of the hand is obtained as the vector of the center of gravity and the fingertips. In order to increase recognition stability and performance the method using a history buffer and a bounding box is also shown. The experiments on various input images show that our hand recognition method provides high level of accuracy and relatively fast stable results.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.775-792
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• 2015

Person re-identification is an important and challenging task in computer vision with numerous real world applications. Despite significant progress has been made in the past few years, person re-identification remains an unsolved problem. This paper presents a novel appearance-based approach to person re-identification. The approach exploits region covariance matrix and color histograms to capture the statistical properties and chromatic information of each object. Robustness against low resolution, viewpoint changes and pose variations is achieved by a novel signature, that is, the combination of Log Covariance Matrix feature and HSV histogram (LCMH). In order to further improve re-identification performance, third-party image sets are utilized as a common reference to sufficiently represent any image set with the same type. Distinctive and reliable features for a given image set are extracted through decision boundary between the specific set and a third-party image set supervised by max-margin criteria. This method enables the usage of an existing dataset to represent new image data without time-consuming data collection and annotation. Comparisons with state-of-the-art methods carried out on benchmark datasets demonstrate promising performance of our method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.200-203
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• 2016

최근 자율주행자동차에 대한 관심이 증가함에 따라 교통 상황을 인식하는 방법에 대한 연구도 활발하게 진행되고 있다. 특히 교통신호등의 인식은 치명적인 결과를 야기하는 교통사고와 밀접하게 연관된다는 점에서 중요성이 더욱 부각되고 있다. 본 논문에서는 컴퓨터 비전 시스템을 기반으로 한 교통신호등 인식 방법을 제안한다. 차선, 표지판 등과는 다르게 교통신호등은 빛을 발하는 특징이 있으며 그 모양과 형태 또한 규격화 되어 있다. 이러한 특징 중 색상과 형태 특징을 이용하여 두 단계의 추출과정을 거쳐 교통신호등을 인식한다. 먼저 HSV 색 공간에서 적색, 녹색, 주황색의 빛을 발하는 영역을 찾아낸 뒤, 신호의 원형 특징을 이용해 가로, 세로 사이즈와 크기로 신호의 후보를 추출한다. 다음, 신호등의 검은 박스 영역을 찾기 위해 추출한 신호 후보군의 주변부가 검정색인지를 확인한다. 최종적으로 신호등의 박스 부분을 검출하여 신호를 발하는 위치를 기반으로 신호를 인식한다. 실험결과 많은 계산량을 요구하는 기계학습을 사용하지 않고도 실시간 처리와 높은 인식률로 교통 신호를 인식할 수 있음을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.447-450
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• 2013

This paper proposes the way to prevent drowsy driving by recognizing drivers eyes and yawn using a front camera. The method uses the Viola-Jones algorithm to detect eyes area and mouth area from detection face region. In the eyes area, it uses the Hough transform to recognize eye circle in order to distinguish drowsy driving. In the mouth area, it determines whether for the driver to yawn through a sub-window testing by applying a HSV-filter and detecting skin color of the tongue. The test result shows that the recognition rate of yawn reaches up to 90%. It is expected that the method introduced in this paper might contribute to reduce the number of drowsy driving accidents.

• Journal of Korea Game Society
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• v.20 no.3
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• pp.45-54
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• 2020

An automated system using a embedded board is required to generate the notation input of the offline Go game. This paper integrates shape and color information of the objects on the Go game board for light-insensitive processing and reduces the computation step. This paper combined the detection of obstacles using connected components with the computation of canny edge detection and HSV-based detection. As a result, the processing time is reduced in the embedded environment so that reliable notation can be automatically stored even in real-time play environment.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.182-187
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• 2007

본 논문에서는 이미지 상에 나타난 색상 정보를 추출하기 위한 새로운 커널 메소드(Kernel method)인 Grid kernel을 제안한다. 제안한 Grid kernel은 Convolution kernel의 하나로 이미지 상에 나타나는 자질을 주변 픽셀에서 나타나는 자질로 정의 하고 이를 재귀적으로 적용함으로써 두 이미지를 비교한다. 본 논문에서는 제안한 커널을 차량 색상 인식 문제에 적용하여 차량 색상 인식 모델을 제안한다. 이미지 생성시 나타나는 주변 요인으로 인해 차량의 색상을 추출하는 것은 어려운 문제이다. 이미지가 야외에서 촬영되기 때문에 시간, 날씨 등의 주변 요인은 같은 차량이라 하더라도 다른 색상을 보이게 할 수 있다. 이를 해결하기 위해 Grid kernel이 적용된 차량 색상 인식 모델은 이미지를 HSV (Hue-Saturation-Value) 색상 공간으로 사상하여 명도를 배제하였다. 제안한 커널과 색상 인식 모델을 검증하기 위해 5가지 색상을 가진 차량 이미지를 이용하여 실험을 하였으며, 실험 결과 92.4%의 정확율과 92.0%의 재현율을 보였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.71-79
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• 2008

This study proposed a content-based image retrieval system that uses RGB maximum frequency indexing and BW clustering in order to deal with existing retrieval errors using histogram. We split RGB from RGB color images, obtained histogram which was evenly split into 32 bins, calculated and analysed pixels of each area at histogram of R, G, B and obtained the maximum value. We indexed the color information obtained, obtained 100 similar images using the values, operated the final image retrieval system using the total number and distribution rate of clusters. The algorithm proposed in this study used space information using the features obtained from R, G, and B and clusters to obtain effective features, which overcame the disadvantage of existing gray-scale algorithm that perceived different images as same if they have the same frequencies of shade. As a result of measuring the performances using Recall and Precision, this study found that the retrieval rate and priority of the proposed algorithm are more outstanding than those of existing algorithm.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.47-52
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• 2007

In Oriental medicine, the status of tongue is the important indicator to diagnose one's health, because it represents physiological and clinicopathological changes of inner parts of the body. The method of tongue diagnosis is not only convenient but also non-invasive, therefore, tongue diagnosis is one of the most widely used in Oriental medicine. But tongue diagnosis is affected by examination circumstances a lot. It depends on a light source, degrees of an angle, doctor's condition and so on. So it is not easy to make an objective and standardized tongue diagnosis. As part of way to solve this problem, in this study, we tried to design a discriminant function for white and yellow coating with multi-dimensional color vectors. There were 62 subjects involved in this study, among them 48 subjects diagnosed as white-coated tongue and 14 subjects diagnosed as yellow-coated tongue by oriental doctors. And their tongue images were acquired by a well-made Digital Tongue Diagnosis System. From those acquired tongue images, each coating section were extracted by oriental doctors, and then mean values of multi -dimensional color vectors in each coating section were calculated. By statistical analysis, two significant vectors, R in RGB space and H in HSV space, were found that they were able to describe the difference between white coating section and yellow coating section very well. Using these two values, we designed the discriminant function for coating classification and examined how good it works. As a result, the overall accuracy of coating classification was 98.4%. We can expect that the discriminant function for other coatings can be obtained in a similar way. Furthermore, if an automated segmentation algorithm of tongue coating is combined with these discriminant functions, an automated tongue coating diagnosis can be accomplished.