• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1117-1124
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• 2018

Contrast enhancement plays an important role in various computer vision systems, since their usability can be improved with visibility enhancement of the images affected by weather and lighting conditions. This paper introduces a histogram modification algorithm that reflects the properties of original images in order to eliminate the saturation effect and washed-out of image details due to the over-enhancement. Our method modifies the original histogram so that an additive term fill histogram pits and the gamma correction suppresses histogram spikes. The parameters for the additive term and gamma correction are adjusted automatically according to statistical properties of the images. Experimental results for various low contrast and hazy images demonstrate that the proposed contrast enhancement improves visibility and reduces haze components effectively, while preserving the characteristics of original images, than the conventional methods.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.205-208
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• 2013

Generally, binary pattern transforms have been used in the field of the face recognition and facial expression, since they are robust to illumination. Thus, this paper proposes an illumination-robust face recognition system combining an MLDP, which improves the texture component of the LDP, and a 2D-PCA algorithm. Unlike that binary pattern transforms such as LBP and LDP were used to extract histogram features, the proposed method directly uses the MLDP image for feature extraction by 2D-PCA. The performance evaluation of proposed method was carried out using various algorithms such as PCA, 2D-PCA and Gabor wavelets-based LBP on Yale B and CMU-PIE databases which were constructed under varying lighting condition. From the experimental results, we confirmed that the proposed method showed the best recognition accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.331-334
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• 2010

This paper proposes a new design of a video-base fire detection system using texture and color spatial distribution information. The video sequences used are taken in different days with different lighting conditions having different backgrounds. The time complexity of most previous vision-based fire detection techniques are very high due to lengthy programing. To overcome the problems of lengthy codes and time complexity, in this algorithm, at first we normalize the video image frames by size and color information. Then the spatial distribution of the color information is used to extract the candidate regions, later using visual texture of the fire, we detect the fire regions. The experimental results show an real-time fire detection over thousands of image frames, and have higher detection rate when compared to the conventional fire detection techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.936-942
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• 2019

Autonomous driving of drone indoor must move along a narrow path and overcome other factors such as lighting, topographic characteristics, obstacles. In addition, it is difficult to operate the drone in the hallway because of insufficient texture and the lack of its diversity comparing with the complicated environment. In this paper, we study an autonomous drone navigation using Convolution Neural Network(CNN) in indoor environment. The proposed method receives an image from the front camera of the drone and then steers the drone by predicting the next path based on the image. As a result of a total of 38 autonomous drone navigation tests, it was confirmed that a drone was successfully navigating in the indoor environment by the proposed method without hitting the walls or doors in the hallway.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.87-93
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• 2023

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.128-135
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• 2009

Techniques for tracking the same region of physical space with the temporal sequences of images by matching the contours of constant phase show robust and stable performance in relative to the tracking techniques using or assuming the constant intensity. Using this property, we describe an algorithm for obtaining the robust motion parameters caused by the global camera motion. First, we obtain the optical flow based on the phase of spacially filtered sequential images on the region in a direction orthogonal to orientation of each component of gabor filter bank. And then, we apply the least squares method to the optical flow to determine the affine motion parameters. We demonstrate hat proposed method can be applied to the vision based pointing device which estimate its motion using the image including the display device which cause lighting condition varieties and noise.

• Cartoon and Animation Studies
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• s.29
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• pp.103-128
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• 2012

Study about existence of illusion which human beings feel from imitated image based reality have been continuing by copy theory and conventionalism for a long time. Traditional copy theory which had controlled representation theory from plato have explained illusion by similarity of image and representation objects. According to copy theory, image is natural sign unlike language but the late in the 20th century, conventionalism from N, Goodman insists they are not any special similarity between image and representation objects. They insist image and conventional sign just as language. These opposit theory rearranged conventionalism by the entrance on the cognitive science. The copy theory couldn't explain the problem of representational gap between reality and duplication, but photo media makes new paradigm about theory of the illusion. The problem of representational gap was disappeared by CGI images on the base of digital media. We are exposed exquisite duplication for a example, movie, advertisement, printings. Sometimes duplications are more real than the original works. Digital is a non-material object by 0 and 1. Specially real lighting skill and mechanism are copied perfectly by photon mapping skills and the duplications are produced more real than the original works. By disappearance of representational gap, we need new theory model for explaining of digital illusion and copy theory can be the key.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.58-64
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• 2016

In this paper presents Robust k-means clustering-based high-speed bar code decoding method to blur and lighting. for fast operation speed and robust decoding to blur, proposed method uses adaptive local threshold binarization methods that calculate threshold value by dividing blur region and a non-blurred region. Also, in order to prevent decoding fail from the noise, decoder based on k-means clustering algorithm is implemented using area data summed pixel width line of the same number of element. Results of simulation using samples taken at various worst case environment, the average success rate of proposed method is 98.47%. it showed the highest decoding success rate among the three comparison programs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.