An Adaptive Histogram Equalization Based Local Technique for Contrast Preserving Image Enhancement

Lee, Joonwhoan,Pant, Suresh Raj,Lee, Hee-Sin Korean Institute of Intelligent Systems 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.1

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

An Adaptive Histogram Equalization Based Local Technique for Contrast Preserving Image Enhancement

Joonwhoan Lee,Suresh Raj Pant,Hee-Sin Lee 한국지능시스템학회 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.1

The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

Fuzzy Similarity-Based Emotional Classification of Color Images

Joonwhoan Lee,EunJong Park IEEE 2011 IEEE transactions on multimedia Vol.13 No.5

<P>This paper proposes a novel scheme for evaluating an emotional response to color images. The proposed scheme uses case-based reasoning in which the prototypical color images for each emotion are stored as cases and are compared with the images to be evaluated. In the comparison, the similarities in terms of image descriptors play an important role, and their combination is crucial for the construction of a proper similarity measure. In the training phase of the proposed scheme, the weights that represent the unequal importance of each descriptor is determined in order to obtain a similarity measure that can be used to evaluate and classify a color image with respect to a pair of emotions. Prior to classification, the representative color images are chosen for each emotion by human subjects and are stored as cases. The stored images are compared with an image to be classified using the constructed similarity measure to determine which emotion is appropriate between a pair of emotions. In this study, we used color and texture descriptors recommended by MPEG-7, represented as high-dimensional vectors. In the training, we proposed a method based on the rough approximation and the fuzzy inter- and intra-similarities to determine the weights that represent the unequal importance of the complex MPEG-7 descriptors. Experimental results show a promising performance for the proposed scheme, and better performance could be achieved by including more prototypical images as cases.</P>

퍼지관계에 기반한 한국 음식과 맛 평가 형용사 분석

이준환(Joonwhoan Lee),박근호(Keunho Park),노정옥(Jeong-Ok Rho) 한국지능시스템학회 2013 한국지능시스템학회논문지 Vol.23 No.5

본 논문에서는 퍼지 관계를 이용하여 한국 음식과 해당 음식의 맛을 표현하는 관능 형용사를 분석하였다. 이를 위하여 음식의 맛뿐만 아니라 냄새 등도 표현할 수 있는 87개의 한국어 형용사를 선별하고, 20명의 실험자를 대상으로 51개의 한국음식들을 시식하게 하고 해당 음식 맛 표현에 적합한 형용사를 선택하게 하는 관능 평가를 실시하였다. 이렇게 얻어진 결과로 부터 퍼지 관계를 구성하고 음식과 형용사의 특성을 분석하였다. 또한 퍼지관계 합성을 통하여 음식과 음식 사이의, 또는 형용사와 형용사 사이의 퍼지허용(호환)관계를 구성하였으며, 이들 관계의 퍼지 완전 α-커버(fuzzy complete α-cover)로 부터 음식과 형용사의 분류체계를 탐색할 수 있었다. 본 논문의 퍼지 관계를 이용한 방법은 비단 음식과 맛 표현 뿐만 아니라 후각과 촉각과 같은 관능 형용사를 분석하는데 활용될 것을 기대된다. In this paper we analyze the Korean foods and sensory adjectives that can be used for the taste expression of corresponding food based on the fuzzy relation. In order to construct fuzzy relation we gathered and chose 87 related Korean adjectives for expressing not only taste but also smell from foods. After then we performed a sensory evaluation for 51 Korean foods with 20 subjects to check the proper adjectives when they take a food. Based on the data collected by the evaluation a fuzzy relation is constructed and used for the analysis of the properties of food and adjectives. In addition the composition of the fuzzy relation provides the fuzzy tolerance(compatibility) relation among foods as well as that among adjectives. From the fuzzy complete α-cover of the relations we could explore the taxonomy of food or adjectives. We expect that the fuzzy relation-based scheme in the paper can be utilized for analysis of the sensory adjectives like smelling and tactile sensation.

컴퓨터 비전을 이용한 터널 유고감지 시스템

정성환 ( Sung-hwan Jeong ),주영호 ( Young-ho Ju ),이희신 ( Hee-sin Lee ),이종태 ( Jong-tae Lee ),이준환 ( Joonwhoan Lee ) 한국정보처리학회 2012 한국정보처리학회 학술대회논문집 Vol.19 No.1

본 논문에서는 터널 내 유고 상황을 실시간으로 빠르게 감지하여 터널 관리자에게 상황을 전달하여 터널의 안전한 운영에 도움을 줄 수 있는 컴퓨터 비전을 이용한 터널 유고감지 시스템을 제안하였다. 제안한 시스템은 관리자, 서버, 영상 검지기로 구성되며 영상 검지기의 경우 객체를 추출하기 위하여 배경차이법을 사용하였으며, 터널 내에서 발생하는 조명의 변화, 입·출입구의 조명의 영향, 카메라의 프리컬링 잡음의 영향을 최소화하였으며, 터널 내에서 발생할 수 있는 정지물체, 차량 외 통행, 연기, 역주행, 정체·지체의 유고 상황을 감지하는 방법을 개발하였다. 제안한 시스템을 전남 여수의 마래터널 및 엑스포터널, 전북 임실의 운암터널에서 실험한 결과 터널 내에서 발생하는 유고 상황을 감지하였다.

Comparison of Two Methods for Stationary Incident Detection Based on Background Image

DeepakGhimire,Joonwhoan Lee 한국스마트미디어학회 2012 스마트미디어저널 Vol.1 No.3

In general, background subtraction based methods are used to detect the moving objects in visual tracking applications. In this paper we employed background subtraction based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection and we compare those in terms of detection performance and computational complexity. In the first approach we used single background and in the second approach we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped object. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short time fully occlusion and illumination changes, as well as it can operate in real time.

A Deep-Learning Based Model for Emotional Evaluation of Video Clips

Byoungjun Kim,Joonwhoan Lee 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.4

Emotional evaluation of video clips is the difficult task because it includes not only stationary objects as the background but also dynamic objects as the foreground. In addition, there are many video analysis problems to be solved beforehand to properly address the emotionrelated tasks. Recently, however, the convolutional neural network (CNN)-based deep learning approach, opens the possibility by solving the action recognition problem. Inspired by the CNN-based action recognition technology, this paper challenges to evaluate the emotion of video clips. In the paper, we propose a deep learning model to capture the video features and evaluate the emotion of a video clip on Thayer 2D emotion space. In the model, the pre-trained convolutional 3D neural network (C3D) generates short-term spatiotemporal features of the video, LSTM accumulates those consecutive time-varying features to characterize long-term dynamic behaviors, and multilayer perceptron (MLP) evaluates emotion of a video clip by regression on the emotion space. Due to the limited number of labeled data, the C3D is employed to extract diverse spatiotemporal from various layers by transfer learning technique. The pre-trained C3D on the Sports-1M dataset and long short term memory (LSTM) followed by the MLP for regression are trained in end-to-end manner to fine-tune the C3D, and to adjust weights of LSTM and the MLP-type emotion estimator. The proposed method achieves the concordance correlation coefficient values of 0.6024 for valence and 0.6460 for arousal, respectively. We believe this emotional evaluation of video could be easily associated with appropriate music recommendation, once the music is emotionally evaluated in the same high-level emotional space.

Hybrid Filter Based on Neural Networks for Removing Quantum Noise in Low-Dose Medical X-ray CT Images

Park, Keunho,Lee, Hee-Shin,Lee, Joonwhoan Korean Institute of Intelligent Systems 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.2

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

A Robust Face Detection Method Based on Skin Color and Edges

Deepak Ghimire,Joonwhoan Lee 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.1

Nonlinear transfer function-based local approach for color image enhancement

Ghimire, D.,Joonwhoan Lee IEEE 2011 IEEE TRANSACTIONS ON CONSUMER ELECTRONICS - Vol.57 No.2

<P>The main objective of image enhancement is to improve some characteristic of an image to make it visually better one. This paper proposes a method for enhancing the color images based on nonlinear transfer function and pixel neighborhood by preserving details. In the proposed method, the image enhancement is applied only on the V (luminance value) component of the HSV color image and H and S component are kept unchanged to prevent the degradation of color balance between HSV components. The V channel is enhanced in two steps. First the V component image is divided into smaller overlapping blocks and for each pixel inside the block the luminance enhancement is carried out using nonlinear transfer function. In the second step, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel value and its neighborhood pixel values. Finally, original H and S component image and enhanced V component image are converted back to RGB image. The subjective and objective performance evaluation shows that the proposed enhancement method yields better results without changing image original color in comparison with the conventional methods.</P>