• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.10
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• pp.604-614
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• 2005

Color segmentation takes great attentions since a color is an effective and robust visual cue for characterizing one object from other objects. Color segmentation is, however, suffered from color variation induced from irregular illumination changes. This paper proposes a reliable color modeling approach in HSI (Hue-Saturation-Intensity) rotor space considering intensity information by adopting B-spline curve fitting to make a mathematical model for statistical characteristics of a color with respect to brightness. It is based on the fact that color distribution of a single-colored object is not invariant with respect to brightness variations even in HS (Hue-Saturation) plane. The proposed approach is applied for the segmentation of human skin areas successfully under various illumination conditions.

• 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 Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.738-744
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• 2016

In this paper, a robust visual odometry system has been proposed and implemented in an environment with dynamic illumination. Visual odometry is based on stereo images to estimate the distance to an object. It is very difficult to realize a highly accurate and stable estimation because image quality is highly dependent on the illumination, which is a major disadvantage of visual odometry. Therefore, in order to solve the problem of low performance during the feature detection phase that is caused by illumination variations, it is suggested to determine an optimal threshold value in the image binarization and to use an adaptive threshold value for feature detection. A feature point direction and a magnitude of the motion vector that is not uniform are utilized as the features. The performance of feature detection has been improved by the RANSAC algorithm. As a result, the position of a mobile robot has been estimated using the feature points. The experimental results demonstrated that the proposed approach has superior performance against illumination variations.

• Journal of the HCI Society of Korea
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• v.6 no.1
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• pp.21-26
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• 2011

This paper presents a robust method for facial image synthesis under varying illumination by combining illumination correction and Poisson image processing techniques. The presented method automatically detects skin area and corrects highly saturated regions that can cause bad effects on the final synthesis image. The developed method can be applied to various facial synthesis applications by correcting illumination variations that can occur frequently on photos taken with a camera phone.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.25-44
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• 2010

With the increasing popularity of mobile devices, it has become necessary to protect private information and content in these devices. Face recognition has been favored over conventional passwords or security keys, because it can be easily implemented using a built-in camera, while providing user convenience. However, because mobile devices can be used both indoors and outdoors, there can be many illumination changes, which can reduce the accuracy of face recognition. Therefore, we propose a new face recognition method on a mobile device robust to illumination variations. This research makes the following four original contributions. First, we compared the performance of face recognition with illumination variations on mobile devices for several illumination normalization procedures suitable for mobile devices with low processing power. These include the Retinex filter, histogram equalization and histogram stretching. Second, we compared the performance for global and local methods of face recognition such as PCA (Principal Component Analysis), LNMF (Local Non-negative Matrix Factorization) and LBP (Local Binary Pattern) using an integer-based kernel suitable for mobile devices having low processing power. Third, the characteristics of each method according to the illumination va iations are analyzed. Fourth, we use two matching scores for several methods of illumination normalization, Retinex and histogram stretching, which show the best and $2^{nd}$ best performances, respectively. These are used as the inputs of an SVM (Support Vector Machine) classifier, which can increase the accuracy of face recognition. Experimental results with two databases (data collected by a mobile device and the AR database) showed that the accuracy of face recognition achieved by the proposed method was superior to that of other methods.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.32-39
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• 2004

The color-based vision systems have been used to recognize our team robots, the opponent team robots and a ball in the robot soccer system. The color-based vision systems have the difficulty in that they are very sensitive to color variations brought by brightness changes. In this paper, a neural network trained with data obtained from various illumination conditions is used to classify colors in the modified YUV color space for the robot soccer vision system. For this, a new method to measure brightness is proposed by use of a color card. After the neural network is constructed, a look-up-table is generated to replace the neural network in order to reduce the computation time. Experimental results show that the proposed color classification method is robust under illumination variations.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.151-157
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• 2006

This paper is concerned with the template-based face recognition from robot camera images with illumination and distance variations. The approaches used in this paper consist of Eigenface, Fisherface, and Icaface which are the most representative recognition techniques frequently used in conjunction with face recognition. These approaches are based on a popular unsupervised and supervised statistical technique that supports finding useful image representations, respectively. Thus we focus on the performance comparison from robot camera images with unwanted variations. The comprehensive experiments are completed for a databases with illumination and distance variations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.25-34
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• 2010

Robust face recognition under various illumination environments is difficult to achieve. For robust face recognition with respect to illumination variations, illumination normalization of face images is usually applied as a preprocessing step. Most of previously proposed illumination normalization methods cannot handle cast shadows in face images effectively. In this paper, We propose a new face illumination normalization method based on the illumination-separated face identity texture subspace. Since the face identity texture subspace is constructed so as to be separated from the effects of illumination variations, the projection of face images into the subspace produces a good illumination-normalized face images. Through experiments, it is shown that the proposed face illumination normalization method can effectively eliminate cast shadows as well as attached shadows and achieves a good face illumination normalization.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.478-486
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• 1993

For satisfactory greenhouse culture, environmental factors must be kept in proper conditions. Therefore, it is important to know relations between environmental conditions and greenhouse systems. In this study, the environment variations and distributions in different types of greenhouses were measured and analyzed. The elements of environment analyzed were temperature , relative humidity, illumination, carbon dioxide and wind velocity. The analyzed greenhouse types were three different types. One of them, A type, was propagation model type by government and the other one, B type, was multiple continuous arches type which was made by farmers himself. The last one, C type, was single arch type which has no environment control system without manual temperature keeping method. The results of this study can be used for reasonable greenhouse environments managements and control.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.67-76
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• 2008

Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.