A gamut expansion method was developed for digital images that were made to be reproduced on devices having narrow gamut such as sRGB. In the method, chroma C^* of an image was increased so as to be inside a color gamut obtained from two databases of object colors. Three different functions were used to increase chroma in this study. Subjective evaluation of naturalness of image appearance revealed that the function “nonlinear” gave the best result, and it was confirmed that the proposed method was better than the method we had reported in the previous study. The observers who were engaged in the subjective evaluation were categorized into two different groups by cluster analysis. The two groups showed an opposite characteristics in evaluation.

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This paper proposes a new piecewise linearization method to approximate arbitrary curve and verifies the validity of the proposed method by experience. Previous methods are applied only to the sequence of points whose coordinate values are known. Furthermore, the sequence needs to be arranged into permutation. This is caused by the difficulty in extracting line segments from arbitrary curve. So, in this proposed method, first, by applying the group of patterns which is named ‘Angle pattern’, the sequence of point on each pixels is detected and the angle of the sequence is shown. Then, the angle on each pixels is divided into the group of labels which is named ‘Line segment label’ by using relaxation method. After labeling, line segments can be extracted. Because this proposed method is able to approximate arbitrary curve without pre-knowledge of the curve, it is considered that this method is quite effective method for feature detection in computer vision.

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In recent years, security system by visual monitoring has been spread into various fields under various environments. A main purpose of the security system is detecting an invader and a suspicious person. However, when the contrast of scene image is not good enough, it is difficult to recognize the features of a person visually. This paper proposes a method to generate a single partial image including an area of interest with improved contrast from a scene image with a moving object. To obtain an output image with better contrast, only useful frames are selected in an objective scene image and these frames are accumulated after two-dimensional alignment. The genetic algorithm is applied to optimize the both numbers of shifting frames and moving frames. The experimental results showed that the generated images by the proposed method had good contrast, clearness and less noise in comparison with the generated image by the conventional method.

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Recently, an infringement of copyright by illegal copy of digital contents becomes a problem. Digital watermark technology is a solution of this problem. Watermarking methods are classified into two types. One is a reversible watermarking method which can restore to the original image from a watermarked one, and the other is an irreversible watermarking method. However, an irreversible watermarking method cannot be utilized to some sort of application system such as a medical imaging system. Therefore, we propose a reversible watermarking method. In the proposed method, two wavelet coefficients in several bands are treated as a vector, and one bit of a watermark is embedded into the vector. The reversible restoration can be performed by keeping one-to-one relationship between the original vector and the converted one. Simulation results show that the proposed method has better performance than the prior study using wavelet coefficients.

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A temporal subtraction technique can be used for enhancing interval changes on medical images by removing most the normal structures. In this study, we developed a temporal subtraction technique with multiple-detector CT (MDCT). This temporal subtraction method consisted of registration by use of a 3-dimensional image matching technique to reduce subtraction artifacts on the temporal subtraction image. Our method was applied onto 20 clinical cases including abnormal and normal cases. In the abnormal cases, at least one of abnormalities is exist on current or/and previous image. With our method, the sum and the full width at half maximum of the histogram from the temporal subtraction image, which was defined as the amount and the contrast of subtraction artifacts, were reduced by 18.9% and by 16.6%, respectively.

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