As a novel therapy for peritoneal dissemination, it is desired to actualize an endoscopic photothermal therapy, which is minimally invasive and is highly therapeutically effective. However, since the endoscopic tumor temperature control has not been actualized, conventional therapies could damage healthy tissues by overhearing. In this paper, we develop a thermal endoscope system that controls the tumor temperature so that the heated tumor gets necrotic. In fact, our thermal endoscope contains a thermal image sensor, a visible light endoscope and a laser fiber. Concerning the thermal image sensor, the conventional thermal endoscope has the problem that the diameter is too large, because the conventional endoscope loads a large thermal image sensor with high-resolution. Therefore, this paper uses a small thermal image sensor with low resolution, because the diameter of the thermal endoscope needs to be smaller than 15mm in order to be inserted into the trocar. However, this thermal image sensor is contaminated by much noise. Thus, we develop a tumor temperature control system using a feedback control and tumor temperature estimation based on Gaussian function, so that the noisy, small thermal image sensor can be used. As experimental results of the proposed endoscopic photothermal therapy for the hepatophyma carcinoma model of rats, it turns out that the tumor temperature by which the heated tumor gets necrotic can be kept stable. It can be said that our endoscopic photothermal therapy achieves a certain degree of therapy effect.

In order to realize automatic recognition of surgical processes in surgical brain tumor removal using microscopic camera, we propose a method of detecting and tracking surgical tools by video analysis. The proposed method consists of a detection part and tracking part. In the detection part, object detection is performed for each frame of surgery video, and the category and bounding box are acquired frame by frame. The convolution layer strengthens the robustness using data augmentation (central cropping and random erasing). The tracking part uses SORT, which predicts and updates the acquired bounding box corrected by using Kalman Filter; next, the object ID is assigned to each corrected bounding box using the Hungarian algorithm. The accuracy of our proposed method is very high as follows. As a result of experiments on spatial detection. the mean average precision is 90.58%. the mean accuracy of frame label detection is 96.58%. These results are very promising for surgical phase recognition.

This paper presents a novel method for extrinsic parameters estimation of a single line scan LiDAR and a camera. Using a checkerboard, the calibration setup is simple and practical. Particularly, the proposed calibration method is based on resolving geometry of the checkerboard that visible to the camera and the LiDAR. The calibration setup geometry is described by planes, lines and points. Our novelty is a new hypothesis of the geometry which is the orthogonal distances between LiDAR points and the line from the intersection between the checkerboard and LiDAR scan plane. To evaluate the performance of the proposed method, we compared our proposed method with the state of the art method i.e. Zhang and Pless [1]. The experimental results showed that the proposed method yielded better results.

As a machine learning algorithm, AdaBoost has obtained considerable success in data classification and object detection. Later its generalized version called Real AdaBoost was proposed by Schapire and Singer. Real AdaBoost increases weights for misclassified samples and decreases weights for correctly classified samples in every iteration. This kind of weight adjustment focuses on the samples with large weights and tries to make them correctly classified in future runs. However, it may lead to the misclassification of some other samples that have been correctly classified in previous runs. If we can curb this kind of misclassification during the boosting process, a faster training can be achieved. Based on this assumption, we propose Parameterized AdaBoost in which a parameter is devised to penalize the misclassification of samples that have already been correctly classified. Then we analyse that samples with positive classification margins in Parameterized AdaBoost are more than in Real AdaBoost. Experimental results show that our approach achieves a faster convergence of the training error and also improves the generalization error to some degree when compared with Real AdaBoost.

In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings(DGP) such as morphing, shape blending, texture transfer, re-meshing and so on. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of our method is to combine a competitive learning and a leastsquare mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses. We show the effectiveness of our approach by giving some examples of its applications.

This paper proposes a new method for integrating range images with different resolutions to generate a whole surface model of a 3D object. Generally, a modeling method using range images needs an overlapping area between the images for the registration process. Accordingly, there are redundant data in the overlapping area. In order to produce a seamless and nonredundant model, we integrate the data, and represent the overlapping area with triangular patches. However, few conventional methods have paid attention to integrating images with different resolutions. We propose a new integration method in which geometric and topological information are used. Experimental results show that our proposed method is efficient for integrating multiple range images

In recent facsimile application fields, mixed documents comprising characters and photographs have come to be generally treated. Following this trend, the `joint bi-level image group (JBIG)' of ISO/IEC/JTC1/SCWG9 and CCITT/SG VIII prepared an international standard for the encoding of binary images obtained by quantizing mixed documents into binary levels. This JBIG coding scheme consists of two parts: (1) the modeling part based on binary Markov model referring to 10 pixels surrounding a current pixel to be encoded and (2) the coding part based on an adaptive arithmetic compression coder. This paper presents an adaptive model for mixed binary images, which realizes higher compression efficiency than the typical Markov model of the JBIG scheme. We describe two significant characteristics, generalized model and area classification. The generalized model refers not only to neighboring pixels like the typical Markov model, but also to a predicted gray-level calculated pixel by pixel from previously scanned pixels near a current pixel. The area classification classifies mixed binary images including both characters and halftone images into two types of areas. In this adaptive model, the typical Markov model and the generalized model for halftone images are selected according to the types of the areas. As a result, the adaptive model reduces the values of Markov entropy of mixed binary images and realizes high compression efficiency.

A method is proposed to extract two-dimensional shapes which are similar to a given model shape from a binary image composed of black and white pixels. This extraction problem is equivalent to the problem of determining the position, size, and rotational angle of each similar shape in the binary image. The model shape is transformed with four space transformation parameters, (chi) c, yc, M, and (theta) , and the transformed model shape is overlapped with the binary image. Parameters (chi) c and yc denote xy coordinate values of the center of gravity of the transformed model shape, M is magnification ratio and (theta) is rotational angle. The research goal here is to find out the space transformation parameter set that gives the maximum matching rate between the transformed model shape and a similar shape in the binary image. Genetic algorithm (GA), which is a kind of searching or optimizing algorithm, is employed for this problem. In this method, several virtual living things whose chromosomes represent space transformation parameters are randomly generated in a computer, and they are evolved according to GA. As the result of generation iterations, an evolved individual corresponding to the best space transformation parameter set is obtained. Algorithm of the method and several experimental results are described.

We report a pattern description and generation method of structural characters, and show some examples of Chinese and Korean character patterns actually generated. We also consider its educational and graphical applications. In this method, any character is regarded as a composite pattern constructed by several simpler subpatterns, and is described in terms of them by introducing three kinds of positional relationships among them. A composite pattern can become a subpattern, too. We call these patterns blocks. Syntactic grammar is defined to encode a pattern expression by two code strings, that is, a string of blocks and a string of production rules. They are used in generating patterns, namely, derivation of pattern expression from the code strings is defined as a process of pattern generation. By this description and generation method, we can encode structural characters without much memory, and generate natural shapes of patterns.

A description method of handprinted Chinese characters is presented. In the method, a Chinese character is composed of some partial patterns which are constructed using the concatenate relation, cross relation, and near relation. The relations of relative location among partial patterns are used for categorization of the partial pattems. A Chinese character is expressed from the results of categorization.

In this paper we propose a coding method of handprinted Chinese characters in which, defining a grammar, the process of block categorization is made to correspond to a process of sentential generation. We obtain two strings of production rules and block codes being equivalent to the descriptions of the structure and internal components of a character pattern, and a Chinese character is encoded effectively by the use of these two strings. It is shown that this coding method is available for the classification and discrimination of handprinted Chinese characters.