In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.

We propose a learning method combining query learning and a "genetic translator" we previously developed. Query learning is a useful technique for high-accuracy, high-speed learning and reduction of training sample size. However, it has not been applied to practical optical character readers (OCRs) because human beings cannot recognize queries as character images in the feature space used in practical OCR devices. We previously proposed a character image reconstruction method using a genetic algorithm. This method is applied as a "translator" from feature space for query learning of character recognition. The results of an experiment with hand-written numeral recognition show the possibility of training sample size reduction.

This paper presents a semi-automatic algorithm for video object segmentation. Our algorithm assumes the use of multiple key video frames in which a semantic object of interest is defined in advance with human assistance. For video frames between every two key frames, the specified video object is tracked and segmented automatically using Learning Vector Quantization (LVQ). Each pixel of a video frame is represented by a 5-dimensional feature vector integrating spatial and color information. We introduce a parameter K to adjust the balance of spatial and color information. Experimental results demonstrate that the algorithm can segment the video object consistently with less than 2% average error when the object is moving at a moderate speed.

For physically disabled persons, the conventional computer keyboard is insufficient as a useable communication device. In this paper, Morse code is selected as a communication adaptive device for persons with impaired hand coordination and dexterity. Morse code is composed of a series of dots, dashes, and space intervals. Each element is transmitted by sending a signal for a defined length of time. Maintaining a stable typing rate by the disabled is difficult. To solve this problem, a suitable adaptive automatic recognition method, which combines a variable degree variable step size LMS algorithm with a learning vector quantization method, was applied to this problem in the present study. The method presented here is divided into five stages: space recognition, tone recognition, learning process, adaptive processing, and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison to alternative methods in the literature.

In this paper, the generalized learning vector quantization (GLVQ) algorithm is applied to design a hand-written Chinese character recognition system. The system proposed herein consists of two modules, feature transformation and recognizer. The feature transformation module is designed to extract discriminative features to enhance the recognition performance. The initial feature transformation matrix is obtained by using Fisher's linear discriminant (FLD) function. A template matching with minimum distance criterion recognizer is used and each character is represented by one reference template. These reference templates and the elements of the feature transformation matrix are trained by using the generalized learning vector quantization algorithm. In the experiments, 540100 (5401 100) hand-written Chinese character samples are used to build the recognition system and the other 540100 (5401 100) samples are used to do the open test. A good performance of 92.18 % accuracy is achieved by proposed system.

This paper focuses on competitive learning algorithms for WTA (winner-take-all) networks which perform rotation invariant pattern classification. Although WTA networks may theoretically be possible to achieve rotation invariant pattern classification with infinite memory capacities, actual networks cannot memorize all input data. To effectively memorize input patterns or the vectors to be classified, we present two algorithms for learning vectors in classes (LVC1 and LVC2), where the cells in the network memorize not only weight vectors but also their firing numbers as statistical values of the vectors. The LVC1 algorithm uses simple and ordinary competitive learning functions, but it incorporates the firing number into a coefficient of the weight change equation. In addition to all the functions of the LVC1, the LVC2 algorithm has a function to utilize under-utilized weight vectors. From theoretical analysis, the LVC2 algorithm works to minimize the energy of all weight vectors to form an effective memory. From computer simulation with two-dimensional rotated patterns, the LVC2 is shown to be better than the LVC1 in learning and generalization abilities, and both are better than the conventional Kohonen self-organizing feature map (SOFM) and the learning vector quantization (LVQ1). Furthermore, the incorporation of the firing number into the weight change equation is shown to be efficient for both the LVC1 and the LVC2 to achieve higher learning and generalization abilities. The theoretical analysis given here is not only for rotation invariant pattern classification, but it is also applicable to other WTA networks for learning vector quantization.