This letter proposes an edge-adaptive estimation algorithm for estimating the mean and standard deviation of the image block in the Mean-Separated and Normalized Vector Quantizer with Feedback Estimation (FMSN-VQ). The adaptation is performed according to the block state which is estimated from its neighboring coded blocks with the consideration of the edge presence and orientation. The simulation results show that the estimation error is significantly reduced and higher SNR is achieved compared to FMSN-VQ with a fixed estimator. Furthermore, this algorithm adds very little complexity.

This paper describes an adaptive sensing system with tracking and zooming a moving object in the stable environment. Both the close contour matching technique and the effective determination of zoom ratio by fuzzy control are proposed for achieving the sensing system. First, the estimation of object feature parameters, 2-dimensional velocity and size, is based on close contour matching. The correspondence problem is solved with cross-correlation in projections extracted from object contours in the specialized difference images. In the stable environment, these contours matching, capable of eliminating occluded contours or random noises as well as background, works well without heavy-cost optical flow calculation. Next, in order to zoom the tracked object in accordance with the state of its shape or movement practically, fuzzy control is approached first. Three sets of input membership function--the confidence of object shape, the variance of object velocity, and the object size--are evaluated with the simplified implementation. The optimal focal length is achieved of not only desired size but safe tracking in combination with fuzzy rule matrix constituted of membership functions. Experimental results show that the proposed system is robust and valid for numerous kind of moving object in real scene with system period 1.85 sec.

This paper proposes a new method for reconstruction a shape of skin surface replica from shaded image sequence taken with different light source directions. Since the shaded images include shadows caused by surface height fluctuation, and specular and inter reflections, the conventional photometric stereo method is not suitable for reconstructing its surface accurately. In the proposed method, we choose measured intensity which does not include specular and inter reflections and self-shadows so that we can calculate accurate normal vector from the selected measured intensity using SVD (Singular Value Decomposition) method. The experimental results from real images demonstrate that the proposed method is effective for shape reconstruction from shaded images, which include specular and inter reflections and self-shadows.

In general, the analysis-synthesis systems are constructed on a linear frequency scale. On the other hand, the frequency resolution of human hearing system have non-linear characteristics. So, it is interesting to study about the analysis-synthesis system on such a non-linear frequency scale like MEL scale. And it is well known that LSP analysis-synthesis method is superior to LPC or PARCOR method in frame rate and quantization characteristics. In this paper, we describe an LSP analysis-synthesis system on MEL frequency scale. At first, we propose the way to obtain LSP parameters on Mel frequency scale (Mel LSP parameters) from the speech signal in linear time domain. Next we propose how to construct the analysis and synthesis filters in linear time domain using the MEL LSP parameters. Furthermore, we combine this system with the ordinary LSP analysis-synthesis system to improve the quality of the synthetic speech. We carried out some experiments to make clear the characteristics of the combined system. The results of tests show that the quality of synthetic speech with the combined system is higher than that with the ordinary LSP system and that with the MEL LSP system on condition that total prediction order is 10. Through the further experiments, we confirm that the synthetic speech quality with the combined system is as good as the that with the standard LSP system at prediction order 12.

Semantic image segmentation and appropriate region content description are crucial issues for region-based image retrieval (RBIR). In this paper, a novel region-based image retrieval method is proposed, which performs fast coarse image segmentation and fine region feature extraction using the decomposition property of image wavelet transform. First, coarse image segmentation is conducted efficiently in the Low-Low(LL) frequency subband of image wavelet transform. Second, the feature vector of each segmented region is hierarchically extracted from all different wavelet frequency subbands, which captures the distinctive feature (e.g., semantic texture) inside one region finely. Experiment results show the efficiency and the effectiveness of the proposed method for region-based image retrieval.

An approach to estimate the information of moving objects is described in terms of their kinetic and static properties such as 2D velocity, acceleration, position, and the size of each object for the features of motion snd shape. To obtain the information of motion/shape of multiple objects, an advanced contour matching scheme is developed, which includes the synthesis of edge images and the analysis of object shape with a high matching confidence as well as a low computation cost. The scheme is composed of three algorithms: a motion estimation by an iterative triple cross-correlation, an image synthesis by shifting and masking the object, and a shape analysis for determining the object size. Implementing fuzzy membership functions to the object shape, the scheme gets improved in accuracy of capturing motion and shape of multiple moving objects. Experimental result shows that the proposed method is valid for several walking men in real scene.

This paper introduces a new method to recover 3-D road plane from its 2-D monocular perspective image. The research is aimed at the reconstruction of depth information from the 2-D visual input in road following and navigation. Planar road model is considered and the road-centered coordinate system which forms slope and turn angles with camera-centered coordinate system is used to describe boundary points on road plane. We develop approaches to find matching points of boundaries of road and to obtain angular parameters thereafter. A way of finding depth of matching points from the perspective images and angular parameters together is proposed. Therefore the 3-D road reconstruction can be replicated without introducing any parameters of inverse perspective.

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The same as all the color image processing, the segmentation of color space is an essential and important step in our system; and here HSI color space is adopted rather than RGB color space. The color space is segmented into achromatic and chromatic regions first; and the segmentation is completed after thresholding the intensity histogram of the achromatic region and the hue histogram of the chromatic region. Then text region extraction and classification follows. After detecting fundamental features (stroke width and local label width) text regions are determined by comparing smeared blocks to the original candidate image. Based on the general cover design model, text regions are classified into author region, title region, and publisher region furthermore, and a bibliography image is obtained as a result, without applying OCR. The appearance of the book is 3D reconstructed as well. In this paper, two examples are presented.

Correspondence problem in road image sequence is discussed and a method to establish road correspondence from its perspective image sequence is suggested. The proposed method is mainly based on the features of turn angles of road edge points, while the turn angle for each edge point at one time can be computed from the frame based on the determination of matching points whin that frame. The turn angles will change from frame to frame according to the panning rotation of the camera and, each stationary edge point, the difference of turn angles between two frames equals the panning angle of the camera. Thus we develop an algorithm to estimate the value of panning angle of the camera by which correspondence in road image sequence can be established.

Efficient content-based retrieval of complex images is a challenging task since the detected object may appear in various scale, rotation and orientation with a wide variety of background colors and forms. In this paper, we propose a novel representation of objects with multiple colors, the spatial neighborhood-adjacency graph(SNAG), which can serve as a basis for detecting object by color contents from the candidate image. The SNAG consists of a set of main-vertices and two sets of edges. Each main-vertex represents a single color region of multi-colored object, and edges are divided into two classes: Neighborhood edges representing neighborhood relationship between two main-vertices with similar color, and adjacency edges representing adjacency relationship between a main-vertex and another vertex with different color. By investigating whether SNAG of object image is an isomorphic subgraph of SNAG of a candidate image, we can determine whether the similar object exists in the candidate image. In addition, we have also applied the proposed approach to a range of different object detection problems involving complex background, and effectiveness has been proved.

Image processing about the vehicle is considered in this paper. When a vehicle is in a factory, image processing is applied for design and inspection, and when vehicle is on the road image processing is useful for Intelligent Transport Systems, which recently have been developed widely. There have been many researches and implementations using image sensors to get information for traffic control and vehicle control. The image seen from camera located beside or upon the road can be used for vehicle detection, velocity of car or car group measurement, parking car detection, etc. Moreover the image seen from camera located in vehicle can be used for preceding car detection, measurement of the distance to preceding car, obstacle detection, lane detection, etc. In this paper, studies about Image Processing for vehicle on the road are described.

The objective of this paper is to present a decision support system which uses a computer-based procedure to detect tumor blocks or lesions in digitized medical images. The authors developed a simple method with a low computation effort to detect tumors on T2-weighted Magnetic Resonance Imaging (MRI) brain images, focusing on the connection between the spatial pixel value and tumor properties from four different perspectives: 1) cases having minuscule differences between two images using a fixed block-based method, 2) tumor shape and size using the edge and binary images, 3) tumor properties based on texture values using spatial pixel intensity distribution controlled by a global discriminate value, and 4) the occurrence of content-specific tumor pixel for threshold images. Measurements of the following medical datasets were performed: 1) different time interval images, and 2) different brain disease images on single and multiple slice images. Experimental results have revealed that our proposed technique incurred an overall error smaller than those in other proposed methods. In particular, the proposed method allowed decrements of false alarm and missed alarm errors, which demonstrate the effectiveness of our proposed technique. In this paper, we also present a prototype system, known as PCB, to evaluate the performance of the proposed methods by actual experiments, comparing the detection accuracy and system performance.

This paper proposes a Mean-Separated and Normalized Vector Quantizer with edge-Adaptive Feedback estimation and variable bit rates (AFMSN-VQ). The basic idea of the AFMSN-VQ is to estimate the statistical parameters of each coding block from its previous coded blocks and then use the estimated parameters to normalize the coding block prior to vector quantization. The edge-adaptive feedback estimator utilizes the interblock correlations of edge connectivity and gray level continuity to accurately estimate the mean and standard deviation of the coding block. The rate-variable VQ is to diminish distortion nonuniformity among image blocks of different activities and to improve the reconstruction quality of edges and contours to which the human vision is sensitive. Simulation results show that up to 2.7dB SNR gain of the AFMSN-VQ over the non-adaptive FMSN-VQ and up to 2.2dB over the 1616 ADCT can be achieved at 0.2-1.0 bit/pixel. Furthermore, the AFMSN-VQ shows a comparable coding performance to ADCT-VQ and A-PE-VQ.

In order to reconstruct the 3 D geometric model of a real object from multi-view line drawings some restrictions in a symmetric space of the original 3 D space have been set up. Based on these restriction a new algorithm of 3 D geometric model reconstruction is proposed. This method can work effectively under the more natural condition than other methods, and it is suitable for the situation of auxiliary views as well as the situation of perspective projection. Some practical computational results have been made which show that this algorithm is effective.

The information compression by LSP analysis-synthesis is a promising method in the sense that the speech with relatively high quality can be synthesized with small amount of codes. Up to present, coding of LSP parameters have been investigated from various viewpoint. It rarely happens that the speech information changes rapidly with time. And, there exists a correlation between two time-series of LSP which are adjacent on the frequency-axis. We have already proposed the coding method which is considered these two features of LSP parameters. However, there still exists redundancy which is lying on the sorrounding LSP parameters. This paper proposes a new LSP coding method which employs the fuzzy reasoning. Using the fuzzy reasoning, it becomes possible that the useful information lying on the surrounding LSP parameters influence the new coding method. Applying the proposed method, the coding experiment and the quality evaluation of the synthetic speeches were performed by comparing our previous method on condition that analysis order is 10. Experiments confirmed two principal results. First, number of transmitting bits which is required to suppress the spectral envelope distortion to 1 dB or less, decreases to 20.8 to 34.7 bit per frame. Second, this coding method needs about 2.8 kbit/sec for transmitting the vocal tract information to suppress the spectral envelope distortion including the time distortion to 1 dB.