A method of visualization of multimodal images by one monochromatic image is presented on the basis of the projection pursuit approach of the inverse process of the anisotropic diffusion which is a method of image restoration enhancing contrasts at edges. The extension of the projection from a linear one to nonlinear sigmoidal functions enhances the contrast further. The deterministic annealing technique is also incorporated into the optimization process for improving the contrast enhancement ability of the projection. An application of this method to a pair of MRI images of brains reveals its promising performance of superior visualization of tissues.

Recently, a great deal of effort has been devoted to the design problem of "constrained least squares M-D FIR filter" because a significant improvement of the squared error is expected by a slight relaxation of the minimax error condition. Unfortunately, no design method has been reported, which has some theoretical guarantee of the convergence to the optimal solution. In this paper, we propose a class of novel design methods of "constrained least squares M-D FIR filter. " The most remarkable feature is that all of the proposed methods have theoretical guarantees of convergences to the unique optimal solution under any consistent set of prescribed maximal error conditions. The proposed methods are based on "convex projection techniques" that computes the metric projection onto the intersection of multiple closed convex sets in real Hilbert space. Moreover, some of the proposed methods can still be applied even for the problem with any inconsistent set of maximal error conditions. These lead to the unique optimal solution over the set of all filters that attain the least sum of squared distances to all constraint sets.

In this paper, we propose a new denoising algorithm based on the dyadic wavelet transform (DWT) for ECG signals corrupted with different types of synthesized noise. Using the property that DWT is overcomplete, we define some convex sets in the set of wavelet coefficients and give an iterative method of the projection on the convex sets. The results show that the noises are not only removed from ECG signals, but also the ECG signals are reconstructed, which is used in detecting QRS complex. The performance of the proposed algorithm is demonstrated by some experiments in comparison with the conventional methods.

We propose a set of new algorithms for linear programming. These algorithms are derived by accelerating the method of averaged convex projections for linear inequalities. We provide strict proofs for the convergence of our algorithms. The algorithms are so simple that they can be calculated by super-parallel processing. To this effect, we propose networks for implementing the algorithms. Furthermore, we provide illustrative examples to demonstrate the capability of our algorithms.

This paper proposes a design method of feature spaces in a two-stage image recognition method that improves the recognition accuracy and efficiency in statistical image recognition. The two stages are (1) image screening and (2) image recognition. Statistical image recognition methods require a lot of calculations for spatially matching between subimages and reference patterns of the specified objects to be detected in input images. Our image screening method is effective in lowering the calculation load and improving recognition accuracy. This method selects a candidate set of subimages similar to those in the object class by using a lower dimensional feature vector, while rejecting the rest. Since a set of selected subimages is recognized by using a higher dimensional feature vector, overall recognition efficiency is improved. The classifier for recognition is designed from the selected subimages and also improves recognition accuracy, since the selected subimages are less contaminated than the originals. Even when conventional recognition methods based on linear transformation algorithms, i. e. principal component analysis (PCA) and projection pursuit (PP), are applied to the recognition stage in our method, recognition accuracy and efficiency may be improved. A new criterion, called a screening criterion, for measuring overall efficiency and accuracy of image recognition is introduced to efficiently design the feature spaces of image screening and recognition. The feature space for image screening are empirically designed subject to taking the lower number of dimensions for the feature space referred to as LS and the larger value of the screening criterion. Then, the recognition feature space which number of dimensions is referred to as LR is designed under the condition LSLR. The two detection tasks were conducted in order to examine the performance of image screening. One task is to detect the eye- and-mouth-areas in a face image and the other is to detect the text-area in a document image. The experimental results demonstrate that image screening for these two tasks improves both recognition accuracy and throughput when compared to the conventional one-stage recognition method.

We consider the situation that plural degraded images are obtained. When no prior knowledge about original images are known, these images are individually restored by an optimum restoration filter, for example, by Wiener Filter or by Projection Filter. If correlations between original images are obtained, some restoration filters based on Wiener Filter or Projection Filter are proposed. In this paper, we deal with the case that some pixels or some parts of original images overlap, and propose a restoration method using a formulae for overlapping. The method is based on Partial Projection Filter. Moreover, we confirm an efficacy of the proposed method by numerical examples.

In this paper, we present an error concealment method to recover damaged blocks for block-based image coding schemes. Imperfect transmission of image data results in damaged blocks in the reconstructed images. Hence recovering damaged image blocks is needed for reliable image communications. To recover damaged blocks is to estimate damaged blocks from the correctly received or undamaged neighborhood information with a priori knowledge about natural images. The recovery problem considered in our method is to estimate a larger block, which consists of a damaged block and the undamaged neighborhood, from the undamaged neighborhood. To find an accurate estimate, a set of the feature vectors is introduced and an estimate is expressed as a linear combination of the feature vectors. The proposed method recoveres damaged blocks by projecting the undamaged neighborhood information onto the feature vectors. The sequential projections onto the feature vectors algorithm is proposed to find the projection coefficients of the feature vectors to minimize the squared difference of an estimate and the undamaged neighborhood information. We tested our algorithm through computer simulations. The experimental results showed the proposed method ourperforms the frequency domain prediction method in the PSNR values by 4.0-5.0dB. Tthe reconstructed images by the proposed method provide a good subjective quality as well as an objective one.

In this paper, a new method for measuring three-dimensional (3D) moving facial shapes is introduced. This method uses two light sources and a slit pattern projector. First, the normal vectors at points on a face are computed by the photometric stereo method with two light sources and a conventional video camera. Next, multiple light stripes are projected onto the face with a slit pattern projector. The 3D coordinates of the points on the stripes are measured using the stereo vision algorithm. The normal vectors are then integrated within 2D finite intervals around the measured points on the stripes. The 3D curved segment within each finite interval is computed by the integration. Finally, all the curved segments are blended into the complete facial shape using a family of exponential functions. By switching the light rays at high speed, the time required for sampling data can be reduced, and the 3D shape of a moving human face at each instant can be measured.

In this paper, we propose a camera calibration method that estimates both intrinsic parameters (perspective and distortion) and extrinsic parameters (rotational and translational). All camera parameters can be determined from one or more images of planar pattern consists of parallelogramatic grid points. As far as the pattern can be visible, the relative relations between camera and patterns are arbitrary. So, we have only to prepare a pattern, and take one or more images changing the relative relation between camera and the pattern, arbitrarily; neither solid object of ground truth nor precise z-stage are required. Moreover, constraint conditions that are imposed on rotational parameters are explicitly satisfied; no intermediate parameter that connected several actual camera parameters are used. Taking account of the conflicting fact that the amount of distortion is small in the neighborhood of the image center, and that small image has poor clues of 3-D information, we adopt iterative procedure. The best parameters are searched changing the size and number of parallelograms selected from grid points. The procedure of the iteration is as follows: The perspective parameters are estimated from the shape of parallelogram by nonlinear optimizations. The rotational parameters are calculated from the shape of parallelogram. The translational parameters are estimated from the size of parallelogram by least squares method. Then, the distortion parameters are estimated using all grid points by least squares method. The computer simulation demonstrates the efficiency of the proposed method. And the results of the implementation using real images are also shown.

The ringing occurred in the step response causes an undesirable stripe pattern in TV signals. A simultaneous approximation with both the frequency and the step response is required in the designing filter which is used in the image signal processing in order to prevent the ringing. The wellknown Remez algorithm for designing FIR filters approximates the response only in the frequency domain. As the result, the filters designed by this algorithm causes the large ringing in the step response. In this paper, we propose the method of design for FIR filters with minimum amplitude in the stopband, under the condition that the step response has no ringing and the prescribed rise characteristics. For this end, we use the constrained successive projections method.

An attenuation correction method was proposed for laser-produced plasma emission computed tomography (ECT), which is based on a relation of the attenuation coefficient and the emission coefficient in plasma. Simulation results show that the reconstructed images are dramatically improved in comparison to the reconstructions without attenuation correction.

This paper introduces a new image representation method that is named the projective image representation (PIR). We consider an image as a collage of symmetric segments each of which can be well represented by its projection data of a single orientation. A quadtree-based method is adopted to decompose an image into variable sized segments according to the complexity within it. Also, we deal with the application of the PIR to the image compression and propose an efficient algorithm, the quadtree-structured projection vector quantization (QTPVQ) which combines the PIR with the VQ. As the VQ is carried out on the projection data instead of the pixel intensities of the segment, the QTPVQ successfully overcomes the drawbacks of the conventional VQ algorithms such as the blocking artifact and the difficulty in manipulating the large dimension. Above all, the QTPVQ improves the subjective quality greatly, especially at low bit rate, which makes it applicable to low bit rate image coding.

In this paper, we propose a new method to measure the 3D object shape without special purpose lighting based upon the Backprojection of Pixel Data.This method need not extract feature points such as edges from images at all and can measure not only the feature points but the whole object surface. It is simply done by project all pixel data back into the object space from each image. Actually, we first assign all pixel data of images into voxels in the object space, and evaluate the variance of assigned data for all voxels. This process is based on the idea that a point on the object surface gives the similar color information or gray level when it is observed from different view points. Then, two kinds of voting are executed as an enhancement process to eliminate the voxels containing the false points. We present experimental results under the circular constraint of camera movement and show the possibility of the proposed method.

This paper proposes a new method that can robustly recover 3D structure and 3D motion of 3D moving objects from a few multi-views. It recovers 3D feature points by obtaining intersections of back-projection lines which are connected from the camera's optical centers thorough projected feature points on the image planes corresponding to the different cameras. We show that our method needs only six views to suppress false 3D feature points in most cases by discussing the relation between the occurrence probability of false 3D feature points and the number of views. This discussion gives us a criterion to design the optimal multi-camera system for recovering 3D structure and 3D motion of 3D moving objects. An experimental multi-camera system is constructed to confirm the validity of our method. This system can take images from six different views at once and record motion image sequence from each view over a period of a few seconds. It is tested successfully on recovering the 3D structure of Vinus's plaster head and on recovering the 3D structure and 3D motion of a moving hand.

This paper presents a technique for disparity selection in the context of binocular pursuit. For vergence control in binocular pursuit, it is a crucial problem to find the disparity which corresponds to the target among multiple disparities generally observed in a scene. To solve the problem of the selection, we propose an approach based on histogramming the disparities obtained in the scene. Here we use an extended phase-based disparity estimation algorithm. The idea is to slice the scene using the disparity histogram so that only the target remains. The slice is chosen around a peak in the histogram using prediction of the target disparity and target location obtained by back projection. The tracking of the peak enables robustness against other, possibly dominant, objects in the scene. The approach is investigated through experiments and shown to work appropriately.

The main purpose of this paper is to give a new representation method of the convergence characteristics of the LMS algorithm using tap-input vectors. The described representation method is an extended version of the interpretation method based on the orthogonal projection. Using this new representation, we can express the convergence characteristics in terms of tap-input vectors instead of the eigenvalues of the input signal. From this representation, we consider a general method for improving the convergence speed.

This paper proposes a new algorithm called the fast Projection algorithm, which reduces the computational complexity of the Projection algorithm from (p+1)L+O(p3) to 2L+20p (where L is the length of the estimation filter and p is the projection order.) This algorithm has properties that lie between those of NLMS and RLS, i.e. less computational complexity than RLS but much faster convergence than NLMS for input signals like speech. The reduction of computation consists of two parts. One concerns calculating the pre-filtering vector which originally took O(p3) operations. Our new algorithm computes the pre-filtering vector recursively with about 15p operations. The other reduction is accomplished by introducing an approximation vector of the estimation filter. Experimental results for speech input show that the convergence speed of the Projection algorithm approaches that of RLS as the projection order increases with only a slight extra calculation complexity beyond that of NLMS, which indicates the efficiency of the proposed fast Projection algorithm.

In robot vision system, enormously large computation power is required to perform three-dimensional (3-D) instrumentation and object recognition. However, many kinds of complex and irregular operations are required to make accurate 3-D instrumentation and object recognition in the conventional method for software implementation. In this paper, a VLSI-oriented Model-Based Robot Vision (MBRV) processor is proposed for high-speed and accurate 3-D instrumentation and object recognition. An input image is compared with two-dimensional (2-D) silhouette images which are generated from the 3-D object models by means of perspective projection. Because the MBRV algorithm always gives the candidates for the accurate 3-D instrumentation and object recognition result with simple and regular procedures, it is suitable for the implementation of the VLSI processor. Highly parallel architecture is employed in the VLSI processor to reduce the latency between the image acquisition and the output generation of the 3-D instrumentation and object recognition results. As a result, 3-D instrumentation and object recognition can be performed 10000 times faster than a 28.5 MIPS workstation.

An electron beam cell projection system has been developed that can effectively expose the fine, demagnified resultant pattern of repeated and non-repeated patterns such as the 256 Mb DRAM on a semiconductor wafer. Particular attention was given to the beam shaping and deflecting optics, which has two stage deflectors for the cell projection beam selection as well as the beam sizing, and three stage deflectors for objective deflection. The cell mask with a rectangular aperture and multiple figure apertures is fabricated by modified Si wafer processes. A new exposure control data for the cell projection is proposed. This data is fitted for the combination of pattern data for the cell mask projection and pattern data for the variable rectangular shape beam within the divided units of the objective deflection. On this exposure system, selective exposure of the desired pattern becomes possible on the semiconductor wafer while a mounting stage of the wafer is being moved, even if the pattern exposure of the repeated and non-repeated patterns is to be carried out. The total overhead time for selecting a subset of multiple figures and a rectangular aperture of the cell mask is less than 5 seconds/wafer. The estimated throughput of this system is approximately 20 wafers/hour.

In this paper, we attempt the comparison of the image/signal restoration between Projection Filter, which is regarded as one of the linear optimal filters, and the non-linear filter based on MEM. From the simulation, we show the advantage of MEM restoration filter in restoring noisy degraded images.