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A speedy filter for removing impulse noise based on an adaptive window observation is presented. The proposed method not only reduces specific content of impulse noise but also produces a low computational complexity in several noise densities, which exploits the adaptive concept. In this research, we use an adaptive window. It is a combination of the post filtered pixel and the pixel that would be filtered. Based on the adaptive window observation, we can get the output filter. The numerical results of using peak signal-to-noise ratio and computational time prove that the proposed method is able to reduce impulse noise and to produce a low computational complexity. Furthermore, the proposed method is capable of overcoming the drawback of previous studies and provides a satisfactory result.

In this study, we propose a method for designing color quantized images for use with digital micromirror device (DMD-based projectors. It is necessary to quantize full-color images if we want to achieve a high frame rate in DMD-based systems. The quantized image should be similar to a full-color image to achieve good results. The proposed method for designing quantized images is based on an evaluation function and a particle swarm optimization algorithm. We can improve the image quality and luminance of the image displayed by tuning the weight values of the evaluation function. We evaluated the quality of the designed binary images using numerical examples.

This paper proposes improved eigen-decomposition template matching. Eigen-decomposition template matching is a method of detecting the displacement and the rotational angle more accurately than traditional rotational correlation matching methods such as the cross-correlation method. The proposed method calculates the correlation by matching images determined by eigen-decomposition of the template image to the input image instead of matching the template image to the input image directly. This paper also proposes a method of detecting the displacement and rotational angle even if the input image includes large translation displacements and intensity variations. The experimental results show that the proposed method can detect the displacement and rotational angle more accurately than the previous method for images with noise and illumination variance. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(10): 27–36, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11389

This paper proposes the improved eigen-decomposition template matching. The eigen-decomposition template matching is a method to detect the displacement and the rotational angle more accurately than the traditional rotational correlation matching such as the Cross-Correlation method. The proposed method calculates the correlation by matching the images which is determined by eigen decomposition of the template image to the input image instead of matching the template image to the input image directly. Furthermore, this paper proposes the method to detect the displacement and rotational angle even if the input image includes the large displacement translation and the intensity variations. The experimental results show that the proposed method can detect the displacement and rotational angle more accurately than the previous method for the image with a noise and an illumination variance.

We propose a method for estimating multiple-hypothesis affine regions from a keypoint by using an anisotropic Laplacian-of-Gaussian (LoG) filter. Although conventional affine region detectors, such as Hessian/Harris-Affine, iterate to find an affine region that fits a given image patch, such iterative searching is adversely affected by an initial point. To avoid this problem, we allow multiple detections from a single keypoint. We demonstrate that the responses of all possible anisotropic LoG filters can be efficiently computed by factorizing them in a similar manner to spectral SIFT. A large number of LoG filters that are densely sampled in a parameter space are reconstructed by a weighted combination of a limited number of representative filters, called "eigenfilters", by using singular value decomposition. Also, the reconstructed filter responses of the sampled parameters can be interpolated to a continuous representation by using a series of proper functions. This results in efficient multiple extrema searching in a continuous space. Experiments revealed that our method has higher repeatability than the conventional methods.

An Image matching technique of target objects recognition and detection is widely used in industrial image processing. In this paper, the authors proposed eigen template method for two dimentional target objects recognition and detection. We have proposed eigen template method of applying the principal component analysis (PCA) to image matching. Also, the authors have proposed to edge based eigen templates method for robust and efficient image matching. These methods can estimate target objects position and pose from two dimensional images. In this paper, eigen template method is extended to the method that can classify multi-class target objects. Our experiment showed that the proposed method can recognize multi-class target objects and estimate position and pose. And the authors showed that the proposed method can efficient image match compared with the previous methods.

We present a novel approach that addresses the blind reconstruction problem in scanning electron microscope (SEM) photometric stereo for complicated semiconductor patterns to be measured. In our previous work, we developed a bootstrapping de-shadowing and self-calibration (BDS) method, which automatically calibrates the parameter of the gradient measurement formulas and resolves shadowing errors for estimating an accurate three-dimensional (3D) shape and underlying shadowless images. Experimental results on 3D surface reconstruction demonstrated the significance of the BDS method for simple shapes, such as an isolated line pattern. However, we found that complicated shapes, such as line-and-space (L&S) and multilayered patterns, produce deformed and inaccurate measurement results. This problem is due to brightness fluctuations in the SEM images, which are mainly caused by the energy fluctuations of the primary electron beam, variations in the electronic expanse inside a specimen, and electrical charging of specimens. Despite these being essential difficulties encountered in SEM photometric stereo, it is difficult to model accurately all the complicated physical phenomena of electronic behavior. We improved the robustness of the surface reconstruction in order to deal with these practical difficulties with complicated shapes. Here, design data are useful clues as to the pattern layout and layer information of integrated semiconductors. We used the design data as a guide of the measured shape and incorporated a geometrical constraint term to evaluate the difference between the measured and designed shapes into the objective function of the BDS method. Because the true shape does not necessarily correspond to the designed one, we use an iterative scheme to develop proper guide patterns and a 3D surface that provides both a less distorted and more accurate 3D shape after convergence. Extensive experiments on real image data demonstrate the robustness and effectiveness of our method.

Scale-space image processing is a basic technique used for object recognition and low-level feature extraction in computer vision. Many Gaussian filtering techniques have been proposed. Recently, the spectral decomposition method was proposed, which is an infinite version of principal components analysis. Using this method, Gaussian blurred images can be represented as polynomials with a scale parameter and a Gaussian blurred image with an arbitrary scale can be obtained from simple linear combinations of the convolved eigenimages. However, the scale is limited to a small range in this method. In this study, we propose an improvement to the spectral decomposition of a Gaussian kernel by widening the scale using a piecewise polynomial representation. We present an analysis of the continuous spectral decompositions of a Gaussian kernel and their eigensolutions. Experimental results show that the proposed method can generate accurate Gaussian blurred images with an arbitrary scale and a wide scale range.

In this study, we propose the application of principal components analysis (PCA) to scale-spaces. PCA is a standard method used in computer vision. The translation of an input image into scale-space is a continuous operation, which requires the extension of conventional finite matrix- based PCA to an infinite number of dimensions. In this study, we use spectral decomposition to resolve this infinite eigenproblem by integration and we propose an approximate solution based on polynomial equations. To clarify its eigensolutions, we apply spectral decomposition to the Gaussian scale-space and scale-normalized Laplacian of Gaussian (LoG) space. As an application of this proposed method, we introduce a method for generating Gaussian blur images and scale-normalized LoG images, where we demonstrate that the accuracy of these images can be very high when calculating an arbitrary scale using a simple linear combination. We also propose a new Scale Invariant Feature Transform (SIFT) detector as a more practical example.

This paper proposes the proportional image enlargement using hybrid methods. Hybrid method is combinations of scaling and carving methods. This method consists of two steps. The first step enlarges the source image to the same size with minimum size for height or width from the target image. In this step, we use a kernel scaling method which is resulted in proportional content image size. The second step is the full size image enlargement in the width direction. The important content in the image is maintained. The image energy is used to detect the significant part in the image. We slice the image by following the minimum energy from top to bottom. The interpolation pixel is implemented among slices of the image. We use rank-ordered mean filters to reduce jagged image, especially in the interpolation pixels. The experiments show the proportional image enlargement, and the aspect ratio of an image is changed.

In this paper, we propose the application of principal component analysis (PCA) to scale-spaces. PCA is a standard method used in computer vision tasks such as recognition of eigenfaces. Because the translation of an input image into scale-space is a continuous operation, it requires the extension of conventional finite matrix based PCA to an infinite number of dimensions. Here, we use spectral theory to resolve this infinite eigenproblem through the use of integration, and we propose an approximate solution based on polynomial equations. In order to clarify its eigensolutions, we apply spectral decomposition to gaussian scale-space. As an application of this proposed method we introduce a method for generating gaussian blur images, demonstrating that the accuracy of such an image can be made very high by using an arbitrary scale calculated through simple linear combination.

This paper proposes an image enlargement method with proportional content magnification by implementing the different scale factor for image slice region. We use window-kernel image magnification to enlarge the image slice. The enlarged image to target image size has three steps. The first step, source image is enlarged to the same size with high of the target image. The second step, the enlarged image in the first step is sliced from top to bottom following the minimum energy from the image. The third step, the image slices with non salient image content are selected to enlarge to the full-size image. The proposed method has been evaluated using peak signal-to-noise ratio (PSNR). The PSNR value on a uniform enlarged image using a scale factor equal to four has highest value compared with the comparison method. The image enlargement in the different ratio is also presented.

This paper proposes a method for single image enlargement with linear weighting techniques and kernel estimation. The aims of our technique are to reduce the distance of the pixel value too far especially for interpolation pixel. Contribution from the closest pixels to the interpolation point is unchanged meanwhile the farthest pixel contribution will be estimated. There are four pixel contributions as a determinant of the interpolation pixel value. The four pixels are placed in a 2×2 kernel matrix. Each pixel in the kernel has a weighting value. The weight value is organized in the separate places that are placed on the weighting matrix with 2×2 sizes. Weight values obtained from the linear curve based on the position of the pixel interpolation. Improving the image quality is performed only on the interpolation pixels. Experimental results show our new method can produce a better enlargement result, especially in the edge image regions compared to the comparison methods that used in this paper.

This paper presents Linear Mean-Median (LMM) filter that used to reduce impulse noise. LMM filter is a combination between Mean and Median filter. Wherein, linear value is acquired from the linearity between mean and median value. Mean and Median filter are only applied for free-noise pixel on the 3×3 windows that has been sorted from the smallest to the largest value. The mean value is obtained from the average value of all free-noise pixels without including the median pixel position. Meanwhile, median pixel is the middle position of the pixel that has been sorted. LMM uses nine sample pixels to determine a pixel for replacement a corrupted pixel. Our filter also provides the impulse noise prediction systems that serve as a facilitator to give information about noise content. If the noise is greater than 30%, the performance of LMM filter needs to be improved by an adaptive rank order mean filters. The filtering results have shown satisfactory results in terms of the quality result and the computation time process. A good image quality can be evidenced by PSNR (Peak Signal to Noise Ratio). Our methods always have higher PSNR value than the comparison methods. In addition, the speed computation time of our method is faster than the comparison method.

Template matching is the basic technique of the image processing for the object detection such as OCR(Optical Character Recognition), Face detection, and the defect inspection. The template matching can detect the XY displacement and the rotational angle of the target object from the input image captured by the camera. We have proposed the eigen template method for the template matching problem. Eigen template method can reduce the times of convolution than previous rotational template matching. However, the computation time remains large still. In this paper, we propose an efficient search algorithm using multi-stage refining process for the eigen template matching. In the experiments, we show that the proposed method can detect the target object fast with high accuracy compared with the previous methods.

This paper proposed Adaptive Manipulation Interpolation Kernel (AMIK) based on local image data. The adaptive interpolation kernel used closed loop system. The previous output result fo'(i,j) also be used as reference for the next interpolation calculation. Closed loop applied in the system, if the interpolation system performed between two pixels for horizontal or vertical directions. Furthermore, fo'(i,j) filtered by Three Mean Median Filter (TMMF). That method used to increase image quality. TMMF is the average value of three median nearest pixels in the 3×3 window size. The test results shown good results compared to the Bilinear, Bicubic, Lanczos 2, Lanczos 3 and Pyramid step. PSNR is the quantitative analysis, which used to measure image performance. Our method has the highest PSNR value in the gray and RGB image, which compared to the comparison methods.

Fuzzy Mean Linear Aliasing Window Kernel (FMLAWK) filter method proposed to reducing the high-density impulse noise interference and generating the smooth image performance. FMLAWK filter is a spatial filter, which combined from fuzzy method and Linear Aliasing Filter (LAF). The initial step is finding the degree of membership function (μ) value of each matrix element on the corrupted image which use the fuzzy method. Furthermore, the μ value of the corrupted image processed by LAF method which using 3×3 window. The reducing of 3×3 windows on LAF process will be obtain one pixel data based on Linear method. Our research also provides kernel algorithms. Preprocessing Kernel algorithm used for checking of each element matrix on the 3×3 window. If the matrix element contaminated by impulse noise, so the matrix element replaced with a new element data. Our simulation result shows the image filtering better and smoother quality than the comparison method.

This paper proposes the eigen-decomposition template matching. The eigen-decomposition template matching is a method to detect the displacement and the rotational angle accurately between the input and the template image. The proposed method calculates the correlation by matching the images given which is determined by eigen decomposition of the template image to the input image instead of matching the template image to the input image directly. The experimental results show that the proposed method can detect the displacement and rotational angle more accurately than that of the previous method.