Shin-Min Chao

An improved anisotropic diffusion model for detail- and edge-preserving smoothing

It is important in image restoration to remove noise while preserving meaningful details such as blurred thin edges and low-contrast fine features. The existing edge-preserving smoothing methods may inevitably take fine details as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. The classical anisotropic diffusion models consider only the gradient information of a diffused pixel, and cannot preserve detailed features with low gradient. Since the fine details in the neighborhood of the image generally have larger gray-level variance than the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results from a variety of test samples including shoulder patch images, medical images and artwork images have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the restored image.

Independent component analysis based filter design for defect detection in low-contrast textured images

In this paper, we propose a convolution filtering scheme for detecting defects in low-contrast textured surface images and, especially, focus on the application for glass substrates in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may present uneven brightness on the surface. All these make the defect detection in low-contrast surface images extremely difficult. In this study, a constrained ICA (independent component analysis) model is proposed to design an optimal filter with the objective that the convolution filter will generate the most representative source intensity of the background surface without noise. The prior constraint incorporated in the ICA model confines the source values of all training image patches of a defect-free image within a small interval of control limits. In the inspection process, the same control parameter used in the constraint is also applied to set up the thresholds that make impulse responses of all pixels in faultless regions within the control limits, and those in defective regions outside the control limits. A stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to solve for the constrained ICA model. Experimental results have shown that the proposed method can effectively detect defects in textured LCD glass substrate images

Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion

This paper proposes a machine vision scheme for detecting micro-crack defects in solar wafer manufacturing. The surface of a polycrystalline silicon wafer shows heterogeneous textures, and the shape of a micro-crack is similar to the multi-grain background. They make the automated visual inspection task extremely difficult. The low gray-level and high gradient are two main characteristics of a micro-crack in the sensed image with front-light illumination. An anisotropic diffusion scheme is proposed to detect the subtle defects. The proposed diffusion model takes both gray-level and gradient as features to adjust the diffusion coefficients. It acts as an adaptive smoothing process. Only the pixels with both low gray-levels and high gradients will generate high diffusion coefficients. It then smoothes the suspected defect region and preserves the original gray-levels of the faultless background. By subtracting the diffused image from the original image, the micro-crack can be distinctly enhanced in the difference image. A simple binary thresholding, followed by morphological operations, can then easily segment the micro-crack. The proposed method has shown its effectiveness and efficiency for a test set of more than 100 wafer images. It has also achieved a fast computation of 0.09s for a 640x480 image.

An anisotropic diffusion-based defect detection for low-contrast glass substrates

In this paper, we propose an anisotropic diffusion scheme to detect defects in low-contrast surface images and, especially, aim at glass substrates used in TFT-LCDs (Thin Film Transistor-Liquid Crystal Displays). In a sensed image of glass substrate, the gray levels of defects and background are hardly distinguishable and result in a low-contrast image. Therefore, thresholding and edge detection techniques cannot be applied to detect subtle defects in the glass substrates surface. Although the traditional diffusion model can effectively smooth noise and irregularity of a faultless background in an image, it can only passively stop the diffusion process to preserve the original low-contrast gray values of defect edges. The proposed diffusion method in this paper can simultaneously carry out the smoothing and sharpening operations so that a simple thresholding can be used to segment the intensified defects in the resulting image. The method adaptively triggers the smoothing process in faultless areas to make the background uniform, and performs the sharpening process in defective areas to enhance anomalies. Experimental results from a number of glass substrate samples including backlight panels and LCD glass substrates have shown the efficacy of the proposed diffusion scheme in low-contrast surface inspection.

An anisotropic diffusion-based defect detection for sputtered surfaces with inhomogeneous textures

Abstract Texture analysis techniques are being increasingly used for surface inspection, in which small defects that appear as local anomalies in textured surfaces must be detected. Traditional surface inspection methods mainly focus on homogeneous textures that contain periodical, repetitive patterns. In this paper, we study defect detection in sputtered glass substrates that involve inhomogeneous textures. Such sputtered surfaces can be found in touch panels and LCDs. An anisotropic diffusion scheme is proposed to detect subtle defects embedded in inhomogeneous textures. The proposed anisotropic diffusion model takes a non-negative decreasing function with an annealing gradient threshold as the diffusion coefficient to adaptively adjust the significance of edge gradients. It triggers the smoothing process in faultless areas for background texture removal by assigning a large diffusion coefficient value, and stops the diffusion process in defective areas to preserve sharp edges of anomalies by assigning a small diffusion coefficient value. Experimental results from a number of sputtered glass samples have shown the effectiveness of the proposed anisotropic diffusion scheme.

Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect, because the intensity difference between the unevenly illuminated background and the defective region is hardly observable and no clear edges are present between the defect and its surroundings. The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in various low-contrast surface images.

Anisotropic diffusion-based detail-preserving smoothing for image restoration

It is important in image restoration to remove noise while preserving meaningful details such as edges and fine features. The existing edge-preserving smoothing methods may inevitably take fine detail as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. Since the fine detail in the neighborhood of a small image window generally have a gray-level variance larger than that of the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the restored image. The proposed method has the best restoration result compared with other edge-preserving methods.

A Generalized Anisotropic Diffusion for Defect Detection in Low-Contrast Surfaces

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect because the intensity difference between unevenly-illuminated background and defective regions are hardly observable. The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in low-contrast surface images.

Defect detection in low-contrast glass substrates using anisotropic diffusion

In this research, we propose an anisotropic diffusion scheme to detect defects in low-contrast surface images and, especially, aim at glass substrates used in TFT-LCDs (thin film transistor-liquid crystal displays). In a sensed glass substrate, the gray levels of defects and background are hardly distinguishable and result in a low-contrast image. Therefore, thresholding and edge detection techniques cannot be applied to detect subtle defects in the glass substrates surface. The proposed diffusion method in this paper can simultaneously carry out the smoothing and sharpening operations. It adaptively triggers the smoothing process in faultless areas to make the background uniform, and performs the sharpening process in defective areas to enhance anomalies. Experimental results from a number of glass substrate samples including backlight panels and LCD glass substrates have shown the efficacy of the proposed diffusion scheme in low-contrast surface inspection

A Real-time Mobile System for Fetal Heart Rate Monitoring and Fetal Distress Detection

This paper proposes a mobile system for real-time fetal heart rate (FHR) monitoring and fetal distress detection. In the proposed system, FHR values are collected using machine vision techniques and analyzed using a pattern matching approach. When a fetal distress is detected, a medical alarm will automatically notify medical experts through a mobile GSM network. Through the proposed system, not only a pregnant woman can track the healthy status of her baby, but also medical experts can allocate medical resources in time. The implementation result shows that the developed mobile system can be a helpful application for medical management.