LCD Automatic Vision Inspection (AVI) systems automatically detect defect features and measure their sizes via camera vision. AVI systems usually report different measurements on the same defect with some variations on position or rotation mainly because we get different images. This is caused by possible variations in the image acquisition process including optical factors, non-uniform illumination, random noise, and so on. For this reason, conventional area based defect measuring method has some problems in terms of robustness and consistency. In this paper, we propose a new defect size measuring method to overcome these problems. We utilize volume information which is completely ignored in the area based conventional defect measuring method. We choose a bell shape as a defect model for experiment. The results show that our proposed method dramatically improves robustness of defect size measurement. Given proper modeling, the proposed volume based measuring method can be applied to various types of defect for better robustness and consistency.

We propose a filtering method for optimal estimation of TFT-LCD's surface region except defect's region. To estimate the non-uniform intensity variation on TFT-LCD surface region, the 4-directional Gaussian filter based on image pyramid structure is proposed. The experimental result verified the proposed method's performance

This paper proposed an automated segmentation algorithm for MR brain images through the complementary use of T1-weighted, T2-weighted, and PD images. The proposed segmentation algorithm is composed of 3 steps. The first step involves the extraction of cerebrum images by placing a cerebrum mask over the three input images. In the second step, outstanding clusters that represent the inner tissues of the cerebrum are chosen from among the 3-dimensional (3D) clusters. The 3D clusters are determined by intersecting densely distributed parts of a 2D histogram in 3D space formed using three optimal scale images. The optimal scale image results from applying scale-space filtering to each 2D histogram and a searching graph structure. As a result, the optimal scale image can accurately describe the shape of the densely distributed pixel parts in the 2D histogram. In the final step, the cerebrum images are segmented by the FCM (Fuzzy c-means) algorithm using the outstanding cluster center value as the initial center value. The ability of the proposed segmentation algorithm to calculate the cluster center value accurately then compensates for the current limitation of the FCM algorithm, which is unduly restricted by the initial center value used. In addition, the proposed algorithm, which includes a multi spectral analysis, can achieve better segmentation results than a single spectral analysis.

The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.

Gamma correction is an essential function and is time consuming task in every display device such as CRT and LCD. And gray scale CCT reproduction in most LCD are quite different from those of standard CRT. An automated fast and accurate display adjusment method and system for gamma correction and for constant gray scale CCT calibration of mobile phone LCD is presented in this paper. We develop the test pattern disply and register control program in mobile phone and devleop automatic measure program in computer using spectroradimeter. The proposed system is maintain given gamma values and CCT values accuratly. In addition, This system is possible to fast mobile phone LCD adjusment within one hour.

This paper proposes an automatic system which can perform the entire diagnostic process from the extraction of the liver to the recognition of a tumor. In particular, the proposed technique uses shape information to identify and recognize a lesion adjacent to the border of the liver, which can otherwise be missed. Because such an area is concave like a bay, morphological operations can be used to find the bay. In addition, since the intensity of a lesion can vary greatly according to the patient and the slice taken, a decision on the threshold for extraction is not easy. Accordingly, the proposed method extracts the lesion by means of a Fuzzy c-Means clustering technique, which can determine the threshold regardless of a changing intensity. Furthermore, in order to decrease any erroneous diagnoses, the proposed system performs a 3-D consistency check based on three-dimensional information that a lesion mass cannot appear in a single slice independently. Based on experimental results, these processes produced a high recognition rate above 91%.

The thin film transistor liquid crystal display (TFT-LCD) image has nonuniform brightness, which is a major difficulty in finding the Mura defect region. To facilitate Mura segmentation, globally widely varying background signal must be flattened and then Mura signal must be enhanced. In this paper, Mura signal enhancement and background-signal-flattening methods using wavelet coefficient processing are proposed. The wavelet approximation coefficients are used for background-signal flattening, while wavelet detail coefficients are employed to magnify the Mura signal on the basis of an adapted contrast sensitivity function (CSF). Then, for the enhanced image, trimodal thresholding segmentation technique and a false-region elimination method based on the human visual system (HVS) are employed for reliable Mura segmentation. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding Muras in a TFT-LCD image.