Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

Defect Detection in Electronic Surfaces Using Template-Based Fourier Image Reconstruction

Abstract: For defect detection in nonperiodical pattern images, such as printed circuit boards or integrated circuit dies found in the electronic industry, template matching could be the only applicable method to tackle the problem. The traditional template matching techniques work in the spatial domain and rely on the local pixel information. They are sensitive to geometric and lighting changes, and random product variations. The currently available Fourier-based methods mainly work for plain and periodical texture surfaces. In this paper, we propose a global Fourier image reconstruction method to detect and localize small defects in nonperiodical pattern images. It is based on the comparison of the whole Fourier spectra between the template and the inspection image. It retains only the frequency components associated with the local spatial anomaly. The inverse Fourier transform is then applied to reconstruct the test image, where the local anomaly will be restored and the common pattern will be removed as a uniform surface. The proposed method is invariant to translation and illumination, and can detect subtle defects as small as 1-pixel wide in a wide variety of nonperiodical patterns found in the electronic industry.

A hybrid fractal transform

Abstract: A generalization of fractal coding of images is presented in which image blocks are represented by mappings derived from least squares approximations using fractal functions. Previously known matching techniques used in fractal transforms are subjects of this generalized method, which is called the Bath fractal transform (BFT). By introducing searching for the best image region for application of the BFT, a hybrid of known methods is achieved. Their fidelity is evaluated by a root-mean-square error measure for a number of polynomial instances of the BFT, over a range of searching levels using a standard test image. It is shown that the fidelity of the fractal transform increases with both search level and order of the polynomial approximation. The method readily extends to data of higher or lower dimensions, including time as an image sequences. >

Digital image compression

Abstract: A method of reconstructing an image, where the input image data is preferably part I or part II compliant JPEG2000 coded data, or pixel data of the original image. The method selects ( 810 ) an output resolution R, and then determines a number of sub-passes to extract from each block code based on the selected resolution. The method then extracts ( 830 ) the determined sub-passes and the remaining sub-passes are discarded. The method then reconstructs 840 the image from the extracted sub-passes. The reconstructed image can be in the form of the selected resolution of the original image, or it can be in the form of compressed image data of the selected resolution of the original image.

Image simulation method

Abstract: Simultaneously and automatically with creating a CG image by computer graphics, (1) the image is segmented to create a mask image, (2) a mask attribute table is created by data transformation, (3) an intrinsic image (object color component, light source color component, ambient color component) is created by data transformation, and the results are saved. The mask image is used to cut a portion from the computer graphics image and the cut portion is combined with a different image, which may be a natural image for example, to obtain a composite image, and at the same time, transparency processing or other such image processing is conducted on the basis of the attribute data. In addition, the color etc. of the object are modified by changing the object color vector or the like.

Face recognition system

Abstract: The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.