Three different image-processing methods based on the time-averaged technique were compared by the electronic speckle pattern interferometry (ESPI) technique for vibration measurement. The three methods are the video-signal-addition method, the video-signal-subtraction method, and the amplitude-fluctuation method. Also, errors introduced by using the zero-order Bessel function directly into the analysis of the fringe pattern were investigated. The video-signal-addition method has been the most generally used ESPI technique for vibration measurement. However, without additional image and/or signal-processing procedures, the fringe pattern obtained directly by the video-signal-addition method is rather difficult to observe. The reason for poor visibility of the experimentally obtained fringe pattern with this method is explained. To increase the fringe pattern's visibility without additional image and/or signal processes, we tried two video-signal-subtraction methods. One of the two methods is the video-signal-subtraction method that has normally been used in the static applications. The other method, called the amplitude-fluctuation method, and its associated theory are reported here.

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In the paper, we propose a Bayesian classifier which exploits nonparametric model to identify the gender from the facial images. Our major contribution is that we use feature patch-based non-parametric method to generate the posteriori of male and female based on the characteristics of the labeled training image patches. Our system consists of four modules. First, we use AAM model to identify facial feature points. Facial images are represented by the overlapping feature patches around the feature points. Second, from the labeled training patches, we select a smaller subset as the patch library based on the K means clustering. Third, in training, we embed the gender characteristics of the training feature patches as the posteriori of the library patches. Fourth, in testing, we integrate the posterior of the test patches to determine the gender. The experimental results demonstrate that our proposed method is better than the conventional non-feature-patch-based methods.

Purpose: This work applies Lowe's Scale Invariant Feature Transform (SIFT) to detect micro‐calcification on mammograms. The objective of this study is to expand the function of SIFT, which has originally been used to match objects by matching the detected feature points, and to provide a new method for micro‐calcification detection. Methods: First, variables in SIFT, the scaling factor between levels of the image, the radiuses of the areas for maximum comparison within current scale and neighboring scales, and the threshold value for maximum search, were adjusted to allow nearly all the micro‐calcification to be detected as the feature points. Second, to reject feature points which are not micro‐calcification, four features of sixty‐five feature points, determined by physicians as micro‐calcification, curvature of scale space, elements of Hessian matrix, used for the discrimination of prominence and shapes, and Contrast to neighboring pixels, Size on image used to reject points on blob‐like dense tissue, were analyzed to determine the specific ranges for selecting feature points on micro‐calcification. Results: Ninety region of interest (ROI) images (268 × 268 pixel) selected from 85 mammograms (3,540 × 4,740 pixel) were employed to test the proposed method. Of the 90 ROI images, 30 images were biopsy‐verified by a physician to present a cluster of micro‐calcification. The other 60 ROI images are selected from normal mammograms. The performance of the study is evaluated by a receiver operating characteristic curve (ROC). An area under the ROC curve of 94.2%, sensitivity of 93.3%, and specificity of 95% was achieved. Conclusion: The proposed system based on SIFT accurately detects micro‐calcification in mammograms with various brightness, size, and breast density, without preprocessing them. Our findings merit further investigation for its potential to classify benign and malignant calcification.