Influence function based Gaussianity tests for detection of microcalcifications in mammogram images

In this paper, computer-aided diagnosis of microcalcifications in mammogram images is considered. Microcalcification clusters are an early sign of breast cancer. Microcalcifications appear as single bright spots in mammogram images. We propose an effective method for the detection of these abnormalities. The first step of this method is two-dimensional adaptive filtering. The filtering produces an error image which is divided into overlapping square regions. In each square region, a Gaussianity test is applied. Since microcalcifications have an impulsive appearance, they are treated as outliers. In regions with no microcalcifications, the distribution of the error image is almost Gaussian, on the other hand, in regions containing microcalcification clusters, the distribution deviates from Gaussianity. Using the theory of the influence function and sensitivity curves, we develop a Gaussianity test. Microcalcification clusters are detected using the Gaussianity test. Computer simulation studies are presented

Small moving object detection in video sequences

In this paper, we propose a method for detection of small moving objects in video. We first eliminate the camera motion using motion compensation. We then use an adaptive predictor to estimate the current pixel using neighboring pixels in the motion compensated image and, in this way, obtain a residual error image. Small moving objects appear as outliers in the residual image and are detected using a statistical Gaussianity detection test based on higher order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. Simulation examples are presented

Small moving object detection using adaptive subband decomposition in video sequences

In this paper, a small moving object method detection method in video sequences is described. In the first step, the camera motion is eliminated using motion compensation. An adaptive subband decomposition structure is then used to analyze the motion compensated image. In the 'low-high' and 'high-low' subimages small moving objects appear as outliers and they are detected using a statistical Gaussianity detection test based on higher order statistics. It turns out that in general, the distribution of the residual error image pixels is almost Gaussian. On the other hand, the distribution of the pixels in the residual image deviates from Gaussianity in the existence of outliers. Simulation examples are presented

Subband analysis for robust speech recognition in the presence of car noise

In this paper, a new set of speech feature representations for robust speech recognition in the presence of car noise are proposed. These parameters are based on subband analysis of the speech signal. Line Spectral Frequency (LSF) representation of the Linear Prediction (LP) analysis in subbands and cepstral coefficients derived from subband analysis (SUBCEP) are introduced, and the performances of the new feature representations are compared to mel scale cepstral coefficients (MELCEP) in the presence of car noise. Subband analysis based parameters are observed to be more robust than the commonly employed MELCEP representations

Automated detection and enhancement of microcalcifications in mammograms using nonlinear subband decomposition

In this paper, computer-aided detection and enhancement of microcalcifications in mammogram images are considered. The mammogram image is first decomposed into subimages using a `subband' decomposition filter bank which uses nonlinear filters. A suitably identified subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. All regions with high positive skewness and kurtosis are marked as a regions of interest. Next, an outlier labeling method is used to find the locations of microcalcifications in these regions. An enhanced mammogram image is also obtained by emphasizing the microcalcification locations. Linear and nonlinear subband decomposition structures are compared in terms of their effectiveness in finding microcalcificated regions and their computational complexity. Simulation studies based on real mammogram images are presented

Angle Perception on Autostereoscopic Displays

Previous studies of depth and distance estimation with autostereoscopic 3D displays indicate the users do not necessarily exhibit better depth perception with such displays. Yet, users claim 3D displays provide higher immersiveness. As shapes of objects are often described by their angles, it is expected that angle perception would contribute to object recognition and scene understanding. However; angle perception studies on stereoscopic displays are not available. Angle estimation accuracy with autostereoscopic 3D displays is investigated in this study

Angle perception on autostereoscopic displays

Previous studies of depth and distance estimation with autostereoscopic 3D displays indicate the users do not necessarily exhibit better depth perception with such displays. Yet, users claim 3D displays provide higher immersiveness. As shapes of objects are often described by their angles, it is expected that angle perception would contribute to object recognition and scene understanding. However; angle perception studies on stereoscopic displays are not available. Angle estimation accuracy with autostereoscopic 3D displays is investigated in this study

Corner validation based on extracted corner properties

We developed a method to validate and filter a large set Of previously obtained corner points. We derived the necessary relationships between image derivatives and estimates of corner angle, orientation and contrast. Commonly Used cornerness measures of the auto-correlation matrix estimates of image derivatives are expressed in terms of these estimated corner properties. A candidate corner is validated if the cornerness score directly obtained from the image is sufficiently close to the cornerness score for ail ideal corner with the estimated orientation, angle and contrast. We tested this algorithm oil both real and synthetic images and observed that this procedure significantly improves the corner detection rates based oil human evaluations. We tested the accuracy Of our corner property estimates under various noise conditions. Extracted corner properties call also be used for tasks like feature point matching, object recognition and pose estimation. (c) 2008 Elsevier Inc. All rights reserved

Selecting image corner points using their corner properties

We developed a method to obtain corner points for healthier point matching using corner properties such as corner angle, corner orientation and contrast. A large corner point set, obtained by a common corner detector (Harris, Tomasi-Kanade etc.) is given to our algorithm as input. Then, the corner properties are extracted for this point set in terns of image derivatives. Cornerness measure calculated from the image is compared with the one calculated using an ideal corner with the extracted properties. If they are close enough, which shows that the neighborhood of the point possess corner properties and estimations are successful, the corner is selected. It is presumed that corners selected by this method are more suitable for point matching. Moreover, extracted corner properties can be used as a priori information for matching