Yasemin Yardimci

Detection of microcalcifications in mammograms using higher order statistics

A new method for detecting microcalcifications in mammograms is described. In this method, the mammogram image is first processed by a subband decomposition filterbank. The bandpass subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. The detection method utilizes these two parameters. A region with high positive skewness and kurtosis is marked as a region of interest. Simulation results show that this method is successful in detecting regions with microcalcifications.

Robust direction-of-arrival estimation in non-Gaussian noise

A nonlinearly weighted least-squares method is developed for robust modeling of sensor array data, weighting functions for various observation noise scenarios are determined using maximum likelihood estimation theory. The computational complexity of the new method is comparable with the standard least-squares estimation procedures. Simulation examples of direction-of-arrival estimation are presented.

Computer vision based mouse

We describe a computer vision based mouse, which can control and command the cursor of a computer or a computerized system using a camera. In order to move the cursor on the computer screen the user simply moves the mouse shaped passive device placed on a surface within the viewing area of the camera. The video generated by the camera is analyzed using computer vision techniques and the computer moves the cursor according to mouse movements. The computer vision based mouse has regions corresponding to buttons for clicking. To click a button the user simply covers one of these regions with his/her finger.

Registration of multispectral satellite images with Orientation-Restricted SIFT

In this study, a modified version of the popular SIFT algorithm is introduced. The algorithm is robust to nonlinear intensity changes between images which makes it a strong candidate for multispectral image registration. The proposed Orientation Restricted SIFT algorithm combines the SIFT descriptor vector elements in opposite directions. In this way, better feature matching performance is achieved with shorter descriptor vectors which translate into shorter matching complexity. The effect of scale restriction and contrast stretching is demonstrated on multispectral satellite images and scale restriction is found to be useful in eliminating incorrectly matched features.

Detection of underdeveloped hazelnuts from fully developed nuts by impact acoustics

Shell-to-kernel weight ratio is a vital measurement of quality in hazelnuts as it helps to identify nuts that have underdeveloped kernels. Nuts containing underdeveloped kernels may contain mycotoxin-producing molds, which are linked to cancer and are heavily regulated in international trade. A prototype system was set up to detect underdeveloped hazelnuts by dropping them onto a steel plate and recording the acoustic signal that was generated when a kernel hit the plate. A feature vector comprising line spectral frequencies and time-domain maxima that describes both the time and frequency nature of the impact sound was extracted from each sound signal and used to classify each nut by a support-vector machine. Experimental studies demonstrated accuracies as high as 97% in classifying hazelnuts with underdeveloped kernels.

Small moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences

In this paper, a small moving object method detection method in video sequence 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 highband subimages moving objects appear as outliers and they are detected using a statistical detection test based on lower 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. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented.

Adaptive methods for dithering color images

Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that exploit the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. In this paper an adaptive error diffusion method is presented. The error diffusion filter coefficients are updated by a normalized LMS type algorithm to prevent textural contours, color impulses and color shifts which are among the the most common side effects of the standard dithering algorithms.

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 an ideal corner with the estimated orientation, angle and contrast. We tested this algorithm on both real and synthetic images and observed that this procedure significantly improves the corner detection rates based on human evaluations. We tested the accuracy of our corner property estimates under various noise conditions. Extracted corner properties can also be used for tasks like feature point matching, object recognition and pose estimation.

Classification of hazelnut kernels by using impact acoustic time-frequency patterns

Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds (Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment, the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step, the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis (LDA) classifier. The algorithm achieved a throughput rate of 45 nuts/s and a classification accuracy of 96% with the 30 most discriminative features, a higher rate than those provided with prior methods.

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.