Enis A. Cetin

Eye tracking using Markov models

We propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. In this method, a decision is made on whether the eyes are closed or not and, using a Markov chain framework to model temporal evolution, the subjects gaze is determined. The method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. We compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. A procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. The method may be used in monitoring a drivers alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction.

Detection of microcalcifications in mammograms using nonlinear subband decomposition and outlier labeling

Computer-aided diagnosis will be an important feature of the next generation picture archiving and communication systems. In this paper, computer-aided detection of microcalcifications in mammograms using a nonlinear subband decomposition and outlier labeling is examined. The mammogram image is first decomposed into subimages using a nonlinear subband decomposition filter bank. 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. A region with high positive skewness and kurtosis is marked as a region of interest. Finally, an outlier labeling method is used to find the locations of microcalcifications in these regions. Simulation studies are presented.

Fast insect damage detection in wheat kernels using transmittance images

We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a false positive rate (1-specificity) of 0.12 at the true positive rate (sensitivity) of 0.8 and an area under the ROC curve (AUC) of 0.90/spl plusmn/0.02. Combining the linear model and a radial basis function network in a committee resulted in a FP rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93/spl plusmn/0.03.

QR-RLS algorithm for error diffusion of color images

Printing color images on color printers, and displaying them on computer monitors requires a significant reduction of physically distinct colors, which causes degradation in image quality. An efficient method to improve the display quality of a quantized image is error diffusion which works by distributing the previous quantization errors to neighboring pixels exploiting the eyes averaging of colors in the neighborhood of the point of interest. This creates the illusion of more colors. A new error diffusion method is presented in which the adaptive recursive least squares (RLS) algorithm is used rather than the deterministic approaches in literature. To improve the performance, a diagonal scan is used in processing the image.

Set theoretic inverse halftoning method for grayscale and color images

Inverse halftoning is the problem of recovering a continuous- tone image from a given halftoned image. In this paper, a new inverse halftoning method which uses a set theoretic formulation is introduced. The new method exploits the prior information at hand, and uses space-domain projections, frequency-domain projections, and space-scale domain projections to obtain a feasible reconstruction of the continuous-tone image. The proposed method is also extended for the inverse halftoning of color error-diffused images.

E-Museum: Web-based Tour and Information System for Museums

A Web-based system-consisting of data entrance, access and retrieval modules-is constructed for museums. Internet users that visit the e-museum, are able to view the written and visual information, belonging to the artworks in the museum, are able to follow the virtual tour prepared for the different sections of the museum, are able to browse the artworks according to certain properties, are able to search the artworks having the similar visual content with the viewed artwork

2D adaptive prediction-based Gaussianity tests in microcalcification detection

With increasing use of Picture Archiving and Communication Systems, computer-aided diagnosis methods will be more widely utilized. In this paper, we develop a CAD method for the detection of microcalcification clusters in mammograms, which are an early sign of breast cancer. The method we propose makes use of 2D adaptive filtering and a Gaussianity test recently developed by Ojeda et al. for causal invertible time series. The first step of this test is adaptive linear prediction. It is assumed that the prediction error sequence has a Gaussian distribution as the mammogram images do not contain sharp edges. Since microcalcifications appear as isolated bright spots, the prediction error sequence contains large outliers around microcalcification locations on the second step of the algorithm is the computation of a test statistic from the prediction error values to determine whether the samples are from a Gaussian distribution. The Gaussianity test is applied over small, overlapping square regions. The regions, in which the Gaussianity test fails, are marked as suspicious regions. Experimental results obtained from a mammogram database are presented.

Wavelet domain textual coding of Ottoman script images

Image coding using wavelet transform, DCT, and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters This method efficiently compresses document images and is suitable for database research, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that are described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.

3D Forest Fire Propagation Simulation

The increase in the number of forest fires in the last few years dispatch governments to take precautions. Besides prevention, early intervention is also very important in fire fighting. If the firefighters know where the fire will be in some time, it would be easier for them to stop the fire. Therefore a big need for simulating the fire behavior exists. In this paper we are proposing a system which can simulate the propagation of fire in time. Also this system can visualize the propagation of fire in any 3D-GIS environment, that accepts KMZ as a file format. Besides, any user demanded data can be visualized on the map of the system. This gives the chance of fire planning to firefighters. The system can visualize its results on 3D screens in 3D. Therefore, a better understanding of the terrain can be obtained.

Small moving object detection using adaptive subband decomposition in video sequences

In this paper, a small moving object method detection 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 Guassian. 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.