Browsing by Subject "Motion compensation"

Now showing 1 - 12 of 12

Open Access
Detection of microcalcifications in mammograms using nonlinear subband decomposition and outlier labeling
(SPIE, 1997-02) Gürcan, M. Nafi; Yardımcı, Yasemin C.; Çetin, A. Enis; Ansari, R.
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.
Open Access
A fast algorithm for subpixel accuracy image stabilization for digital film and video
(SPIE, 1998) Eroğlu, Çiğdem; Erdem, A. T.
This paper introduces a novel method for subpixel accuracy stabilization of unsteady digital films and video sequences. The proposed method offers a near-closed-form solution to the estimation of the global subpixel displacement between two frames, that causes the misregistration of them. The criterion function used is the mean-squared error over the displaced frames, in which image intensities at subpixel locations are evaluated using bilinear interpolation. The proposed algorithm is both faster and more accurate than the search-based solutions found in the literature. Experimental results demonstrate the superiority of the proposed method to the spatio-temporal differentiation and surface fitting algorithms, as well. Furthermore, the proposed algorithm is designed so that it is insensitive to frame-to-frame intensity variations. It is also possible to estimate any affine motion between two frames by applying the proposed algorithm on three non-collinear points in the unsteady frame.
Open Access
Key frame selection from MPEG video data
(SPIE, 1997-02) Gerek, Ömer. N.; Altunbaşak, Y.
This paper describes a method for selecting key frames by using a number of parameters extracted from the MPEG video stream. The parameters are directly extracted from the compressed video stream without decompression. A combination of these parameters are then used in a rule based decision system. The computational complexity for extracting the parameters and for key frame decision rule is very small. As a results, the overall operation is very quickly performed and this makes our algorithm handy for practical purposes. The experimental results show that this method can select the distinctive frames of video streams successfully.
Open Access
Mean-shift tracking of moving objects using multi-dimensional histograms
(Society of Photo-Optical Instrumentation Engineers (SPIE), 2004-04) Cüce, Halil I.; Çetin, A. Enis
In this paper, a moving object tracking algorithm for infrared image sequences is presented. The tracking algorithm is based on the mean-shift tracking method which is based on comparing the histograms of moving objects in consecutive image frames. In video obtained after visible light, the color histogram of the object is used for tracking. In forward looking infrared image sequences, the histogram is constructed not only from the pixel values but also from a highpass filtered version of the original image. The reason behind the use of highpass filter outputs in histogram construction is to capture structural nature of the moving object. Simulation examples are presented.
Open Access
Moving object detection in video by detecting non-Gaussian regions in subbands and active contours
(IEEE, 2003-09) Gök, M. Y.; Çetin, A. Enis
A multi-stage moving object detection algorithm in video is described in this paper. First, the camera motion is eliminated by motion compensation. An adaptive subband decomposition structure is then used to analyze the difference image. In the high-band subimages, moving objects which produce outliers are detected using a statistical test determining non-Gaussian regions. It turns out that the distribution of the subimage pixels is almost Gaussian in general. But, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. Regions containing moving objects in the original image frame are detected by detecting regions containing outliers in subimages. Finally, active contours are initiated in these regions in the wavelet domain and object boundaries are accurately estimated.
Open Access
Moving object detection using adaptive subband decomposition and fractional lower-order statistics in video sequences
(Elsevier, 2002) Bagci, A. M.; Yardimci, Y.; Çetin, A. Enis
In this paper, a moving object 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 moving objects appear as outliers and they are detected using a statistical detection test based on fractional lower-order statistics. It turns out that the distribution of the subimage pixels is almost Gaussian in general. On the other hand, at the object boundaries the distribution of the pixels in the subimages deviates from Gaussianity due to the existence of outliers. By detecting the regions containing outliers the boundaries of the moving objects are estimated. Simulation examples are presented. © 2002 Elsevier Science B.V. All rights reserved.
Open Access
A particle swarm optimization based SAR motion compensation algorithm for target image reconstruction
(IEEE, 2010) Uğur, Salih; Arıkan, Orhan
A new SAR motion compensation algorithm is proposed for robust reconstruction of target images even under large deviations of the platform from intended flight path. Phase error due to flight path deviations is estimated as a solution to an optimization problem in terms of the positions of the reflectivity centers of the target. Particle swarm optimization is used to obtain phase error estimates efficiently. The quality of the reconstructions is demonstrated by using simulation studies. © 2010 IEEE.
Open Access
SAR image reconstruction by expectation maximization based matching pursuit
(Academic Press, 2015) Ugur, S.; Arıkan, Orhan; Gürbüz, A. C.
Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching Pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation.
Open Access
Segmentation-based extraction of important objects from video for object-based indexing
(IEEE, 2008-06) Baştan, Muhammet; Güdükbay, Uğur; Ulusoy, Özgür
We describe a method to automatically extract important video objects for object-based indexing. Most of the existing salient object detection approaches detect visually conspicuous structures in images, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of regional and inter-regional color, shape, texture and motion features for all regions, which are classified as being important or not using SVMs trained on a few hundreds of example regions. Finally, each important region is tracked within each shot for trajectory generation and consistency check. Experimental results from news video sequences show that the proposed approach is effective. © 2008 IEEE.
Open Access
Small moving object detection in video sequences
(IEEE, 2000-06) Zaibi, Rabi; Çetin, A. Enis; Yardımcı, Y.
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.
Open Access
Small moving object detection using adaptive subband decomposition and fractional lower order statistics in video sequences
(2001-07-08) Bağci, A.Murat; Yardımcı, Y.; Çetin, A. Enis
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 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.
Open Access
Three-dimensional motion and dense-structure estimation using convex projections
(SPIE, 1997-02) Alatan, A. Aydın; Erdem, A. Tanju; Onural, Levent
We propose a novel method for estimating the 3D motion and dense structure of an object form its two 2D images. The proposed method is an iterative algorithm based on the theory of projections onto convex sets (POCS) that involves successive projections onto closed convex constraint sets. We seek a solution for the 3D motion and structure information that satisfies the following constraints: (i) rigid motion - the 3D motion parameters are the same for each point on the object. (ii) Smoothness of the structure - depth values of the neighboring points on the object vary smoothly. (iii) Temporal correspondence - the intensities in the given 2D images match under the 3D motion and structure parameters. We mathematically derive the projection operators onto these sets and discuss the convergence properties of successive projections. Experimental results show that the proposed method significantly improves the initial motion and structure estimates.