Conventional algorithms for track association (termed "correlation" by convention) employ algorithms which are applied to all sensor tracks at a specific time. The overall value of sensor networks for data f...
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ISBN:
(纸本)9780819471604
Conventional algorithms for track association (termed "correlation" by convention) employ algorithms which are applied to all sensor tracks at a specific time. The overall value of sensor networks for data fusion is closely tied to the reliability of correct association of common objects tracked by the sensors. Multisensor architectures consisting of gaps in target coverage requires that tracks must be propagated substantially forward or backward to a common time for correlation. This naturally gives rise to the question: at which time should track correlation be performed? In the conventional approach, a two-sensor correlation problem would be solved by propagating the first sensor's tracks forward to the update time (current time) of the tracks from the second sensor. We question this approach by showing simulation results that indicate that the current time can be the worst time to correlate. In addition, a methodology for calculating the approximate optimal correlation time for linear-Gaussian tracking problems is provided.
Intravascular Ultrasound (IVUS) palpography is a techniques that depicts the distribution of the mechanical strain over the luminal surface of coronary arteries. It utilizes conventional radiofrequency (RF) signals ac...
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ISBN:
(纸本)9780819471048
Intravascular Ultrasound (IVUS) palpography is a techniques that depicts the distribution of the mechanical strain over the luminal surface of coronary arteries. It utilizes conventional radiofrequency (RF) signals acquired at two different levels of a compressional load. The signals are cross-correlated to obtain the microscopic tissue displacements, which can be directly translated into local strain of the vessel wall. However, (apparent) tissue motion and nonuniform deformation of the vessel wall due to catheter jolting and rotation reduce signal correlation and result in void strain estimates. Implications of probe motion were studied on the tissue-mimicking phantom. The measured circumferential tissue displacement and level of the speckle decorrelation amounted to 12 degrees and 0.58 for the catheter displacement of 800 mu m, respectively. To compensate for the motion artifacts in IVUS palpography, a novel method, based on the feature-based scale-space Optical Flow (OF) was employed. The computed OF vector field quantifies the amount of the local tissue misalignment in consecutive frames. Subsequently, the extracted motion pattern is used to realign the signals prior to the cross-correlation analysis, reducing signal decorrelation and increasing the number of valid strain estimates. The advantage of applying the motion compensation algorithms was demonstrated in a mid-scale validation study on 14 in-vivo pullbacks. Both methods substantially increase the number of valid strain estimates in the partial and compounded palpograms. A mean relative improvement amounts to 28% and 14%, respectively. Implementation of motion compensation method increase the diagnostic value of IVUS palpography.
OCRopus is a new, open source OCR system emphasizing modularity, easy extensibility, and reuse, aimed at both the research community and large scale commercial document conversions. This paper describes the current st...
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ISBN:
(纸本)9780819469878
OCRopus is a new, open source OCR system emphasizing modularity, easy extensibility, and reuse, aimed at both the research community and large scale commercial document conversions. This paper describes the current status of the system, its general architecture, as well as the major algorithms currently being used for layout analysis and text line recognition.
In this paper, we study how specific design principles and elements of steganographic schemes for the JPEG format influence their security. Our goal is to shed some light on how the choice of the embedding operation a...
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ISBN:
(纸本)9780819469915
In this paper, we study how specific design principles and elements of steganographic schemes for the JPEG format influence their security. Our goal is to shed some light on how the choice of the embedding operation and domain, adaptive selection channels, and syndrome coding influence statistical detectability. In the experimental part of this paper, the detectability is evaluated using a state-of-the-art blind steganalyzer and the results are contrasted with several adhoc detectability measures, such as the embedding distortion. We also report the first results of our steganalysis of the recently proposed YASS algorithm and compare its security to-other steganographic methods for the JPEG format.
We consider the problem of improving contour detection by filling gaps between collinear contour pieces. A fast algorithm is proposed which takes into account local edge orientation and local curvature. Each edge poin...
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ISBN:
(纸本)9780819469847
We consider the problem of improving contour detection by filling gaps between collinear contour pieces. A fast algorithm is proposed which takes into account local edge orientation and local curvature. Each edge point is replaced by a curved elongated patch, whose orientation and curvature match the local edge orientation and edge. The proposed contour completion algorithm is integrated in a multiresolution framework for contour detection. Experimental results show the superiority of the proposed method to other well-established approaches.
There exists a strong need to reconstruct computed tomographic (CT) images with practically useful quality from a small number of projections in image-guided radiation therapy: for lowering radiation dose delivered to...
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ISBN:
(纸本)9780819470973
There exists a strong need to reconstruct computed tomographic (CT) images with practically useful quality from a small number of projections in image-guided radiation therapy: for lowering radiation dose delivered to the subject, for shortening the imaging time, and for reducing the imaging-configuration complexity. We have recently developed an iterative image reconstruction algorithm based on total-variation (TV) minimization from incomplete projection data in CT. In numerical studies with a variety of incomplete projection-data sets including truncated data, reduced scan range, and sparse sampling, the developed algorithm seems to yield reasonable reconstruction, as compared to some of the existing algorithms, such as algebraic reconstruction technique (ART) and expectation minimization (EM). The TV-based algorithm begins in general with a uniform image as an initial guess, and goes through iteration steps to minimize the image TV subject to satisfying the given incomplete projection data. In image-guided radiation therapy (IGRT), a patient usually undergoes CT scanning for treatment. planning, which can provide the reference image for image guidance. Therefore, we propose a TV-based algorithm with a priori information in few-view CT for IGRT, in an attempt to further reduce the number of projections needed for image reconstruction from what the TV-based algorithm uses when no a priori information is included. In this work, we report the initial results of a preliminary numerical study that we have conducted to demonstrate this approach.
Many traditional methods produce classification results by processing one image frame at a time. For instance, conventional correlation filters are designed to yield well defined correlation peaks when a pattern or ob...
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ISBN:
(纸本)9780819471581
Many traditional methods produce classification results by processing one image frame at a time. For instance, conventional correlation filters are designed to yield well defined correlation peaks when a pattern or object of interest is present in the input image. However, the decision process is memory-less, and does not take advantage of the history of results on previous frames in a sequence. Recently, Kerekes and Kumar introduced a new Bayesian approach for multi-frame correlation that first produces an estimate of the object's location based on previous results, and then builds up the hypothesis using both the current data as well as the historical estimate. A motion model is used as part of this estimation process to predict the probability of the object at a particular location. Since the movement and behavior of objects can change with time, it may be disadvantageous to use a fixed motion model. In this paper, we show that it is possible to let the motion model vary over time, and adaptively update it based on data. Preliminary analysis shows that the adaptive multi-frame approach has the potential for yielding significant performance improvements over the conventional approach based on individual frames.
In this paper, we present shift-invariant filtered backprojection (FBP) cone-beam image reconstruction algorithms for a cone-beam CT system based on a clinical C-arm gantry. The source trajectory consists of two conce...
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ISBN:
(纸本)9780819470973
In this paper, we present shift-invariant filtered backprojection (FBP) cone-beam image reconstruction algorithms for a cone-beam CT system based on a clinical C-arm gantry. The source trajectory consists of two concentric arcs which is complete in the sense that the Tuy data sufficiency condition is satisfied. This scanning geometry is referred to here as a CC geometry (each are is shaped like the letter "C"). The challenge for image reconstruction for the CC geometry is that the image volume is not well populated by the familiar doubly measured (DM) lines. Thus, the well-known DM-line based image reconstruction schemes are not appropriate for the CC geometry. Our starting point is a general reconstruction formula developed by Pack and Noo which is not dependent on the existence of DM-lines. For a specific scanning geometry, the filtering lines must be carefully selected to satisfy the Pack-Noo condition for mathematically exact reconstruction. The new points in this paper are summarized here. (1) A mathematically exact cone-beam reconstruction algorithm was formulated for the CC geometry by utilizing the Pack-Noo image reconstruction scheme. One drawback of the developed exact algorithm is that it does not solve the long-object problem. (2) We developed an approximate image reconstruction algorithm by deforming the filtering lines so that the long object problem is solved while the reconstruction accuracy is maintained. (3) In addition to numerical phantom experiments to validate the developed image reconstruction algorithms, we also validate our algorithms using physical phantom experiments on a clinical C-arm system.
This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in feature-based tracking of objects for surveillance applications. The proposed algorithm detects sudden variati...
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ISBN:
(纸本)9780819466211
This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in feature-based tracking of objects for surveillance applications. The proposed algorithm detects sudden variations of spatio-temporal features of objects in order to identify possible occlusion or split events. The detection is followed by a validation stage that uses past tracking information to prevent false detection of occlusion or split. Special care is taken in case of heavy occlusion, when there is a large superposition of objects. In this case the system relies on long-term 'temporal behavior of objects to avoid updating the video object features with unreliable (e.g. shape and motion) information. Occlusion is corrected by separating occluded objects. For the detection of splits, in addition to the analysis of spatio-temporal changes in objects features, our algorithm analyzes the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects, such as in the deposit of an object. Split is corrected by physically merging the objects detected to be split. To validate the proposed approach, objective and visual results are presented. Experimental results show the ability of the proposed algorithm to detect and correct, both, split and occlusion of objects. The proposed algorithm is most suitable in video surveillance applications due to: its good performance in multiple, heavy, and total occlusion;its distinction between real object separation and faulty object split;its handling of simultaneous occlusion and split events;and its low computational complexity.
We deal with video shot-cut detection in digital videos using the singular-value decomposition (SVD). SVD is performed on a matrix whose columns are the 3D frame color histograms. We have used SVD for its capabilities...
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We deal with video shot-cut detection in digital videos using the singular-value decomposition (SVD). SVD is performed on a matrix whose columns are the 3D frame color histograms. We have used SVD for its capabilities to derive a refined low-dimensional feature space from the high-dimensional raw feature space, where similar video patterns are placed together and can be easily clustered. After SVD is performed, a two-phase process is employed to detect the shots. In the first phase, a dynamic clustering method is used to create the frame clusters. In the second phase, every two consecutive clusters, obtained by the clustering procedure, are tested for a possible merging in order to reduce false shot-cut detections. In the merging phase, statistical hypothesis testing is used. The detection technique was applied to several TRECVID video test sets that exhibit different types of shots and contain significant object and camera motion inside the shots. We demonstrate that the method detects cuts and gradual transitions, such as dissolves and fades, with high accuracy. (c) 2007 SPIE and IS&T.
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