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Free viewpoint television (FTV) is a new concept that aims at giving viewers the flexibility to select a novel viewpoint by employing multiple video streams as the input. Current proposed solutions for FTV include those based on ray-space resampling which demand at least dozens of cameras and large storage and transmission resources for those video streams. Image-based rendering (IBR) methods that rely on dense depth map estimation also face practical difficulties since accurate depth map estimation remains a challenging problem. This paper proposes a framework for FTV based on IBR that relieves the need for an accurate depth map by introducing a hybrid virtual view synthesis method. The framework also includes an intuitive method for virtual view specification in uncalibrated views. We present both simulation and real data experiments to validate the proposed framework and the component algorithms. Index Terms--3-D TV, image-based rendering, view specification, view synthesis.

A motion-compensated reduced order model Kalman filter for restoring progressive and interlaced video is derived. The formulation is then extended to include mean-residual signal modeling and to develop a motion compensation approach to reduce artifacts resulting from fix-lag recursive linear space-varying (LSV) filtering. It is shown that the filters effectively restores images degraded by LSV blurs and additive noise.

Image processing research has established a method of estimating motion (optical flow) and segmentation using the parametric and residual fields of a Bayesian framework. Minimum-norm residual field estimates are used to update the motion field. The highest confidence first and iterated conditional mode optimization methods are employed in successive optimization.

The study demonstrates that clearer video images can be obtained by allowing for the effects of motion which occurs during the aperture opening time. Real videos are used. The model for video acquisition considers sampling on an arbitrary lattice, aperture time, focus blur, additive noise and a nonzero aperture time. The model is applied to develop a POCS-based algorithm for reforming a SR image or video from LR video which contains relative motion.

We present robust algorithms which combine global motion compensation and motion adaption for deinterlacing in the presence of both dominant motion, such as camera zoom, pan, or jitter, and local motion, such as object motion. The dominant motion is modeled by a global airline warping and estimated by a gradient-based estimation method. Two alternative algorithms are proposed for compensation of the dominant motion: a bilinear interpolation based on the affine model, and a projections onto convex sets (POCS) based method that takes into account blurring in the image formation. It is important to note that the latter must be used if the blurring is severe enough to act as an anti-alias filter, which imposes an irreversible limit on the resolution improvement ability of any motion-compensated filter. Global motion-compensated images are then input to a motion-adaptive filter to detect and correct for those pixels where there exists local motion. A dynamic thresholding for motion detection is presented, with weighted directional-filtering for regions where motion is detected, to obtain the best results. Experimental results with application to obtaining high quality stills from video camcorders demonstrate the effectiveness of the proposed methods. Index Terms - Deinterlacing, motion compensation, motion detection, standards conversion, video signal processing, video stills.

Projection onto convex sets (POCS) method, used to restore space-varying blurred images, comprises of a distinct blurring function at each image pixels for effective computation. The POCS formulation varies from the techniques used for space-invariant image restoration problems. The results of the POCS method are compared to ROMKT method simulations for real-life images with space-varying out-of focus blur.

We propose a novel adaptive spatiotemporal filter, called the adaptive weighted averaging (AWA) filter, for effective noise suppression in image sequences without introducing visually disturbing blurring artifacts. Filtering is performed by computing the weighted average of image values within a spatiotemporal support along the estimated motion trajectory at each pixel. The weights are determined by optimizing a well-defined mathematical criterion, which provides an implicit mechanism for deemphasizing the contribution of the outlier pixels within the spatiotemporal filter support to avoid blurring. The AWA filter is therefore particularly well suited for filtering sequences that contain segments with abruptly changing scene content due to, for example, rapid zooming and changes in the view of the camera. The performance of the proposed AWA filter is compared with that of the spatiotemporal, local linear minimum mean square error (LMMSE) filtering. The results demonstrate that the proposed AWA filter outperforms the LMMSE filter, especially in the cases of low signal-to-noise ratios and abruptly varying scene content.

Techniques for the efficient restoration of spatially blurred and noisy image multiframe sequences are proposed using the cross-correlated multiframe (CCMF) and motion compensated multiframe (MCMF) Wiener filters. Results show that single-frame restoration of degraded frames works best for CCMF while an undegraded image with statistical properties similar to the original is best for the MCMF.

We develop a new method for the estimation of the parameters of multipath communication channels. The method makes use of matched filtering, set-theoretic deconvolution and autoregressive modeling. Pulse compression waveforms are used as channel probing signals. We report the results of a feasibility study where the proposed method is applied to specular multipath channels to estimate the multipath parameters at a number of different signal-to-noise ratios (SNR).

We propose a three-level video-event detection methodology and apply it to animal-hunt detection in wildlife documentaries. The first level extracts color, texture, and motion features, and detects shot boundaries and moving object blobs. The mid-level employs a neural network to determine the object class of the moving object blobs. This level also generates shot descriptors that combine features from the first level and inferences from the mid-level. The shot descriptors are then used by the domain-specific inference process at the third level to detect video segments that match the user-defined event model. The proposed approach has been applied to the detection of hunts in wildlife documentaries. Our method can be applied to different events by adapting the classifier at the intermediate level and by specifying a new event model at the highest level. Event-based video indexing, summarization, and browsing are among the applications of the proposed approach. Index Terms--Browsing and visualization, content-based indexing and retrieval, video content analysis.

Free viewpoint television (FTV) is a new concept that aims at giving viewers the flexibility to select a novel viewpoint by employing multiple video streams as the input. Current proposed solutions for FTV include those based on ray-space resampling which demand at least dozens of cameras and large storage and transmission resources for those video streams. Image-based rendering (IBR) methods that rely on dense depth map estimation also face practical difficulties since accurate depth map estimation remains a challenging problem. This paper proposes a framework for FTV based on IBR that relieves the need for an accurate depth map by introducing a hybrid virtual view synthesis method. The framework also includes an intuitive method for virtual view specification in uncalibrated views. We present both simulation and real data experiments to validate the proposed framework and the component algorithms. [ABSTRACT FROM AUTHOR]

Focuses on robust, object based high resolution image reconstruction from low resolution video. What procedure was used; Indepth look at the projection onto convex sets (POCS) method; Estimation of the object based motion.