Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm

A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution

An apparatus and methods for processing video data are described The invention provides a representation of video data that can be used to assess agreement between the data and a fitting model for a particular parameterization of the data This allows the comparison of different parameterization techniques and the selection of the optimum one for continued video processing of the particular data The representation can be utilized in intermediate form as part of a larger process or as a feedback mechanism for processing video data When utilized in its intermediate form, the invention can be used in processes for storage, enhancement, refinement, feature extraction, compression, coding, and transmission of video data The invention serves to extract salient information in a robust and efficient manner while addressing the problems typically associated with video data sources

Apparatus and method for processing video data

We describe a pipelined optical-flow processing system that works as a virtual motion sensor. It is based on a field-programmable gate array (FPGA) device enabling the easy change of configuring parameters to adapt the sensor to different speeds, light conditions and other environmental factors. We refer to it as a "virtual sensor" because it consists of a conventional camera as front-end supported by an FPGA processing device, which embeds the frame grabber, optical-flow algorithm implementation, output module, and some configuration and storage circuitry. To the best of our knowledge, this is the first study that presents a fully stand-alone optical-flow processing system to include measurements of the platform performance in terms of accuracy and speed.

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FPGA-based real-time optical-flow system

Techniques for performing visual surveillance of one or more moving objects are provided. The techniques include registering one or more images captured by one or more cameras, wherein registering the one or more images comprises region-based registration of the one or more images in two or more adjacent frames, performing motion segmentation of the one or more images to detect one or more moving objects and one or more background regions in the one or more images, and tracking the one or more moving objects to facilitate visual surveillance of the one or more moving objects.

Detection and tracking of moving objects

A method and apparatus for image data compression includes detecting a portion of an image signal that uses a disproportionate amount of bandwidth compared to other portions of the image signal. The detected portion of the image signal result in determined components of interest. Relative to certain variance, the method and apparatus normalize the determined components of interest to generate an intermediate form of the components of interest. The intermediate form represents the components of interest reduced in complexity by the certain variance and enables a compressed form of the image signal where the determined components of interest maintain saliency. In one embodiment, the video signal is a sequence of video frames. The step of detecting includes any of: (i) analyzing image gradients across one or more frames where image gradient is a first derivative model and gradient flow is a second derivative, (ii) integrating finite differences of pels temporally or spatially to form a derivative model, (iii) analyzing an illumination field across one or more frames, and (iv) predictive analysis, to determine bandwidth consumption. The determined bandwidth consumption is then used to determine the components of interest.

Computer method and apparatus for processing image data

Digitized visual information is a rapidly growing component of emerging video and multimedia services. In its raw digitized form visual information places large demands on bandwidth and storage requirements of applications. Picture and video compression is a key enabling technology for overcoming this problem. This paper focuses on motion estimation and compensation techniques as tools that are used in video compression applications.

T.Na Veen

Papers

Motion Estimation Methods for Video Compression—A Review