The volume of cargo flowing through todays transportation system is growing at an ever increasing rate. Recent studies show that 90% of all international cargo that enters the United States flows through our vast seaport system. When this cargo enters the US, time is of the essence to quickly obtain and verify its identity, screen it against an ever increasingly wide variety of security concerns, and ultimately correctly direct the cargo towards its final destination. Over the past few years, new port and container security initiatives and regulations have generated huge interest in the need for accurate real-time identification and tracking of incoming and outgoing traffic of vehicles and cargo. On the contrary, the manually intensive identification and tracking processes, typically employed today, are inherently both inefficient and inadequate, and can be seen as a possible enabling factor for potential threats to our ports and therefore our national security. The contradiction between current and required processes coupled to the correlation with accelerated growth in container traffic, has clearly identified the need for a solution. One heavily researched option is the utilization of video based systems implementing Optical Character Recognition (OCR) processes for automatically extracting the unique container identification code to expedite the flow of cargo through various points in the seaport. The actual current process of how this occurs along with the opportunities and challenges for adding such a technological solution will be investigated in great detail. This thesis will investigate the feasibility of application of motion compensation algorithms as an enhancement to OCR systems specifically designed to address the challenges of OCR of cargo containers in a seaport environment. This motion compensation could offer a cost effective alternative to the sophisticated hardware systems currently being offered to US ports.

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Displacement information is important in dynamic image analysis. The method of optical flow has been well applied to compute the displacement in the field of computer vision. We apply the method of optical flow to compute the displacement information for video compression. We can predict the optical flow between picture 2 and picture 3 by the optical flow between picture 1 and picture 2 by using the principle of inertia. Using the predicted optical flow between picture 2 and picture 3, we can recover a rough version of picture 3. This version can be taken as a reference picture for encoding picture 3. This reference will decrease the compensation information in the following stage, and then improve the compression ratio of MPEG . We modified the traditional optical flow of Horn & Schunck to a regional optical flow by segmentation. Then, the displacement information could be reduced. The picture is recovered by the optical flow in a modified way because some objects couldn¡¦t move in the same direction and velocity. We propose two methods in optical flow prediction. One is the complete information: shape and value. The other is shape only with value recomputed and extra encoded. Experiments demonstrate a better compression ratio of 1% for our motion compensation than the regular motion compensation.

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International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Compressed video is necessary for a variety of telemetry requirements. A large number of competing video compression algorithms exist. This paper compares the ability of these algorithms to meet criteria which are of interest for telemetry applications. Included are: quality, compression, noise susceptibility, motion performance and latency. The algorithms are divided into those which employ inter-frame compression and those which employ intra-frame compression. A video tape presentation will also be presented to illustrate the performance of the video compression algorithms.

Cone-beam computed tomography (CBCT) is an imaging technique used in conjunction with radiation therapy. CBCT is used to verify the position of tumours just prior to radiation treatment session. The accuracy of the radiation treatment of thoracic and upper abdominal tumours is heavily affected by respiratory movement. Blurring artefacts, due to the movement during a CBCT scanning, cause misregistration between the CBCT image and the planning image. There has been growing interest in the use of motion-compensated CBCT for correcting the breathing-induced artefacts. A wide range of iterative reconstruction methods have been developed for CBCT imaging. The direct motion compensation technique has been applied to algebraic reconstruction technique (ART), simultaneous ART (SART), ordered-subset SART (OS-SART) and conjugate gradient least squares (CGLS). In this thesis a dual modality imaging of electrical impedance tomography (EIT) and CBCT is proposed for the first time. This novel dual modality imaging uses the advantages of high temporal resolution of EIT imaging and high spatial resolution of the CBCT method. The main objective of this study is to combine CBCT with EIT imaging system for motion-compensated CBCT using experimental and computational phantoms. The EIT images were used for extracting motion for a motion-compensated CBCT imaging system. A simple motion extraction technique is used for extracting motion data from the low spatial resolution EIT images. This motion data is suitable for input into the direct motion-compensated CBCT. The performance of iterative algorithms for motion compensation was also studied. The dual modality CBCT-EIT is verified using experimental EIT system and computational CBCT phantom data.

The work in this thesis explore the DWT based video coding by the introduction of a novel DWT (Discrete Wavelet Transform) / MC (Motion Compensation) / DPCM (Differential Pulse Code Modulation) video coding framework, which adopts the EBCOT as the coding engine for both the intra- and the inter-frame coder. The adaptive switching mechanism between the frame/field coding modes is investigated for this coding framework. The Low-Band-Shift (LBS) is employed for the MC in the DWT domain. The LBS based MC is proven to provide consistent improvement on the Peak Signal-to-Noise Ratio (PSNR) of the coded video over the simple Wavelet Tree (WT) based MC. The Adaptive Arithmetic Coding (AAC) is adopted to code the motion information. The context set of the Adaptive Binary Arithmetic Coding (ABAC) for the inter-frame data is redesigned based on the statistical analysis. To further improve the perceived picture quality, a Perceptual Distortion Measure (PDM) based on human vi sion model is used for the EBCOT of the intra-frame coder. A visibility assessment of the quantization error of various subbands in the DWT domain is performed through subjective tests. In summary, all these findings have solved the issues originated from the proposed perceptual video coding framework. They include: a working DWT/MC/DPCM video coding framework with superior coding efficiency on sequences with translational or head-shoulder motion; an adaptive switching mechanism between frame and field coding mode; an effective LBS based MC scheme in the DWT domain; a methodology of the context design for entropy coding of the inter-frame data; a PDM which replaces the MSE inside the EBCOT coding engine for the intra-frame coder, which provides improvement on the perceived quality of intra-frames; a visibility assessment to the quantization errors in the DWT domain.

Synthetic aperture sonar (SAS) is the underwater acoustic counterpart to stripmap-mode synthetic aperture radar (SAR). Current seagoing SAS systems are deployed on unmanned robotic vechicles, commonly referred to as autonomous underwater vehicles (AUVs). As with SAR, SAS imaging is ideally done with a straight-line collection trajectory. However, SAS is far more susceptible to image degradation caused by the actual sensor trajectory deviating from a pefectly straight line. Unwanted motion is virtually unavoidable in the sea due to the influence of currents and wave action. In order to construct a perfectly-focused SAS image the motion must either be constrained to within one-eighth of a wavelength over the synthetic aperture, or it must be measured with the same degree of accuracy and then accounted for in the processing software. Since the former is not possible, the latter approach must be taken. The technique known as redundant phase centers (RPC) has proven to be insrumental in solving the problem of SAS motion compensation. In essence, RPC simply refers to the practice of overlapping a portion of the receiver array from one ping (transmission and reception) to the next. The signals observed by this overlapping portion will be identical except for a time shift proportional to the relative motion between pings. The time shifts observed by the RPC channels of the receiver array are scalars representing the projection of the array receiver locations onto the image slant plane, and these time shifts can be used to compensate for the unwanted platform motion. This thesis presents several extensions to the standard RPC technique in which the RPC time delays are combined with the AUV's on-board navigation data. The scalar time delays are decomposed into the components induced by the six degrees of freedom of the motion: i.e., the linear and angular velocities. Thus, the time delays observed in the image slant plane can be used to refine the motion estimate in an absolute frame of reference external to the AUV. Creating a high-resolution SAS image of the sea floor in an automatic fashion demands accurate and robust motion estimation. The performance of the motion estimation schemes presented is demonstrated using actual field data collected from an assortment of current research SAS systems.

MPEG is a primary standards for dynamic image compression. The error can be introduced in the quantization process of the block DCT transformation . In this sense, the shape compensation by Boolean filter is proposed by us to adjust the intensity distribution of the motion compensated image. That is, our dynamic images are coded by the kinds of Boolean filter in addition to motion vectors after DCT coding. Boolean operation is usually applied to the binary images. The minterms of Boolean operations can be reprensented by masks. For the gray level images the mask operation is changed to be the more complex window operations. More clearly, there are two masks for a miniterm. One is a complement mask. The other is a maximum or minium window. For coding efficiency, in this paper a common complemented mask is used for two miniterms. In this sense, our Boolean filters are limited in a category of two miniterms with a same complemented structure. Experiment results have demonstrated that images going through the extra Boolean filtering have better visual quality than images processed by motion compensation only. The price is paid by the coding complexity and a relatively simple decoding structure. However, it is the decoding process which is more crucial in real time play. Therefore, we successfully integrated the Boolean filtering into MPEG-1 system in software implementation.

In recent years, the internet has been in widespread use and the number of internet subscribers increased quickly. Hence a lot of applications on the network have been developed, multimedia programs especially. Whereas the original video content always takes up considerable storage and transmission time which doesn¡¦t suit for network application, many video compression standards have been drawn up in the literature Due to the temporal redundancy of the video sequences, motion estimation / compensation has been widely used in many interframe video coding protocols to reduce the required bit rates for transmission and storage of video signals by eliminating it, such as the MPEG-1, MPEG-2, H.261 and H.263. The performance and speed of the interframe motion estimation method for video sequence compression are the important issues especially in networking application such as video conference and video on demand. Today all motion estimating method find out the estimating point which has minimal Mean Square Error, and motion compensation aim at estimating error to do JPEG. compression. As everyone knows, JPEG employs DCT to eliminate the correlation of spatial domain. So the best motion estimating point is the point which has the minimal compressed data size. In some alalyses show that over 50% best estimating point do not have the minimal compressed data size. So the factor which effects the compressed data size is correlation coefficient and not MSE. Hence, we try to define a new criterion for motion estimation which can get better motion compensation with less compressing bit rate. To reach this goal, we try to find out the correlation among the motion compensation as the new criterion for motion estimation.