Motion compensation using correlation-feedback

Motion compensation is widely used for exploiting temporal redundancies in the coding of image sequences. Accurate estimation of motion information in image sequences is important in motion-compensated coding. Different approaches have been used to estimate motion to obtain the motion-compensated frame difference signal. This work uses the correlation-feedback approach to estimate the velocity or the optic flow of the moving image pixel. After the motion of the pixel is estimated. the motion-compensated frame difference signal is found by subtracting the current. frame from the predicted frame. This correlation-feedback approach estimates the true motion vector of moving image accurately. Consequently, the reduced error in determining the optic flow of the moving image leads to a better motion-compensated frame difference signal. This work evaluates the performance of the correlation-feedback method by comparing it with the gradient-based approach and block method

Multiresolutional Fault-Tolerant Sensor Integration and Object Recognition in Images.

This dissertation applies multiresolution methods to two important problems in signal analysis. The problem of fault-tolerant sensor integration in distributed sensor networks is addressed, and an efficient multiresolutional algorithm for estimating the sensors\u27 effective output is proposed. The problem of object/shape recognition in images is addressed in a multiresolutional setting using pyramidal decomposition of images with respect to an orthonormal wavelet basis. A new approach to efficient template matching to detect objects using computational geometric methods is put forward. An efficient paradigm for object recognition is described

Motion estimation using optical flow field

Over the last decade, many low-level vision algorithms have been devised for extracting depth from intensity images. Most of them are based on motion of the rigid observer. Translation and rotation are constants with respect to space coordinates. When multi-objects move and/or the objects change shape, the algorithms cannot be used. In this dissertation, we develop a new robust framework for the determination of dense 3-D position and motion fields from a stereo image sequence. The framework is based on unified optical flow field (UOFF). In the UOFF approach, a four frame mode is used to compute six dense 3-D position and velocity fields. Their accuracy depends on the accuracy of optical flow field computation. The approach can estimate rigid and/or nonrigid motion as well as observer and/or object(s) motion. Here, a novel approach to optical flow field computation is developed. The approach is named as correlation-feedback approach. It has three different features from any other existing approaches. They are feedback, rubber window, and special refinement. With those three features, error is reduced, boundary is conserved, subpixel estimation accuracy is increased, and the system is robust. Convergence of the algorithm is proved in general. Since the UOFF is based on each pixel, it is sensitive to noise or uncertainty at each pixel. In order to improve its performance, we applied two Kalman filters. Our analysis indicates that different image areas need different convergence rates, for instance. the areas along boundaries have faster convergence rate than an interior area. The first Kalman filter is developed to conserve moving boundary in optical How determination by applying needed nonhomogeneous iterations. The second Kalman filter is devised to compute 3-D motion and structure based on a stereo image sequence. Since multi-object motion is allowed, newly visible areas may be exposed in images. How to detect and handle the newly visible areas is addressed. The system and measurement noise covariance matrices, Q and R, in the two Kalman filters are analyzed in detail. Numerous experiments demonstrate the efficiency of our approach

Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information

3-D data handling and registration of multiple modality medical images

The many different clinical imaging modalities used in diagnosis and therapy deliver two different types of information: morphological and functional. Clinical interpretation can be assisted and enhanced by combining such information (e.g. superimposition or fusion). The handling of such data needs to be performed in 3-D. Various methods for registration developed by other authors are reviewed and compared. Many of these are based on registering external reference markers, and are cumbersome and present significant problems to both patients and operators. Internal markers have also been used, but these may be very difficult to identify. Alternatively, methods based on the external surface of an object have been developed which eliminate some of the problems associated with the other methods. Thus the methods which have been extended, developed, and described here, are based primarily on the fitting of surfaces, as determined from images obtained from the different modalities to be registered. Annex problems to that of the surface fitting are those of surface detection and display. Some segmentation and surface reconstruction algorithms have been developed to identify the surface to be registered. Surface and volume rendering algorithms have also been implemented to facilitate the display of clinical results. An iterative surface fitting algorithm has been developed based on the minimization of a least squares distance (LSD) function, using the Powell method and alternative minimization algorithms. These algorithms and the qualities of fit so obtained were intercompared. Some modifications were developed to enhance the speed of convergence, to improve the accuracy, and to enhance the display of results during the process of fitting. A common problem with all such methods was found to be the choice of the starting point (the initial transformation parameters) and the avoidance of local minima which often require manual operator intervention. The algorithm was modified to apply a global minimization by using a cumulative distance error in a sequentially terminated process in order to speed up the time of evaluating of each search location. An extension of the algorithm into multi-resolution (scale) space was also implemented. An initial global search is performed at coarse resolution for the 3-D surfaces of both modalities where an appropriate threshold is defined to reject likely mismatch transformations by testing of only a limited subset of surface points. This process is used to define the set of points in the transformation space to be used for the next level of resolution, again with appropriately chosen threshold levels, and continued down to the finest resolution level. All these processes were evaluated using sets of well defined image models. The assessment of this algorithm for 3-D surface registration of data from (3-D) MRI with MRI, MRI with PET, MRI with SPECT, and MRI with CT data is presented, and clinical examples are illustrated and assessed. In the current work, the data from multi-modality imaging of two different types phantom (e.g. Hoffman brain phantom, Jaszczak phantom), thirty routinely imaged patients and volunteer subjects, and ten patients with setting external markers on their head were used to assess and verify 3-D registration. The accuracy of the sequential multi-resolution method obtained by the distance values of 4-10 selected reference points on each data set gave an accuracy of 1.44±0.42 mm for MR-MR, 1.82±0.65 for MR-CT, 2.38±0.88 for MR-PET, and 3.17±1.12 for MR-SPECT registration. The cost of this process was determined to be of the order of 200 seconds (on a Micro-VAX II), although this is highly dependent on some adjustable parameters of the process (e.g. threshold and the size of the geometrical transformation space) by which the accuracy is aimed

Construction of the Scale Aware Anisotropic Diffusion Pyramid With Application to Multi-scale Tracking

This thesis is concerned with the identification of features within two-dimensional imagery. Current acquisition technology is capable of producing very high-resolution images at large frame rates and generating an enormous amount of raw data. Exceeding present signal processing technology in all but the simplest image processing tasks, the visual information contained in these image sequences is tremendous in both spatial and temporal content. A majority of this detail is relatively unimportant for the identification of an object, however, and the motivations for this thesis, at the core, are the study and development of methods that are capable of identifying image features in a highly robust and efficient manor. Biological vision systems have developed methods for coping with high-resolution imagery, and these systems serve as a starting point for designing robust and efficient algorithms capable of identifying features within image sequences. By foveating towards a region of interest, biological systems initially search coarse-scale scene representations and exploit this information to efficiently process finer resolution data. This search procedure is facilitated by the nonlinear distribution of visual sensors within a biological vision system, and the result is a very efficient and robust method for identifying objects. Humans will initially identify peripheral objects as potential regions of interest, acquiring higher-resolution image information by focusing on the region, and deciding if the perceived object is actually present through the use of all available knowledge of the scene

APPLICATION OF IMAGE ANALYSIS TECHNIQUES TO SATELLITE CLOUD MOTION TRACKING

Cloud motion wind (CMW) determination requires tracking of individual cloud targets. This is achieved by first clustering and then tracking each cloud cluster. Ideally, different cloud clusters correspond to diiferent pressure levels. Two new clustering techniques have been developed for the identification of cloud types in multi-spectral satellite imagery. The first technique is the Global-Local clustering algorithm. It is a cascade of a histogram clustering algorithm and a dynamic clustering algorithm. The histogram clustering algorithm divides the multi-spectral histogram into'non-overlapped regions, and these regions are used to initialise the dynamic clustering algorithm. The dynamic clustering algorithm assumes clusters have a Gaussian distributed probability density function with diiferent population size and variance. The second technique uses graph theory to exploit the spatial information which is often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering and spectral clustering. The first stage extracts homogeneous objects in the image using a family of algorithms based on stepwise optimization. This family of algorithms can be further divided into two approaches: Top-down and Bottom-up. The second stage groups similar segments into clusters using a statistical hypothesis test on their similarities. The clusters generated are less noisy along class boundaries and are in hierarchical order. A criterion based on mutual information is derived to monitor the spatial clustering process and to suggest an optimal number of segments. An automated cloud motion tracking program has been developed. Three images (each separated by 30 minutes) are used to track cloud motion and the middle image is clustered using Global-Local clustering prior to tracking. Compared with traditional methods based on raw images, it is found that separation of cloud types before cloud tracking can reduce the ambiguity due to multi-layers of cloud moving at different speeds and direction. Three matching techniques are used and their reliability compared. Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The optimum target size for first generation METEOSAT images has also been found.Meteorological Office, Bracknel

Construction and use of research tools for image processing

Image processing now has a wide variety of applications and a large amount of algorithm development is required. Clearly, a convenient and easily used development system is a great advantage. Some preliminary work with an existing machine indicated that a carefully tailored interactive facility could provide such an environment. An image storage unit containing a novel, fast, method of accessing the window to be processed has been constructed. By delegating to the storage unit some of the tasks normally performed by image processing software a considerable increase in processing speed has been achieved. While the improvement is not sufficient for an industrial system, it does allow for the convenient investigation of algorithms of considerably greater complexity than has hitherto been found possible on a moderately priced machine. To make full use of the hardware and to provide a concise notation for the description of processing algorithms, a versatile computer language, PPL2, has been developed. PPL2 provides, in addition to an extensive range of operators, a very concise yet very efficient method of denoting image operations. A compiler for this language has been incorporated into a complete image processing system for fast interactive development and testing of programs. Use has been made of the system to investigate the possible application of the quadtree in image processing and also for the formation of the skeleton description of an object. In the latter application interest centered around the possible advantages of a 5 x 5 over a 3 x 3 pixel window. Awareness of the potential industrial applications of image processing has led to observations and comments on the hardware and software required for image processing. Conclusions are reached concerning the relative merits of parallel versus sequential algorithms and of various types of processors.<p

Digitale Bildsignalverarbeitung : Grundlagen, Verfahren, Beispiele

Mit dem vorliegenden Buch soll eine Einführung in die digitale Bildverarbeitung gegeben werden. Hierbei werden gemäß der allgemeinen Themenstellung der vorliegenden Buchreihe insbesondere die signalorientierten Aspekte der Bildverarbeitung in den Vordergrund gestellt. Den besonderen Reiz der Bildverarbeitung sehe ich in dem interessanten Zusammenwirken von intuitiven und theoretischen Lösungsansätzen, die jeweils durch ”anschauliche” Experimente evaluierbar sind. Entsprechend wird in der vorliegenden Darstellung versucht, insbesondere anhand von zahlreichen Simulationsbeispielen ein Verständnis dieses jungen, an Bedeutung zunehmenden Gebietes beim Leser zu erreichen. Zum Verständnis des Buches sind außer Kenntnissen der Mathematik, wie sie etwa in ingenieurswissenschaftlichen Studiengängen vermittelt werden, Grundkenntnisse der linearen Systemtheorie bzw. Signalverarbeitung und Grundkenntnisse der Wahrscheinlichkeitsrechnung vorteilhaft