Data comparison schemes for Pattern Recognition in Digital Images using Fractals

Pattern recognition in digital images is a common problem with application in remote sensing, electron microscopy, medical imaging, seismic imaging and astrophysics for example. Although this subject has been researched for over twenty years there is still no general solution which can be compared with the human cognitive system in which a pattern can be recognised subject to arbitrary orientation and scale. The application of Artificial Neural Networks can in principle provide a very general solution providing suitable training schemes are implemented. However, this approach raises some major issues in practice. First, the CPU time required to train an ANN for a grey level or colour image can be very large especially if the object has a complex structure with no clear geometrical features such as those that arise in remote sensing applications. Secondly, both the core and file space memory required to represent large images and their associated data tasks leads to a number of problems in which the use of virtual memory is paramount. The primary goal of this research has been to assess methods of image data compression for pattern recognition using a range of different compression methods. In particular, this research has resulted in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. It allows both ANNs and correlation to be used subject to appropriate pre-and post-processing techniques for digital image processing on aspect for which a dedicated programmer's work bench has been developed using X-Designer

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

Selected Topics in Bayesian Image/Video Processing

In this dissertation, three problems in image deblurring, inpainting and virtual content insertion are solved in a Bayesian framework.;Camera shake, motion or defocus during exposure leads to image blur. Single image deblurring has achieved remarkable results by solving a MAP problem, but there is no perfect solution due to inaccurate image prior and estimator. In the first part, a new non-blind deconvolution algorithm is proposed. The image prior is represented by a Gaussian Scale Mixture(GSM) model, which is estimated from non-blurry images as training data. Our experimental results on a total twelve natural images have shown that more details are restored than previous deblurring algorithms.;In augmented reality, it is a challenging problem to insert virtual content in video streams by blending it with spatial and temporal information. A generic virtual content insertion (VCI) system is introduced in the second part. To the best of my knowledge, it is the first successful system to insert content on the building facades from street view video streams. Without knowing camera positions, the geometry model of a building facade is established by using a detection and tracking combined strategy. Moreover, motion stabilization, dynamic registration and color harmonization contribute to the excellent augmented performance in this automatic VCI system.;Coding efficiency is an important objective in video coding. In recent years, video coding standards have been developing by adding new tools. However, it costs numerous modifications in the complex coding systems. Therefore, it is desirable to consider alternative standard-compliant approaches without modifying the codec structures. In the third part, an exemplar-based data pruning video compression scheme for intra frame is introduced. Data pruning is used as a pre-processing tool to remove part of video data before they are encoded. At the decoder, missing data is reconstructed by a sparse linear combination of similar patches. The novelty is to create a patch library to exploit similarity of patches. The scheme achieves an average 4% bit rate reduction on some high definition videos

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Adaptive filtering techniques for acquisition noise and coding artifacts of digital pictures

The quality of digital pictures is often degraded by various processes (e.g, acquisition or capturing, compression, filtering process, transmission, etc). In digital image/video processing systems, random noise appearing in images is mainly generated during the capturing process; while the artifacts (or distortions) are generated in compression or filtering processes. This dissertation looks at digital image/video quality degradations with possible solution for post processing techniques for coding artifacts and acquisition noise reduction for images/videos. Three major issues associated with the image/video degradation are addressed in this work. The first issue is the temporal fluctuation artifact in digitally compressed videos. In the state-of-art video coding standard, H.264/AVC, temporal fluctuations are noticeable between intra picture frames or between an intra picture frame and neighbouring inter picture frames. To resolve this problem, a novel robust statistical temporal filtering technique is proposed. It utilises a re-descending robust statistical model with outlier rejection feature to reduce the temporal fluctuations while preserving picture details and motion sharpness. PSNR and sum of square difference (SSD) show improvement of proposed filters over other benchmark filters. Even for videos contain high motion, the proposed temporal filter shows good performances in fluctuation reduction and motion clarity preservation compared with other baseline temporal filters. The second issue concerns both the spatial and temporal artifacts (e.g, blocking, ringing, and temporal fluctuation artifacts) appearing in compressed video. To address this issue, a novel joint spatial and temporal filtering framework is constructed for artifacts reduction. Both the spatial and the temporal filters employ a re-descending robust statistical model (RRSM) in the filtering processes. The robust statistical spatial filter (RSSF) reduces spatial blocking and ringing artifacts whilst the robust statistical temporal filter (RSTF) suppresses the temporal fluctuations. Performance evaluations demonstrate that the proposed joint spatio-temporal filter is superior to H.264 loop filter in terms of spatial and temporal artifacts reduction and motion clarity preservation. The third issue is random noise, commonly modeled as mixed Gaussian and impulse noise (MGIN), which appears in image/video acquisition process. An effective method to estimate MGIN is through a robust estimator, median absolute deviation normalized (MADN). The MADN estimator is used to separate the MGIN model into impulse and additive Gaussian noise portion. Based on this estimation, the proposed filtering process is composed of a modified median filter for impulse noise reduction, and a DCT transform based denoising filter for additive Gaussian noise reduction. However, this DCT based denoising filter produces temporal fluctuations for videos. To solve this problem, a temporal filter is added to the filtering process. Therefore, another joint spatio-temporal filtering scheme is built to achieve the best visual quality of denoised videos. Extensive experiments show that the proposed joint spatio-temporal filtering scheme outperforms other benchmark filters in noise and distortions suppression