Orthogonal Wavelets via Filter Banks: Theory and Applications

Wavelets are used in many applications, including image processing, signal analysis and seismology. The critical problem is the representation of a signal using a small number of computable functions, such that it is represented in a concise and computationally efficient form. It is shown that wavelets are closely related to filter banks (sub band filtering) and that there is a direct analogy between multiresolution analysis in continuous time and a filter bank in discrete time. This provides a clear physical interpretation of the approximation and detail spaces of multiresolution analysis in terms of the frequency bands of a signal. Only orthogonal wavelets, which are derived from orthogonal filter banks, are discussed. Several examples and applications are considered

Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data

Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation. In this paper, the nonferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (fuzzy spectral and spatial classifier) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the nonferrous materials than the single pixel spectral features when applied to the construction of multivariate Gaussian distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyperspectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyperspectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for nonferrous material classification

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Biodegradable polyesters with potential applications in 3D printing: structure, morphology and crystallization

269 p.En este trabajo se han estudiado varios sistemas basados en poliésteres con el objetivo de ampliar su conocimiento y difundir su uso. La familia de poliésteres más investigada en esta tesis es la de los polihidroxialcanoatos (PHAs), poliésteres biodegradables de origen bacteriana con excelentes propiedades ópticas y de barrera, ideales para aplicaciones en embalaje y en la industria biomédica pero con dos grandes defectos que limitaban su difusión: alta cristalinidad lo que implica malas propiedades mecánicas y altos puntos de fusión lo que implica una rápida degradación de los productos. Por tanto, este trabajo se encuadra en este contexto con el objetivo de estudiar nuevos sistemas sustitutivos de los clásicos; de hecho, diferentes sistemas basados en PHB se estudian con varias técnica

Algorithms of Clustering and Classifying Batik Images Based on Color, Contrast and Motif

An interactive system could be provided for batik customers with the aim of helping them in selecting the right batiks. The system should manage a collection of batik images along with other information such as fashion color type, the contrast degree, and motif. This research aims to find methods of clustering and classifying batik images based on fashion color, contrast and motif. A color clustering algorithm using HSV color system is proposed. Two algorithms for contrast clustering, both utilize wavelet, are proposed. Six algorithms for clustering and classifying batik images based on group of motifs, employing shape- and texture-based techniques, are explored and proposed. Wavelet is used in image pre-processing, Canny detector is used to detect image edges. Experiments are conducted to evaluate the performance of the algorithms. The result of experiments shows that fashion color and contrast clustering algorithms perform quite well. Three of motif based clustering and classification algorithms perform fairly well, further work is needed to increase the accuracy and to refine the classification into detailed motif

Light microscopy--a prototype for a remote image diagnosis system

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 67-69).by Alexander B. Zakharov.M.Eng

Fusion of Visual and Thermal Images Using Genetic Algorithms

Demands for reliable person identification systems have increased significantly due to highly security risks in our daily life. Recently, person identification systems are built upon the biometrics techniques such as face recognition. Although face recognition systems have reached a certain level of maturity, their accomplishments in practical applications are restricted by some challenges, such as illumination variations. Current visual face recognition systems perform relatively well under controlled illumination conditions while thermal face recognition systems are more advantageous for detecting disguised faces or when there is no illumination control. A hybrid system utilizing both visual and thermal images for face recognition will be beneficial. The overall goal of this research is to develop computational methods that improve image quality by fusing visual and thermal face images. First, three novel algorithms were proposed to enhance visual face images. In those techniques, specifical nonlinear image transfer functions were developed and parameters associated with the functions were determined by image statistics, making the algorithms adaptive. Second, methods were developed for registering the enhanced visual images to their corresponding thermal images. Landmarks in the images were first detected and a subset of those landmarks were selected to compute a transformation matrix for the registration. Finally, A Genetic algorithm was proposed to fuse the registered visual and thermal images. Experimental results showed that image quality can be significantly improved using the proposed framework