Thesis (DSc (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2009. / The replacement of the standard discrete cosine transform (DCT) of JPEG with the windowed modifed DCT (MDCT) is investigated to determine whether improvements in numerical quality can be achieved. To this end, we employ an existing algorithm for optimal quantisation, for which we also propose improvements. This involves the modelling and prediction of quantisation tables to initialise the algorithm, a strategy that is also thoroughly tested. Furthermore, the effects of various window functions on the coding results are investigated, and we find that improved quality can indeed be achieved by modifying JPEG in this fashion.

This thesis deals with the development and evaluation of blind discrete cosine transform-based watermarking algorithms for copyright protection of digital still images using handwritten signatures and mobile phone numbers. The new algorithms take into account the perceptual capacity of each low frequency coefficients inside the Discrete Cosine Transform (DCT) blocks before embedding the watermark information. They are suitable for grey-scale and colour images. Handwritten signatures are used instead of pseudo random numbers. The watermark is inserted in the green channel of the RGB colour images and the luminance channel of the YCrCb images. Mobile phone numbers are used as watermarks for images captured by mobile phone cameras. The information is embedded multiple-times and a shuffling scheme is applied to ensure that no spatial correlation exists between the original host image and the multiple watermark copies. Multiple embedding will increase the robustness of the watermark against attacks since each watermark will be individually reconstructed and verified before applying an averaging process. The averaging process has managed to reduce the amount of errors of the extracted information. The developed watermarking methods are shown to be robust against JPEG compression, removal attack, additive noise, cropping, scaling, small degrees of rotation, affine, contrast enhancements, low-pass, median filtering and Stirmark attacks. The algorithms have been examined using a library of approximately 40 colour images of size 512 512 with 24 bits per pixel and their grey-scale versions. Several evaluation techniques were used in the experiment with different watermarking strengths and different signature sizes. These include the peak signal to noise ratio, normalized correlation and structural similarity index measurements. The performance of the proposed algorithms has been compared to other algorithms and better invisibility qualities with stronger robustness have been achieved.

This thesis presents a novel lip-reading approach to classifying utterances from video data, without evaluating voice signals. This work addresses two important issues which are • the efficient representation of mouth movement for visual speech recognition • the temporal segmentation of utterances from video. The first part of the thesis describes a robust movement-based technique used to identify mouth movement patterns while uttering phonemes. This method temporally integrates the video data of each phoneme into a 2-D grayscale image named as a motion template (MT). This is a view-based approach that implicitly encodes the temporal component of an image sequence into a scalar-valued MT. The data size was reduced by extracting image descriptors such as Zernike moments (ZM) and discrete cosine transform (DCT) coefficients from MT. Support vector machine (SVM) and hidden Markov model (HMM) were used to classify the feature descriptors. A video speech corpus of 2800 utterances was collected for evaluating the efficacy of MT for lip-reading. The experimental results demonstrate the promising performance of MT in mouth movement representation. The advantages and limitations of MT for visual speech recognition were identified and validated through experiments. A comparison between ZM and DCT features indicates that th e accuracy of classification for both methods is very comparable when there is no relative motion between the camera and the mouth. Nevertheless, ZM is resilient to rotation of the camera and continues to give good results despite rotation but DCT is sensitive to rotation. DCT features are demonstrated to have better tolerance to image noise than ZM. The results also demonstrate a slight improvement of 5% using SVM as compared to HMM. The second part of this thesis describes a video-based, temporal segmentation framework to detect key frames corresponding to the start and stop of utterances from an image sequence, without using the acoustic signals. This segmentation technique integrates mouth movement and appearance information. The efficacy of this technique was tested through experimental evaluation and satisfactory performance was achieved. This segmentation method has been demonstrated to perform efficiently for utterances separated with short pauses. Potential applications for lip-reading technologies include human computer interface (HCI) for mobility-impaired users, defense applications that require voice-less communication, lip-reading mobile phones, in-vehicle systems, and improvement of speech-based computer control in noisy environments.

Many scientific data sets are contaminated by noise, either because of data acquisition process, or because of naturally occurring phenomena. A first step in analyzing such data sets is denoising, i.e., removing additive noise from a noisy image. For images, noise suppression is a delicate and a difficult task. A trade of between noise reduction and the preservation of actual image features has to be made in a way that enhances the relevant image content. The beginning chapter in this thesis is introductory in nature and discusses the Popular denoising techniques in spatial and frequency domains. Wavelet transform has wide applications in image processing especially in denoising of images. Wavelet systems are a set of building blocks that represent a signal in an expansion set involving indices for time and scale. These systems allow the multi-resolution representation of signals. Several well known denoising algorithms exist in wavelet domain which penalize the noisy coefficients by threshold them. We discuss the wavelet transform based denoising of images using bit planes. This approach preserves the edges in an image. The proposed approach relies on the fact that wavelet transform allows the denoising strategy to adapt itself according to directional features of coefficients in respective sub-bands. Further, issues related to low complexity implementation of this algorithm are discussed. The proposed approach has been tested on different sets images under different noise intensities. Studies have shown that this approach provides a significant reduction in normalized mean square error (NMSE). The denoised images are visually pleasing. Many of the image compression techniques still use the redundancy reduction property of the discrete cosine transform (DCT). So, the development of a denoising algorithm in DCT domain has a practical significance. In chapter 3, a DCT based denoising algorithm is presented. In general, the design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated approach to design filters based on DCT is proposed in chapter 3. This algorithm reorganizes DCT coefficients in a wavelet transform manner to get the better energy clustering at desired spatial locations. An adaptive threshold is chosen because such adaptively can improve the wavelet threshold performance as it allows additional local information of the image to be incorporated in the algorithm. Evaluation results show that the proposed filter is robust under various noise distributions and does not require any a-priori Knowledge about the image. Inpainting is another application that comes under the category of image processing. In painting provides a way for reconstruction of small damaged portions of an image. Filling-in missing data in digital images has a number of applications such as, image coding and wireless image transmission for recovering lost blocks, special effects (e.g., removal of objects) and image restoration (e.g., removal of solid lines, scratches and noise removal). In chapter 4, a wavelet based in painting algorithm is presented for reconstruction of small missing and damaged portion of an image while preserving the overall image quality. This approach exploits the directional features that exist in wavelet coefficients in respective sub-bands. The concluding chapter presents a brief review of the three new approaches: wavelet and DCT based denoising schemes and wavelet based inpainting method.

We describe a fast method for the evaluation of an arbitrary high-dimensional multivariate algebraic polynomial in Chebyshev form at the nodes of an arbitrary rank-1 Chebyshev lattice. Our main focus is on conditions on rank-1 Chebyshev lattices allowing for the exact reconstruction of such polynomials from samples along such lattices and we present an algorithm for constructing suitable rank-1 Chebyshev lattices based on a component-by-component approach. Moreover, we give a method for the fast, exact and stable reconstruction.

In this thesis, a modified 2D Discrete Cosine Transform based electro-optic and IR image fusion algorithm is proposed and implemented on an FPGA platform. The platform is a custom FPGA board which uses ALTERA Stratix III family FPGA. The algorithm is also compared with state of the art image fusion algorithms by means of an image fusion software application GUI developed in Matlab&reg / . The proposed algorithm principally takes corresponding 4x4 pixel blocks of two images to be fused and transforms them by means of 2D Discrete Cosine Transform. Then, the L2 norm of each block is calculated and used as the weighting factor for the AC values of the fused image block. The DC value of the fused block is the arithmetic mean of the DC coefficients of both input blocks. Based on this mechanism, the whole two images are processed in such a way that the output image is a composition of the processed 4x4 blocks. The proposed algorithm performs well compared to the other state of the art image fusion algorithms both in subjective and objective quality evaluations. In hardware, v the implemented algorithm can accept input videos as fast as 65 MHz pixel clock with a resolution of 1024x768 @60 Hz.

Face recognition can be used in various biometric applications ranging from identifying criminals entering an airport to identifying an unconscious patient in the hospital With the introduction of 3-dimensional scanners in the last decade, researchers have begun to develop new methods for 3-D face recognition. This thesis focuses on 3-D face recognition using the one- and two-dimensional Discrete Cosine Transform (DCT) . A feature ranking based dimensionality reduction strategy is introduced to select the DCT coefficients that yield the best classification accuracies. Two forms of 3-D representation are used: point cloud and depth map images. These representations are extracted from the original VRML files in a face database and are normalized during the extraction process. Classification accuracies exceeding 97% are obtained using the point cloud images in conjunction with the 2-D DCT.

Abstract Vectorcardiography (VCG) determines the direction and magnitude of the heart’s electrical forces. Interpretation of the digital three-dimensional vectorcardiography in clinical applications requires robust methods and novel approaches for calculating the vectorcardiographic features. This dissertation aimed to develop new methods for vectorcardiographic signal processing. The robustness of selected pre-processing and feature extraction algorithms was improved, novel methods for detecting the injured myocardial tissue from electrocardiogram (ECG) were devised, and dynamical behavior of vectorcardiographic features was determined. The main results of the dissertation are: (1) Digitizing process and proper filtering did not produce significant distortions for dipolar Singular Value Decomposition -based ECG parameters from a diagnostic viewpoint, whereas non-dipolar parameters were very sensitive to the pre-processing operations. (2) A novel method for estimating the severity of the myocardial infarction (MI) was developed by combining the action potential based computer model and 12-lead ECG patient data. Using the method it is possible to calculate an approximate estimate of the maximum troponin value and therefore the severity of the MI. In addition, the size and location of the myocardial infarction was found to affect diagnostic significant Total-cosine-R-to-T parameter (TCRT) - changes, both in the simulations and in the patient study. (3) Furthermore, the results showed that carefully targeted improvements to the basic algorithm of the TCRT parameter can evidently decrease the number of algorithm-based failures and therefore improve the diagnostic value of TCRT in different patient data. (4) Finally, a method for calculating beat-to-beat vectorcardiographic features during exercise was developed. It was observed that the breathing affects the beat-to-beat variability of all the QRS/T angle measures and the trend of the TCRT parameter during exercise was found to be negative. Further, the results of the thesis clearly showed that the QRS/T angle measures exhibit a strong correlation with the heart rate in individual subjects. The results of the dissertation highlight the importance of robust algorithms in a VCG analysis. The results should be taken into account in further studies, so that the vectorcardiography can be utilized more effectively in clinical applications. / Tiivistelmä Vektorikardiorgafia (VKG) kuvaa sydämen sähköisen toiminnan suuntaa ja suuruutta sydämen lyönnin eri vaiheissa. Vektorikardiogrammin onnistunut tulkinta kliinisissä sovelluksissa edellyttää luotettavia menetelmiä ja uusia lähestymistapoja vektorikardiografisten piirteiden laskennassa. Tämän väitöskirjan tavoitteena oli kehittää uusia vektorikardiografisia signaalinkäsittelymenetelmiä. Väitöstyössä parannettin tiettyjen elektrokardiorgafisen (EKG) -signaalin esikäsittelyvaiheiden ja piirteentunnistusalgoritmien luotettavuutta, kehitettiin uusia menetelmiä vaurioituneen sydänlihaskudoksen tunnistamiseen EKG-signaalista, sekä tutkittiin vektorikardiografisten piirteiden dynaamista käyttäytymistä. Väitöskirjan päätulokset voidaan tiivistää seuraavasti: (1) Paperitallenteisten EKG-tallenteiden digitointiprosessi ja EKG-signaalin asianmukainen suodatus ei aiheuta diagnostisesti merkittäviä vääristymiä ns. dipolaarisiin singulaariarvohajotelmaan (SVD) perustuviin EKG-parametreihin. Kuitenkin ns. ei-dipolaariset herkemmät parametrit ovat sensitiivisiä näille esikäsittelyvaiheille. (2) Väitöskirjatyössä kehitettiin uusi menetelmä sydäninfarktin vakavuuden arvioimiselle 12-kanavaisesta EKG-signaalista käyttäen aktiopotentiaaleihin perustuvaa tietokonemallia. Väitöstyössä todettiin, että menetelmää käyttäen on mahdollista laskea karkea estimaatti kliinisessä käytössä olevalle maksimaaliselle troponiiniarvolle, joka kertoo vaurion määrästä sydänlihaskudoksessa. Lisäksi sydäninfarktin koon ja sijainnin havaittiin vaikuttavan vektorikardiografiseen de- ja repolarisaation suhdetta kuvaavaan diagnostisesti merkittävään Total-cosine-R-to-T- (TCRT) muuttujaan. (3) Tulokset osoittivat myös, että tekemällä muutamia pieniä parannuksia alkuperäiseen TCRT-parametrin algoritmiin, voidaan merkittävästi vähentää parametrin laskennassa aiheutuvia vääristymiä ja täten parantaa TCRT-parametrin diagnostista arvoa erilaisissa potilasaineistoissa. (4) Neljänneksi, työssä kehitettiin menetelmä, jolla vektorikardiografisia piirteitä laskettiin dynaamisesti lyönti lyönniltä. Hengityksen havaittiin aiheuttavan rasitustestin aikana merkittävää lyöntikohtaista vaihtelua. Työssä havaittiin myös, että niin TCRT-parametrilla kuin myös muillakin de- ja repolarisaation välistä suhdetta kuvaavilla muuttujilla oli selvä korrelaatio sydämen sykkeen kanssa. Väitöskirjan tulokset korostavat luotettavien algoritmien tärkeyttä vektorikardiografisessa analyysissä. Tulosten huomioiminen jatkotutkimuksissa edesauttaa vektorikardiografian hyödyntämistä kliinisissä sovelluksissa.

The process of contourization is presented which converts a raster image into a discrete set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimises noticeable artifacts in the simplified image. The contour merging technique offers a complementary lossy compression system to the QDCT (Quantised Discrete Cosine Transform). The artifacts introduced by the two methods are very different; QDCT produces a general blurring and adds extra highlights in the form of overshoots, whereas contour merging sharpens edges, reduces highlights and introduces a degree of false contouring. A format based on the contourization technique which caters for most image types is defined, called the contour tree image format. Image operations directly on this compressed format have been studied which for certain manipulations can offer significant operational speed increases over using a standard raster image format. A couple of examples of operations specific to the contour tree format are presented showing some of the features of the new format.

The master's thesis is concerned with digital image denoising methods. The theoretical part explains some elementary terms related to image processing, image noise, categorization of noise and quality determining criteria of denoising process. There are also particular denoising methods described, mentioning their advantages and disadvantages in this paper. The practical part deals with an implementation of the selected denoising methods in a Java, in the environment of application RapidMiner. In conclusion, the results obtained by different methods are compared.