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This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

Esta dissertação de mestrado versa sobre um algoritmo de detecção e de estimação de movimento robustas a rotação, escala e translação em sequências de imagens da retina em infravermelho. O algoritmo desenvolvido detecta movimentos da retina com base em medidas de distribuição estatísticas, como a entropia conjunta e a informação mútua entre os quadros adjacentes de um vídeo. A técnica de estimação de movimento inspira-se nos métodos de deslocamento de fase e na transformação log-polar para estimar os parâmetros de movimento. Esta abordagem produz uma solução robusta para este tipo de imagens intrinsecamente ruidosas e comumente utilizadas no diagnóstico e tratamento a laser do edema macular diabético. / This thesis it is about an algorithm of retinal motion detection and estimation robust to rotation, scaling and translation in infrared images sequences. This algorithm detects retinal movements based on measures of statistical distribution, such as joint entropy and mutual information between adjacent frames of a video. The technique of motion estimation is inspired by the methods of phase shift and log-polar transformation to estimate the parameters of motion. This approach yields a robust solution for this type intrinsically noisy images and commonly used in the diagnosis and laser treatment of diabetic macular edema.

Esta dissertação de mestrado versa sobre um algoritmo de detecção e de estimação de movimento robustas a rotação, escala e translação em sequências de imagens da retina em infravermelho. O algoritmo desenvolvido detecta movimentos da retina com base em medidas de distribuição estatísticas, como a entropia conjunta e a informação mútua entre os quadros adjacentes de um vídeo. A técnica de estimação de movimento inspira-se nos métodos de deslocamento de fase e na transformação log-polar para estimar os parâmetros de movimento. Esta abordagem produz uma solução robusta para este tipo de imagens intrinsecamente ruidosas e comumente utilizadas no diagnóstico e tratamento a laser do edema macular diabético. / This thesis it is about an algorithm of retinal motion detection and estimation robust to rotation, scaling and translation in infrared images sequences. This algorithm detects retinal movements based on measures of statistical distribution, such as joint entropy and mutual information between adjacent frames of a video. The technique of motion estimation is inspired by the methods of phase shift and log-polar transformation to estimate the parameters of motion. This approach yields a robust solution for this type intrinsically noisy images and commonly used in the diagnosis and laser treatment of diabetic macular edema.

Esta dissertação de mestrado versa sobre um algoritmo de detecção e de estimação de movimento robustas a rotação, escala e translação em sequências de imagens da retina em infravermelho. O algoritmo desenvolvido detecta movimentos da retina com base em medidas de distribuição estatísticas, como a entropia conjunta e a informação mútua entre os quadros adjacentes de um vídeo. A técnica de estimação de movimento inspira-se nos métodos de deslocamento de fase e na transformação log-polar para estimar os parâmetros de movimento. Esta abordagem produz uma solução robusta para este tipo de imagens intrinsecamente ruidosas e comumente utilizadas no diagnóstico e tratamento a laser do edema macular diabético. / This thesis it is about an algorithm of retinal motion detection and estimation robust to rotation, scaling and translation in infrared images sequences. This algorithm detects retinal movements based on measures of statistical distribution, such as joint entropy and mutual information between adjacent frames of a video. The technique of motion estimation is inspired by the methods of phase shift and log-polar transformation to estimate the parameters of motion. This approach yields a robust solution for this type intrinsically noisy images and commonly used in the diagnosis and laser treatment of diabetic macular edema.

This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

In this thesis, rotation, scale and translation (RST) invariant automatic target recognition (ATR) for satellite imagery is presented. Template matching is used to realize the target recognition. However, unlike most of the studies of template matching in the literature, RST invariance is required in our problem, since most of the time we will have only a small number of templates of each target, while the targets to be recognized in the scenes will have various orientations, scaling and translations. RST invariance is studied in detail and implemented with some of the competing methods in the literature, such as Fourier-Mellin transform and bipectrum combined with log-polar mapping. Phase correlation and normalized cross-correlation are used as similarity metrics. Encountered drawbacks were overcome with additional operations and modifications of the algorithms. ATR using reconstruction of the target image with respect to the template, based on bispectrum, log-polar mapping and phase correlation outperformed the other methods and successful recognition was realized for various target types, especially for targets on relatively simpler backgrounds, i.e. containing little or no other objects.

Performing tasks with a robotic hand often requires a complete knowledge of the manipulated object, including its properties (shape, rigidity, surface texture) and its location in the environment, in order to ensure safe and efficient manipulation. While well-established procedures exist for the manipulation of rigid objects, as well as several approaches for the manipulation of linear or planar deformable objects such as ropes or fabric, research addressing the characterization of deformable objects occupying a volume remains relatively limited. The fundamental objectives of this research are to track the deformation of non-rigid objects under robotic hand manipulation using RGB-D data, and to automatically classify deformable objects as either rigid, elastic, plastic, or elasto-plastic, based on the material they are made of, and to support recognition of the category of such objects through a robotic probing process in order to enhance manipulation capabilities. The goal is not to attempt to formally model the material of the object, but rather employ a data-driven approach to make decisions based on the observed properties of the object, capture implicitly its deformation behavior, and support adaptive control of a robotic hand for other research in the future. The proposed approach advantageously combines color image and point cloud processing techniques, and proposes a novel combination of the fast level set method with a log-polar mapping of the visual data to robustly detect and track the contour of a deformable object in a RGB-D data stream. Dynamic time warping is employed to characterize the object properties independently from the varying length of the detected contour as the object deforms. The research results demonstrate that a recognition rate over all categories of material of up to 98.3% is achieved based on the detected contour. When integrated in the control loop of a robotic hand, it can contribute to ensure stable grasp, and safe manipulation capability that will preserve the physical integrity of the object.