ROBUST DWT - SVD DOMAIN IMAGE WATERMARKING:EMBEDDING DATA IN ALL FREQUENCIES

    Watermarking ( data hiding ) is the process of embedding data into a multimedia element such as image, audio or video. This embedded data can later be extracted from, or detected in, the multimedia for security purposes . A watermarking algorithm consists of the watermark structure, an embedding algorithm, and an extraction, or a detection, algorithm. Watermarks can be embedded in the pixel domain or a transform domain. In multimedia applications, embedded watermarks should be invisible, robust, and have a high capacity .Robustness is the resistance of an embedded watermark against intentional attacks, and normal A/V processes such as noise, filtering ( blurring , sharpening, etc.), resampling, scaling, rotation, cropping, and lossy compression. Capacity is the amount of data that can be represented by an embedded watermark. The approaches used in watermarking still images include least-significant bit encoding, basic M-sequence, transform techniques,and image-adaptive techniques. An important criterion for classifying watermarking schemes is the type of information needed by the detector: Non-blind schemes: Both the original image and the secret key(s) for watermark embedding. Semi-blind schemes: The secret key(s) and the watermark bit sequence. Blind schemes: Only the secret key(s). Typical uses of watermarks include copyright protection,the cost of a watermarking system will depend on the intended use,and may vary considerably . Two widely used image compression standards are JPEG and JPEG2000. The former is based on the Discrete Cosine Transform (DCT), and the latter the Discrete Wavelet Transform (DWT).

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AN FPGA-BASED ARCHITECTURE FOR REAL TIME IMAGE FEATURE EXTRACTION

    Realtime image pattern recognition is a challenging task which involves image processing,feature extraction and pattern classification . It applies to a wide range of applications including multimedia , military and medical ones. Its high computational requirements force systems to use very expensive clusters, custom VLSI designs or even both. These approaches suffer from various disadvantages, such as high cost and long development times. Recent advances in fabrication technology allow the manufacturing of high density and high performance Field Programmable Gate Arrays ( FPGAs ) capable of performing many complex computations in parallel while hosted by conventional computer hardware. A variety of architecture designs capable of supporting real-time pattern recognition have been proposed in the recent literature , such as implementations of algorithms for image and video processing, classification and image feature extraction algorithms.Texture plays a significant role in image analysis & pattern recognition only a few architectures implement on-board textural feature extraction.Most prominent approaches include the extraction of Gabor wavelet features for face/object recognition and the computation of mean and contrast Gray Level Cooccurrence Matrix (GLCM) features.

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