The privacy and integrity protection of digital documents specially the case of biomedical images is a vital subject in the current world of telecommunication and digital multimedia exchanges. In this paper, a modified semi robust digital image watermarking method with tamper detection and recovery ability is proposed. For this purpose, the proposed method comprises two stages. In the first stage, a specific order of the integer wavelet coefficient of the original image forms the watermark which prepares both tamper detection and recovery ability. These abilities are created by a specific parsing of watermark bits named as dual watermarking. In the second stage of the proposed method, watermark embedding and extraction procedures are done. To be more robust, a Convolutional Error Correction Code is employed in the watermark establishment process. In the extraction process, the probable tamper will be detected and the original image shall be recovered. This method is also improved to be adopted for reversible ROI recovery of medical images even in the presence of some kind of intentional and unintentional attacks. The experimental results show that the proposed method has a high performance in the case of tamper detection and is able to recover original images from noisy ones.
Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive Gaussian mixture model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. In comparison to other mixture models that are complex and computationally expensive, the proposed method is fast and easy to implement. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method. Index Terms-image processing, Background models, video surveillance, foreground detection, Gaussian mixture model.
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