Video copyright protection is the most generally acknowledged method of preventing data piracy. This paper proposes a blind video copyright protection technique based on the Fast Walsh Hadamard Transform (FWHT), Discrete Wavelet Transform (DWT), and Arnold Map. The proposed method chooses only frames with maximum and minimum energy features to host the watermark. It also exploits the advantages of both the fast Walsh Hadamard transform (FWHT) and discrete wavelet transforms (DWT) for watermark embedding. The Arnold map encrypts watermarks before the embedding process and decrypts watermarks after extraction. The results show that the proposed method can achieve a fast embedding time, good transparency, and robustness against various

In this paper, two new simple, fast and efficient block matching algorithms are introduced, both methods begins blocks matching process from the image center block and moves across the blocks toward image boundaries. With each block, its motion vector is initialized using linear prediction that depending on the motion vectors of its neighbor blocks that are already scanned and their motion vectors are assessed. Also, a hybrid mechanism is introduced, it depends on mixing the proposed two predictive mechanisms with Exhaustive Search (ES) mechanism in order to gain matching accuracy near or similar to ES but with Search Time ST less than 80% of the ES. Also, it offers more control capability to reduce the search errors. The experimental tests

     Variations in perspective, illumination, motion blur, and weatherworn degeneration of signs may all be essential factors in road-sign identification. The current research purpose is to evaluate the effectiveness of the image processing technique in detecting road signs as well as to find the appropriate threshold value range for doing so. The efficiency of the cascade object detector in detecting road signs was tested under variations of speed and threshold values. The suggested system involved using video data to calculate the number of frames per second and creating an output file that contains the specified targets with their labels to use later in the final process (i.e., training stage). In the current research, two videos

Background Subtraction (BGS) is one of the main techniques used for moving object detection which further utilized in video analysis, especially in video surveillance systems. Practically, acquiring a robust background (reference) image is a real challenge due to the dynamic change in the scene. Hence, a key point to BGS is background modeling, in which a model is built and repeatedly used to reconstruct the background image.

From N frames the proposed method store N pixels at location(x,y) in a buffer, then it classify pixel intensity values at that buffer using a proposed online clustering model based on the idea of relative  run length, the cluster center with the highest frequency will be adopted as the background pixel

      In the last decade, 3D models gained interest in many applications, such as games, the medical field, and manufacture. It is necessary to protect these models from unauthorized copying, distribution, and editing. Digital watermarking is the best way to solve this problem. This paper introduces a robust watermarking method by embedding the watermark in the low-frequency domain, then selecting the coarsest level for embedding the watermark based on the strength factor. The invisibility of the watermark for the proposed algorithm is tested by using different measurements, such as HD and PSNR. The robustness was tested by using different types of attacks; the correlation coefficient was applied for the evaluati