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ABSTRACT
In the field of computer vision, image processing stands out as a critical area of study, encompassing various techniques aimed at analyzing signals where the input is an image, such as images or frames from a video. Notably, feature selection plays a pivotal role in image processing, influencing the outcomes of the applied algorithms. The results of image processing efforts can manifest as enhanced images, or a set of parameters and characteristics associated with the processed image.
While digital image processing is the predominant focus, it is noteworthy that analog and optical image processing techniques are also viable options. The core objective is to develop image processing techniques capable of detecting and manipulating images within both static images and live video streams, utilizing conventional signal-processing methodologies.
Image acquisition is inherently tied to imaging, where algorithms predominantly concentrate on tracking mechanisms to ensure the smooth progression of video sequences. Conversely, some methodologies leverage pre-existing information about image attributes such as color, shape, and texture. This paper delves into a comprehensive investigation and review of prior approaches to image tracking and detection, with a particular emphasis on video sequences.
