The recent innovations in real-time video and image enhancements are allowing much advancement in a wide range of diverse applications. These innovations and advancements provide a new hardware architecture which aims to improve the image visualization, processing speed, and complexity reduction in hardware. The imaging chip concept is introduced in this article to support the Multiprocessing system on chip (MPSoC) applications in real-time scenarios on a single chip. The imaging chip model is designed using high-speed interface protocol, which includes different image enhancement algorithms acts as a master model, Advanced Extensible Interface (AXI)-4 as an interface model, and dual-port memory as a slave model. The image enhancement algorithm includes mainly, Brightness control, contrast stretching, Adaptive median filtering (AMF), Edge-detection techniques, image Thresholding, and Image Histogram method. The AXI-4 provides a high-speed interface for communicating master and slave modules. The proposed model work based on the modes of the operation to process the enhanced image output in MPSoC. The design supports multiple masters and multiple slave modules with reconfigurable nature. The imaging chip is a module on the Xilinx ISE environment and implemented on Artix-7 FPGA, along with the performance metrics like chip Area, time, power, and memory utilization are analyzed with improvements. The model offers low latency and high throughput architecture for real-time Multimedia applications.
Abstract-Animals leaving the forest area and entering the human habitat is increasing day by day. Animals entering the agricultural areas placed near the forest destroy crops or even attack on people therefore there is a need of system which detects the animal presence and gives warning about that in the view of security purpose. In this paper wild animal which enters the human habitation are recognized. The movement of animals in the input video is detected using Foreground detector algorithm. Extraction of foreground animal is done using Back ground subtraction based Gaussian mixture model. Morphological filters are used to remove the noise from binary image obtained from background subtraction. Training and recognition is done using back propagation algorithm. If trained images are matched with the test image, then the animal is recognized. The project aims to safe guard the wild life and animal life and it is a great help to forest department.
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