Traditional Computer Vision

Traditional computer vision focuses on developing algorithms that enable computers to "see" and interpret images and videos, mimicking human visual perception. Current research emphasizes improving the robustness and efficiency of these algorithms, particularly through the integration of deep learning models like Mask R-CNN and MobileNet, as well as exploring novel approaches such as self-supervised learning and spiking neural networks for specific applications. This field is crucial for numerous applications, including autonomous driving, industrial automation, medical imaging, and augmented reality, driving advancements in both scientific understanding of visual perception and practical technological solutions.