Edge Based

Edge-based methods are gaining traction across diverse fields, focusing on leveraging edge information for improved performance in tasks ranging from image processing and object recognition to 3D point cloud understanding and medical diagnostics. Current research emphasizes developing efficient algorithms and model architectures, such as those employing local Laplacian filtering or incorporating edge features into deep learning models, to enhance accuracy and reduce computational costs, particularly for resource-constrained edge devices. This work is significant because it enables the deployment of sophisticated algorithms in applications where computational power is limited, leading to advancements in areas like robotics, autonomous systems, and remote healthcare.