Patch Level

Patch-level analysis in image processing and computer vision focuses on extracting meaningful information from localized image regions, aiming to improve accuracy and efficiency in various tasks. Current research emphasizes the development of robust patch descriptors and classifiers, often employing deep learning architectures like convolutional neural networks (CNNs), Vision Transformers (ViTs), and Siamese networks, alongside techniques such as contrastive learning and feature fusion to enhance performance. This approach is proving valuable across diverse applications, including medical image analysis (e.g., cancer diagnosis and segmentation), remote sensing (e.g., land cover classification), and object recognition, where it addresses challenges like high-resolution image processing and limited labeled data.