Python: The primary language for developing computer vision models, with libraries like OpenCV, TensorFlow, and PyTorch. C++: Often used for performance optimization in computer vision applications. MATLAB: Useful for prototyping and algorithm development in academia and industry. Image Processing: Skills in techniques like filtering, edge detection, and image transformations. Feature Detection and Matching: Understanding of algorithms like SIFT, SURF, and ORB for identifying and matching features. Object Detection: Knowledge of techniques such as YOLO, SSD, and Faster R-CNN for detecting objects within images. Image Segmentation: Experience with methods like U-Net, Mask R-CNN, and watershed algorithms for partitioning images into meaningful segments. Image Classification: Proficiency with CNNs (Convolutional Neural Networks) for categorizing images into classes. TensorFlow: Experience with TensorFlow for building and training deep learning models. PyTorch: Proficiency in PyTorch, particularly for dynamic computation and model development. Keras: Familiarity with Keras for building neural network models in a more user-friendly API.