Computer vision has evolved significantly, transitioning from classical techniques based on handcrafted features to deep learning models that learn representations automatically. This article compares classical and deep learning approaches in terms of feature extraction, model complexity, computational efficiency, and application performance. While classical methods like Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and edge detection have been effective in controlled environments, deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated superior adaptability in complex real-world scenarios. The discussion highlights their advantages, limitations, and future directions in computer vision research.

Computer vision, classical methods, deep learning, CNN, feature extraction, image processing, SIFT, HOG, Res Net, AI.

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