Modern Computer Vision and Deep Learning for Images – CNNs, RNNs, 3D Vision, Detection by NPTEL

About this course

Computer vision has moved from handcrafted features to end-to-end learning systems that can recognize, segment, track, and reconstruct the world from pixels. This free online course guides you through that modern shift, connecting core image fundamentals with the deep learning techniques that power today’s products—from mobile cameras and autonomous systems to medical imaging and industrial inspection. You will build an intuitive understanding of what visual information means, how it is represented, and how models learn to extract it reliably under real-world variability.

You will start by grounding deep learning in the essential building blocks: neurons, multilayer networks, losses, gradients, and backpropagation. Along the way, you will learn why training can be challenging, how optimization choices influence convergence, and how regularization techniques help models generalize beyond the training set. Preprocessing and activation/initialization decisions are treated as practical levers that affect stability and performance, helping you reason about why a network works, not just how to run it.

From there, you will connect classic and modern vision. You will revisit low-level methods such as filtering, edges, corners, blobs, and descriptors, and see how these ideas relate to learned representations. You will also study CNNs in depth, understand what convolutional channels capture, and explore influential architectural concepts that shaped performance breakthroughs. Sequence modeling is introduced through RNNs and LSTMs, including encoder–decoder ideas used in tasks that connect images and language.

The course expands to geometric vision and 3D perception, covering the intuition behind homographies, camera intrinsics, two-view stereo, epipolar geometry, and structure from motion, culminating in concepts like bundle adjustment and dense reconstruction. Finally, you will bridge to mid-level and high-level tasks such as optical flow, segmentation, and object detection, linking deep networks to the practical goals of locating, classifying, and delineating objects in complex scenes.

To reinforce learning, you will encounter frequent conceptual checks that sharpen your reasoning about model capacity, training dynamics, and the assumptions behind vision pipelines. By the end, you will be able to choose appropriate approaches for common vision problems, explain key trade-offs, and follow the technical language used in modern computer vision and machine learning work.