Computer Vision (CS 704) - Semester 7 BTech CSE at NITV

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Overview

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Why it matters

Self-driving cars see the road. Medical AI diagnoses from X-rays. Face unlock on phones. Augmented reality apps. Computer Vision is teaching machines to see and understand the visual world.

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Placement relevance

CV Engineer roles at Tesla, NVIDIA, autonomous vehicle companies. Medical imaging startups. AR/VR companies (Meta, Apple). ₹30-65 LPA for CV specialists.

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Prerequisites for

Autonomous Vehicles · Medical Imaging · AR/VR Development · Robotics · Surveillance Systems · Visual AI Research

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Recommended books

Computer Vision: Algorithms and Applications by Richard Szeliski · Deep Learning for Computer Vision by Rajalingappaa Shanmugamani · Programming Computer Vision with Python by Jan Erik Solem · Multiple View Geometry in Computer Vision by Hartley and Zisserman

Curriculum — 4 Units

U1

Unit 1 · 7 Topics · 0% complete

Image Processing Fundamentals

⚡ Key Formulae

Convolution:G(x,y) = Σ Σ I(x+i, y+j) × K(i,j)

Sobel:Gx, Gy kernels for edge detection

Image Representation (RGB, Grayscale)

Filtering (Gaussian, Median)

Edge Detection (Sobel, Canny)

Morphological Operations

Histogram Equalization

Feature Extraction (SIFT, SURF, ORB)

Image Transformations

U2

Unit 2 · 7 Topics · 0% complete

Deep Learning for Vision

⚡ Key Formulae

IoU:Intersection over Union = Area(overlap) / Area(union)

Non-Max Suppression:Filter overlapping boxes (keep highest confidence)

CNNs for Image Classification

Transfer Learning (VGG, ResNet, Inception)

Data Augmentation Techniques

Object Detection (R-CNN, Fast R-CNN)

YOLO (You Only Look Once)

SSD (Single Shot Detector)

Bounding Box Regression

U3

Unit 3 · 7 Topics · 0% complete

Segmentation & Advanced Tasks

⚡ Key Formulae

Dice Coefficient:2×|A∩B| / (|A| + |B|) — segmentation metric

U-Net:Encoder-Decoder with skip connections

Semantic Segmentation

Instance Segmentation

U-Net Architecture

Mask R-CNN

Image Captioning

Visual Question Answering

Style Transfer

U4

Unit 4 · 7 Topics · 0% complete

Specialized Applications

⚡ Key Formulae

Face Recognition:Extract embeddings, compute similarity (cosine/Euclidean)

OCR Pipeline:Detection → Recognition → Post-processing

Face Detection & Recognition

Pose Estimation

Optical Character Recognition (OCR)

Image Generation (GANs, Diffusion)

3D Vision Basics

Video Analysis

Real-time Processing

Previous Year Questions

Unit 12023 · End Semester10 marks

Apply Canny edge detection on a given image. Explain the steps: Gaussian smoothing, gradient calculation, non-maximum suppression, hysteresis thresholding. Show intermediate results.

Unit 22023 · End Semester8 marks

Explain YOLO architecture for object detection. How does it differ from R-CNN? Calculate IoU for two bounding boxes: Box1(10,10,50,50), Box2(30,30,70,70).

Unit 32022 · End Semester6 marks

What is semantic segmentation? Explain U-Net architecture with a diagram. How is it different from object detection?

Exam Strategy

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Image processing algorithms

Canny edge detection, SIFT features, histogram equalization — know step-by-step procedures. Show intermediate images/matrices.

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Object detection formulas

IoU calculation, Non-Max Suppression, Anchor boxes. Practice bounding box math. Compare YOLO vs R-CNN (speed vs accuracy).

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Draw CNN architectures

U-Net, ResNet, YOLO diagrams expected. Label conv layers, pooling, skip connections. Specify filter sizes and output dimensions.