Fast R-CNN, GANs, Edge Detection and Core Image Processing Concepts

Fast R-CNN Multi-Stage Architecture and Benefits

Q. Explain the multi-stage architecture of Fast R-CNN and how it improves upon R-CNN.

Definition: Region-based Convolutional Neural Network
Fast R-CNN is an object-detection algorithm that improves R-CNN by using a single CNN and a multi-stage training architecture for faster and more accurate detection.

Multi-Stage Architecture of Fast R-CNN

Fast R-CNN works in the following stages:

Input Image
– The whole image is given as input once.

Shared Convolutional Layers
– A single CNN extracts feature maps from the entire image.
– These features are shared for all regions.

Region of Interest (RoI) Pooling
– Region proposals are applied on feature maps.
– RoI pooling converts all regions into fixed-size feature vectors.

Fully Connected Layers
– Extract high-level features from RoI outputs.

Two Output Layers

• Softmax layer → object classification
• Bounding-box regression → location refinement

Fast R-CNN vs R-CNN Improvements

Advantages of Fast R-CNN

• Faster training and testing
• Less memory usage
• Higher detection accuracy

Generator and Discriminator Roles in GANs

Q. Describe the role of a Generator and a Discriminator in a GAN.

GAN (Generative Adversarial Network):
A Generative Adversarial Network (GAN) is a deep-learning framework composed of two neural networks: a Generator and a Discriminator. The generator creates fake data samples, while the discriminator evaluates whether the samples are real or fake. Both networks are trained simultaneously in an adversarial manner. This competition improves the quality of generated data over time.

Generator (G): Role and Functions

Role / Functions:

• Takes random noise as input
• Generates synthetic images (fake data)
• Tries to fool the discriminator
• Improves quality through training

Goal: Produce data that looks realistic.

Discriminator (D): Role and Functions

Role / Functions:

• Takes real and fake images as input
• Checks authenticity of data
• Provides feedback to the generator
• Acts like a binary classifier

Goal: Correctly identify real vs. fake data.

How GANs Work

1. The generator creates fake images.
2. The discriminator evaluates them.
3. Both networks improve through adversarial competition.

GAN Advantages and Applications

• Generates high-quality images
• Useful for data augmentation
• Used in image synthesis and super-resolution

Prewitt, Sobel, and Canny Edge Detection

B) Explain the concept of Prewitt, Sobel, and Canny edge detection methods.

Edge Detection: Short Introduction

Edge detection is a technique used to find boundaries in an image where intensity changes sharply.

Prewitt Edge Detection

Concept: Prewitt uses first-order derivatives to detect edges by calculating intensity changes.

Key Points:

• Uses horizontal and vertical masks
• Simple and fast method
• Sensitive to noise

Use: Basic edge detection.

Sobel Edge Detection

Concept: Sobel improves Prewitt by adding weighting to central pixels.

Key Points:

• Uses weighted masks
• Better noise handling than Prewitt
• Detects horizontal and vertical edges

Use: Better edge detection than Prewitt.

Canny Edge Detection

Concept: Canny is a multi-stage edge detection algorithm that gives accurate and thin edges.

Key Steps:

1. Noise reduction (Gaussian filter)
2. Gradient calculation
3. Non-maximum suppression
4. Double thresholding and edge tracking

Use: High-accuracy edge detection.

Region-based vs Edge-based Segmentation

C) Describe the differences between region-based segmentation and edge-based segmentation.

Segmentation Basics

Segmentation (Main techniques: • Region growing • Region splitting • Region merging)

Image segmentation is the process of dividing an image into meaningful regions based on pixel characteristics such as intensity, color, or texture. It helps in identifying objects and their boundaries in an image. Segmentation simplifies image analysis and is widely used in object detection and image understanding. Based on the technique used, segmentation is mainly classified into region-based and edge-based segmentation.

Region-based Segmentation

Concept: Segments image based on similarity of pixel values.

Key Points:

• Uses intensity similarity
• Produces connected regions
• More accurate but slower

Examples: Region growing, split & merge.

Edge-based Segmentation

Concept: Segments image by detecting edges or boundaries.

Key Points:

• Uses intensity discontinuity
• Fast method
• Sensitive to noise

Examples: Sobel, Canny.

Segmentation Differences

Image Representation in Computer Vision

Explain the concept of Image Representation and its importance in Computer Vision

Image Representation:
In computer vision, an image is represented as a 2-D matrix of pixels, where each pixel stores an intensity value. For a grayscale image, each pixel has one value, while for a color image, three values (Red, Green, Blue) are used.

Key Points:

• Digital image = collection of pixels
• Pixel value represents brightness or color
• Grayscale → single matrix
• RGB image → three matrices (R, G, B)

Importance in Computer Vision:

• Makes images suitable for computer processing
• Helps in image enhancement and filtering
• Required for feature extraction and object detection
• Useful in storage, transmission, and analysis

DFT vs DCT

DFT and DCT are frequency-domain transforms used to represent an image in terms of frequency components.

SIFT Algorithm Steps

Explain SIFT Algorithm: SIFT (Scale Invariant Feature Transform) is a feature-detection algorithm used to detect and describe local features in an image. It is invariant to scale, rotation, and illumination changes.

Steps of SIFT Algorithm:

1. Scale-space construction – Image is blurred at different scales.
2. Keypoint detection – Stable points are detected.
3. Keypoint localization – Removes low-contrast points.
4. Orientation assignment – Assigns orientation to keypoints.
5. Keypoint descriptor – Generates feature vectors.

SIFT Advantages and Applications

• Scale and rotation invariant
• Robust to noise and illumination
• Accurate feature matching

Applications:

• Object recognition
• Image matching and stitching

YOLO Algorithm (You Only Look Once)

Explain YOLO Algorithm: YOLO is a real-time object detection algorithm that detects objects in a single forward pass of the network.

YOLO Working and Features

• Input image is divided into grid cells
• Each grid cell predicts bounding boxes
• Each box has class probability and confidence score
• Objects are detected in one pass

Key Features: Single-stage detection, very fast, suitable for real-time applications.

Advantages

• High speed
• End-to-end training
• Detects multiple objects at once

Applications

• Self-driving cars
• Surveillance systems
• Real-time video analysis

Walsh–Hadamard Transform and Uses

Walsh–Hadamard Transform (WHT) is a mathematical transform used to represent an image in the frequency domain using orthogonal square-wave functions. It uses only +1 and −1 values, so no multiplication is required, making WHT computationally simple and fast. It is mainly used in image processing and signal analysis.

Key Points:

• Uses square-wave functions
• No multiplication required
• Fast computation
• Simple and efficient transform

Applications:

• Image compression
• Image enhancement
• Pattern recognition
• Digital signal processing

Image → WHT → Transformed Image

Image Filtering

Image filtering is the process of modifying an image by enhancing important features or removing unwanted noise. It is done by applying a filter or mask over the image pixels. Filtering improves image quality and helps in further analysis. It is widely used in image enhancement and edge detection.

Purpose of Image Filtering:

• Noise reduction
• Image smoothing
• Edge enhancement

Types of Image Filtering

1. Spatial Domain Filtering

   • Mean filter
   • Median filter

2. Frequency Domain Filtering

   • Low-pass filter
   • High-pass filter

Applications

• Noise removal
• Edge detection
• Image enhancement

Diagram: Image + Filter → Output Image

Adaptive Histogram Equalization (AHE)

Adaptive Histogram Equalization (AHE) is an image-enhancement technique used to improve the local contrast of an image. Unlike global histogram equalization, AHE divides the image into small regions (tiles) and applies histogram equalization to each region separately. This helps in enhancing details in both dark and bright areas of the image. AHE is widely used where local details are important.

Working of AHE

1. Input image is divided into small blocks or regions
2. Histogram is calculated for each region
3. Histogram equalization is applied locally
4. Enhanced regions are combined to form the output image

Advantages of AHE

• Improves local contrast
• Enhances fine details
• Works well for non-uniform lighting

Limitations of AHE

• Amplifies noise
• High computational cost

Applications of AHE

• Medical image processing (X-ray, MRI)
• Satellite and remote sensing images
• Low-contrast image enhancement

Diagram: Image → Small Regions → Local HE → Enhanced Image

SVM, KNN and Random Forest Comparison

SVM, KNN and Random Forest are popular machine-learning algorithms used for classification and prediction tasks.

Convolutional Neural Network (CNN)

Define Convolutional Neural Network (CNN) and its role in image classification

A Convolutional Neural Network (CNN) is a type of deep-learning model designed to process grid-like data such as images. It automatically extracts features like edges, textures, and shapes from the image using convolutional layers. CNN reduces the need for manual feature extraction and works efficiently for large image datasets.

It is widely used in computer vision tasks like object detection, recognition, and image classification.

Role in Image Classification

1. Feature extraction: CNN extracts hierarchical features automatically.
2. Classification: Fully connected layers map features to image classes.
3. High accuracy: Learns complex patterns from images.
4. End-to-end learning: Input image → CNN → Class prediction.

Input Image → Conv → ReLU → Pooling → FC → Output Class

Noise Models and Image Restoration

Explain the concept of noise models and their importance in image restoration

Noise in images refers to unwanted random variations in pixel values that degrade image quality. Noise can be caused by sensor errors, transmission issues, or environmental factors. Noise models mathematically describe how noise affects images, helping in designing restoration techniques.

Common Noise Models

• Gaussian Noise – Random variations with a normal distribution
• Salt & Pepper Noise – Random black and white pixels
• Speckle Noise – Multiplicative noise common in radar images

Importance in Image Restoration:

1. Helps identify the type and characteristics of noise
2. Guides selection of denoising/filtering methods
3. Improves image quality for analysis and interpretation

Gray Level Co-occurrence Matrix (GLCM)

Explain the concept of the Gray Level Co-occurrence Matrix (GLCM) in texture analysis

GLCM is a statistical method used to study the spatial relationship between pixel intensities in an image. It calculates how often pairs of pixel values occur at a certain distance and orientation. GLCM helps in extracting texture features for image classification and analysis.

Key Features Extracted from GLCM

• Contrast – Difference between high and low intensity variations
• Energy – Sum of squared elements (uniformity)
• Homogeneity – Closeness of distribution to the diagonal
• Correlation – Linear dependency of grey levels

Applications: Texture classification, medical image analysis, remote sensing and pattern recognition.

Convolution in Image Processing

Q. What is Convolution in Image Processing?

Convolution is a mathematical operation used in image processing to combine an image with a filter (kernel) to extract features or modify the image. It calculates the weighted sum of neighboring pixels using the filter values. Convolution is used in smoothing, sharpening, edge detection, and feature extraction.

Key Points

• Input = Image + Kernel
• Output = Processed image
• Helps in highlighting or suppressing image details

Image Sharpening: Laplacian Filter

What is Image Sharpening? Explain using the Laplacian filter.

Image sharpening enhances edges and fine details in an image to make it clearer. It emphasizes high-frequency components and suppresses low-frequency regions. (Sharpening: Original Img → Laplacian Filter → Edge Map → Sharpened Img)

Laplacian Filter

The Laplacian is a second-order derivative filter that highlights regions of rapid intensity change (edges). The sharpened image is often formed as: Sharpened Image = Original Image + Laplacian Output.

Key Points:

• Detects edges and fine details
• Often used before segmentation or feature extraction

Contrast Stretching

Q. Explain Contrast Stretching

Contrast stretching (or normalization) is a technique that enhances the contrast of an image by expanding the intensity range of pixels. It maps low-intensity values to darker and high-intensity values to brighter regions.

Key Points:

• Improves visual quality of low-contrast images
• Simple linear mapping of pixel values
• Often used as a pre-processing step before analysis

(Low-contrast Image → Contrast Stretching → High-contrast Image)