Introduction to Convolutional Neural Networks (CNNs).pptx

MACHINE LEARNING – CONVOLUTIONAL NEURAL NETWORK

Introduction to Computer Vision  Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. - The British Machine Vision Association and Society for Pattern Recognition (BMVA) (or)  It is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. - Wikipedia 2

What is CNN(Convolution Neural Network) 3 ● It is a class of deep learning. ● Convolutional neural network (ConvNet’s or CNNs) is one of the main categories to do images recognition, images classifications, objects detections, recognition faces etc., ● It is similar to the basic neural network. CNN also have learnable parameter like neural network i.e., weights, biases etc. ● CNN is heavily used in computer vision ● There 3 basic components to define CNN ○ The Convolution Layer ○ The Pooling Layer ○ The Output Layer (or) Fully Connected Layer

Basic Structure of CNN • Input Layer: Accepts input images as pixel data. • Convolutional Layer: Applies filters to extract features. • ReLU Layer: Introduces non-linearity to the network. • Pooling Layer: Reduces spatial dimensions of feature maps. • Fully Connected Layer: Final layer for classification.

Convolutional Layer • Filters/Kernels: Detect specific features in input images. • Stride: Controls the movement of filters across the input. • Padding: Adds pixels around the input to maintain dimensions. • Output: Produces feature maps indicating detected features.

Convolution Layer 7 Images source: Analytics Vidhya

Padding in CNN • Zero Padding: Adds zeros around the input image to preserve dimensions. • Valid Padding: No padding, reduces the size of output feature maps. • Role: Helps preserve edge information during convolution.

9 The concept of stride : ● The weight of a matrix moves 1 pixel at a time is called as stride 1 (as we did in above case). What if we increase the stride value? Images source: Analytics

10 • As we can see in above image the increase in the stride value decreases the size of the image (which may cause in losing the features of the image). • Padding the input image across it solves our problem, we add more than one layer of zeros around the image in case of higher stride values. Images source: Analytics

11 • when the input of 6x6 is padded around with zeros we get the output with same dimensions of 6x6 this is known as ‘Same Padding’. ● The middle 4x4 pixel remains the same, here we have retained the more information from borders and also preserved the size of image. Images source: Analytics

Pooling Layer • Purpose: Reduces dimensionality and computation in the network. • Max Pooling: Selects the maximum value from each pooling region. • Average Pooling: Takes the average value from each pooling region. • Impact: Retains important features while reducing overfitting.

Basic Mathematics of CNN (B&W Image) • Convolution: Applies a filter matrix across the image to detect features. • Example: Sliding a 3x3 filter over a grayscale image, producing a feature map. • ReLU: Applies non-linearity after convolution. • Pooling: Reduces the size of the resulting feature map.

Basic Mathematics of CNN (Colored Image) • Convolution: Applies the same filter across each RGB channel. • Result: Produces a combined feature map from all channels. • Example: Sliding a filter across an RGB image and summing up feature maps. • Pooling: Reduces the size of the resulting feature map while preserving important information.

Fully Connected Layer • Purpose: Flattens the output and connects to a fully connected layer. • Function: Combines features for final classification. • Uses: Softmax or sigmoid activation functions for output.

Types of CNN ● Based on the problems, we have the different CNN’s which are used in computer vision. ● The five major computer vision techniques which can be addressed using CNN. ■ Image Classification ■ Object Detection ■ Object Tracking ■ Semantic Segmentation ■ Instance Segmentation 16

Types of CNN Image Classification: ● In an image classification we can use the traditional CNN models or there also many architectures designed by developers to decrease the error rate and increasing the trainable parameters. ■ LeNet (1998) ■ AlexNet (2012) ■ ZFNet (2013) ■ GoogLeNet19 (2014) ■ VGGNet 16 (2014) 17

LeNet-5 Architecture • Designed for handwritten digit recognition (MNIST dataset). • Structure: 2 convolutional layers, 2 subsampling layers, 2 fully connected layers. • Key Feature: Simple and efficient, early CNN model.

AlexNet Architecture • Winner of the ImageNet competition in 2012. • Structure: 5 convolutional layers, 3 fully connected layers. • Features: Uses ReLU, dropout, and data augmentation. • Impact: Revolutionized deep learning and computer vision.

VGG-16 Architecture • Uses 16 layers (13 convolutional, 3 fully connected). • Features: Smaller filters (3x3) with deeper networks. • Strength: Achieves high accuracy with a simple structure.

ResNet Architecture • Introduces Residual Learning to combat vanishing gradients. • Structure: Skip connections or shortcuts between layers. • Impact: Allows very deep networks (e.g., ResNet-50, ResNet-101).

Inception (GoogLeNet) Architecture • Introduces Inception modules: parallel convolutional filters. • Structure: Multiple filter sizes (1x1, 3x3, 5x5) in parallel. • Impact: Efficient and scalable for large-scale image recognition.

Transfer Learning • Concept: Uses a pre-trained model on a new but related task. • Benefits: Speeds up training, requires less data, and improves performance. • Example: Using a pre-trained model like ResNet for a new image classification task.

Object Localization • Purpose: Identifies the location of objects within an image. • Methods: Bounding box regression, Region Proposal Networks (RPNs). • Applications: Object detection, image segmentation.

Landmark Detection • Definition: Detects specific key points or landmarks within an image. • Applications: Facial recognition, medical imaging (e.g., key anatomical points). • Methods: CNNs used to detect and regress the position of landmarks.

Applications of Computer Vision ● Computer vision, an AI technology that allows computers to understand and label images, is now used in convenience stores, driverless car testing, daily medical diagnostics, and in monitoring the health of crops and livestock. ● Different use cases found in the computer vision as follows ■ Retail and Retail Security ■ Automotive ■ Healthcare ■ Banking ■ Agriculture 26

Conclusion • CNNs have revolutionized computer vision tasks. • Architectures like LeNet, AlexNet, VGG, ResNet, and Inception paved the way for modern image processing. • Transfer learning, object localization, and landmark detection expand the versatility of CNNs.

Introduction to Convolutional Neural Networks (CNNs).pptx

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