Computer Vision

## **1. Image Processing Basics** - **Filters**: - **Description**: Filters are used to enhance or extract specific features from images. They are applied via convolution operations. - **Types of Filters**: - **Gaussian Filter**: Used for smoothing and noise reduction. - **Sobel Filter**: Detects edges in an image. - **Median Filter**: Reduces noise while preserving edges. - **Use Cases**: Noise reduction, edge enhancement, and image sharpening. - **Edge Detection**: - **Description**: Edge detection identifies points in an image where the brightness changes sharply, highlighting boundaries of objects within the image. - **Popular Algorithms**: - **Sobel Operator**: Detects vertical and horizontal edges by calculating gradient magnitude. - **Canny Edge Detector**: A multi-step algorithm for detecting a wide range of edges in images. - **Laplacian of Gaussian (LoG)**: Combines Gaussian filtering with Laplacian edge detection for detecting fine edges. - **Use Cases**: Object recognition, image segmentation, and computer vision tasks requiring shape recognition. - **Image Segmentation**: - **Description**: Image segmentation is the process of partitioning an image into multiple segments (superpixels) to simplify or change the representation of the image. - **Types of Segmentation**: - **Threshold-Based Segmentation**: Separates pixels based on intensity levels. - **Region-Based Segmentation**: Groups pixels into regions based on predefined criteria. - **Semantic Segmentation**: Labels each pixel of an image according to a predefined category. - **Use Cases**: Medical image analysis, object recognition, and self-driving car vision systems. #### **2. Object Detection** - **Convolutional Networks (CNNs)**: - **Description**: CNNs are a class of deep learning models specifically designed for analyzing visual data by learning spatial hierarchies of features. - **Key Components**: - **Convolutional Layers**: Automatically extract features like edges, textures, and objects. - **Pooling Layers**: Reduce the spatial dimensions of the feature maps, making the model more computationally efficient. - **Fully Connected Layers**: Used for classification tasks after feature extraction. - **Use Cases**: Image classification, face recognition, and video surveillance. - **YOLO (You Only Look Once)**: - **Description**: YOLO is a real-time object detection system that frames object detection as a single regression problem, predicting both bounding boxes and class probabilities directly from full images in one evaluation. - **How It Works**: YOLO divides the image into a grid and assigns bounding boxes to objects, predicting their class and location simultaneously. - **Use Cases**: Real-time object detection in video feeds, self-driving cars, and surveillance systems. - **R-CNN (Regions with Convolutional Neural Networks)**: - **Description**: R-CNN is a two-stage object detection algorithm that first generates region proposals and then uses CNNs to classify objects within those regions. - **Variants**: - **Fast R-CNN**: Improves speed and accuracy by using a single CNN for feature extraction. - **Faster R-CNN**: Introduces a Region Proposal Network (RPN) for faster object detection. - **Mask R-CNN**: Adds instance segmentation to object detection, creating pixel-wise masks for objects. - **Use Cases**: Object detection, facial recognition, and autonomous vehicle vision. #### **3. Image Generation** - **Generative Adversarial Networks (GANs)**: - **Description**: GANs are a class of machine learning frameworks consisting of two neural networks, a generator and a discriminator, that compete against each other. The generator creates fake images, while the discriminator tries to differentiate between real and fake images. - **How It Works**: The generator learns to produce increasingly realistic images by fooling the discriminator, while the discriminator improves its ability to detect fake images. - **Use Cases**: Image synthesis, video generation, deepfakes, and art generation. - **Autoencoders**: - **Description**: Autoencoders are neural networks used for unsupervised learning, primarily for dimensionality reduction and image reconstruction. They consist of an encoder that compresses input data and a decoder that reconstructs the original data. - **Variants**: - **Denoising Autoencoders**: Train to remove noise from images. - **Variational Autoencoders (VAEs)**: A generative model that can produce new images from a learned distribution. - **Use Cases**: Image compression, anomaly detection, and generating new samples from existing data.