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.