Computer Vision and Image Processing

👁️Computer Vision and Image Processing Unit 6 – Machine Learning in Computer Vision

Machine learning in computer vision empowers computers to interpret and understand visual data. This unit covers key concepts, preprocessing techniques, feature extraction, and popular algorithms used in the field. Deep learning, particularly convolutional neural networks, has revolutionized computer vision tasks. The unit explores various architectures, transfer learning, and applications like object detection and semantic segmentation, along with performance evaluation metrics and real-world use cases.

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Key Concepts and Foundations

  • Computer Vision (CV) focuses on enabling computers to interpret, understand, and process visual data from the world
  • CV involves capturing, processing, analyzing, and understanding digital images or videos
  • Draws from various fields including mathematics, physics, signal processing, and artificial intelligence
  • Aims to automate tasks that the human visual system can perform (object recognition, scene understanding)
  • Key challenges include variations in lighting, viewpoint, scale, and occlusion
    • Occlusion occurs when objects are partially or fully hidden by other objects in the scene
  • CV systems often follow a pipeline: image acquisition, preprocessing, feature extraction, and high-level understanding
  • Low-level vision deals with early processing stages (filtering, edge detection) while high-level vision focuses on semantic interpretation (object recognition, scene understanding)

Machine Learning Basics for CV

  • Machine Learning (ML) involves building systems that can learn and improve from experience without being explicitly programmed
  • ML algorithms automatically learn patterns and rules from data to make predictions or decisions
  • Three main types of ML: supervised learning, unsupervised learning, and reinforcement learning
    • Supervised learning uses labeled data to train models for classification or regression tasks
    • Unsupervised learning discovers hidden patterns or structures in unlabeled data (clustering, dimensionality reduction)
    • Reinforcement learning trains agents to make decisions based on rewards and punishments from the environment
  • In CV, ML is used for tasks like image classification, object detection, and semantic segmentation
  • Training data consists of input images and corresponding labels or annotations
  • Models learn to map input features to output predictions by minimizing a loss function that measures the difference between predicted and true outputs
  • Overfitting occurs when models memorize training data and fail to generalize to new data
    • Regularization techniques (L1/L2 regularization, dropout) can help prevent overfitting

Image Preprocessing Techniques

  • Preprocessing aims to enhance image quality, remove noise, and standardize data for further analysis
  • Image resizing adjusts the spatial dimensions of an image while maintaining its aspect ratio
    • Resizing is often necessary to match the input size of ML models or to reduce computational complexity
  • Image normalization scales pixel values to a standard range (e.g., [0, 1] or [-1, 1]) to improve convergence during training
  • Contrast enhancement techniques (histogram equalization, gamma correction) improve the visual appearance and highlight important details
  • Noise reduction methods (median filtering, Gaussian smoothing) remove unwanted noise while preserving edges and structures
  • Data augmentation artificially increases the training set by applying random transformations (rotation, flipping, cropping) to existing images
    • Augmentation helps improve model robustness and generalization to new data
  • Image segmentation separates an image into multiple segments or regions based on pixel characteristics (color, texture, intensity)

Feature Extraction and Representation

  • Features are distinctive properties or patterns in an image that can be used for recognition or matching
  • Hand-crafted features are manually designed based on domain knowledge (SIFT, HOG, LBP)
    • Scale-Invariant Feature Transform (SIFT) detects and describes local features that are invariant to scale and rotation
    • Histogram of Oriented Gradients (HOG) captures the distribution of gradient orientations in local regions
    • Local Binary Patterns (LBP) encodes local texture information by comparing each pixel with its neighbors
  • Learned features are automatically discovered by ML models during training (CNN features)
  • Convolutional Neural Networks (CNNs) learn hierarchical features from raw pixel data
    • Lower layers capture simple patterns (edges, corners) while higher layers capture more complex and abstract features (objects, parts)
  • Feature descriptors compactly represent the extracted features for efficient storage and comparison
    • Bag-of-Visual-Words (BoVW) quantizes local features into a fixed-size vocabulary and represents images as histograms of visual word occurrences
  • Dimensionality reduction techniques (PCA, t-SNE) project high-dimensional features into a lower-dimensional space while preserving important information
  • Support Vector Machines (SVM) find the optimal hyperplane that maximally separates different classes in a high-dimensional feature space
    • SVMs are effective for binary classification tasks and can handle non-linearly separable data using kernel tricks
  • Random Forests (RF) are ensemble models that combine multiple decision trees trained on random subsets of features and data
    • RFs are robust to overfitting and can handle high-dimensional data with correlated features
  • K-Nearest Neighbors (KNN) classifies a new sample based on the majority class of its k nearest neighbors in the feature space
    • KNN is a non-parametric method that requires no training but can be computationally expensive for large datasets
  • Naive Bayes (NB) is a probabilistic classifier that assumes independence between features given the class label
    • NB is simple, fast, and works well with high-dimensional data but may underperform when the independence assumption is violated
  • Logistic Regression (LR) models the probability of a binary outcome as a linear function of input features
    • LR is often used as a baseline model and can be extended to multi-class problems using one-vs-all or softmax approaches
  • Decision Trees (DT) recursively partition the feature space based on the most informative features at each node
    • DTs are interpretable and can handle both numerical and categorical features but may overfit if grown too deep

Deep Learning for Computer Vision

  • Deep Learning (DL) refers to ML models with multiple layers that can learn hierarchical representations from raw data
  • Convolutional Neural Networks (CNNs) are the most popular DL architecture for CV tasks
    • CNNs consist of convolutional layers that learn local features, pooling layers that downsample the feature maps, and fully connected layers that perform classification or regression
    • Popular CNN architectures include LeNet, AlexNet, VGGNet, GoogLeNet, and ResNet
  • Transfer Learning leverages pre-trained models on large datasets (ImageNet) to solve related tasks with limited training data
    • The pre-trained model can be used as a feature extractor or fine-tuned on the target task
  • Object Detection aims to localize and classify multiple objects in an image
    • Region-based methods (R-CNN, Fast R-CNN, Faster R-CNN) generate object proposals and then classify each proposal
    • Single-shot methods (YOLO, SSD) directly predict object bounding boxes and classes in a single forward pass
  • Semantic Segmentation assigns a class label to each pixel in an image
    • Fully Convolutional Networks (FCN) adapt CNNs for dense pixel-wise prediction by replacing fully connected layers with convolutional layers
    • U-Net is a popular architecture for biomedical image segmentation that uses skip connections to combine high-level and low-level features

Performance Evaluation and Metrics

  • Evaluation metrics quantify the performance of ML models on test data
  • Accuracy measures the overall correctness of predictions but can be misleading for imbalanced datasets
    • Accuracy = (True Positives + True Negatives) / (Total Samples)
  • Precision measures the proportion of true positive predictions among all positive predictions
    • Precision = True Positives / (True Positives + False Positives)
  • Recall measures the proportion of true positive predictions among all actual positive samples
    • Recall = True Positives / (True Positives + False Negatives)
  • F1 Score is the harmonic mean of precision and recall, providing a balanced measure of model performance
    • F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
  • Intersection over Union (IoU) measures the overlap between predicted and ground truth bounding boxes or segmentation masks
    • IoU = Area of Intersection / Area of Union
  • Mean Average Precision (mAP) is a common metric for object detection that computes the average precision across different recall levels and object classes
  • Confusion Matrix summarizes the model's performance in a table, showing the counts of true positives, true negatives, false positives, and false negatives for each class

Practical Applications and Case Studies

  • Face Recognition involves identifying or verifying individuals based on their facial features
    • Applications include security systems, surveillance, and social media tagging
  • Autonomous Vehicles rely on CV techniques for tasks like lane detection, obstacle avoidance, and traffic sign recognition
    • Cameras, LiDAR, and other sensors provide visual data for perception and decision-making
  • Medical Image Analysis applies CV to diagnose diseases, segment anatomical structures, and guide surgical procedures
    • Examples include tumor detection in MRI scans, retinal image analysis for diabetic retinopathy, and cell segmentation in microscopy images
  • Augmented Reality (AR) overlays virtual content on top of the real world using CV techniques like object tracking and pose estimation
    • AR applications range from gaming (Pokémon Go) to education (interactive learning experiences) and industrial maintenance (remote assistance)
  • Retail and E-commerce use CV for product recognition, visual search, and recommendation systems
    • Customers can upload images to find similar products or receive personalized recommendations based on their visual preferences
  • Agriculture and Precision Farming employ CV to monitor crop health, detect pests and diseases, and optimize resource allocation
    • Drones equipped with cameras can capture aerial imagery for analysis and decision support
  • Robotics and Manufacturing integrate CV for tasks like quality inspection, defect detection, and object grasping
    • CV enables robots to perceive and interact with their environment in real-time


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© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.
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