Key Terms in Computer Vision

Core Concepts

• Image Processing: Manipulating digital images to enhance or extract information.
• Computer Vision: Teaching computers to interpret and understand visual information.
• Feature Extraction: Identifying essential characteristics of an image.
• Feature Matching: Comparing features between images.
• Image Segmentation: Dividing an image into meaningful regions.
• Pixel: Smallest unit of a digital image.
• Image Resolution: Number of pixels in an image.
• Color Space: Model describing colors and how they relate.
• Image Noise: Random variation of brightness or color in an image.
• Image Enhancement: Improving image quality for better perception.
• Edge Detection: Finding boundaries between regions in an image.
• Corner Detection: Identifying points of significant curvature.
• Blob Detection: Identifying regions with similar properties.
• Texture Analysis: Describing image patterns.
• Shape Analysis: Analyzing the geometric properties of objects.

Object Recognition and Analysis

• Object Recognition: Identifying and classifying objects in an image.
• Object Detection: Locating and identifying objects within an image.
• Object Tracking: Following objects over time in a video sequence.
• Image Classification: Assigning an image to a predefined category.
• Image Retrieval: Finding images similar to a query image.
• Image Segmentation: Dividing an image into meaningful regions.
• Image Registration: Aligning multiple images to a common coordinate system.
• Stereo Vision: Creating depth information from two images.
• Optical Flow: Estimating motion between image frames.
• Motion Estimation: Determining the movement of objects in a scene.

Deep Learning

• Convolutional Neural Network (CNN): Neural network architecture for image analysis.
• Feature Maps: Output of a convolutional layer.
• Pooling: Reducing the dimensionality of feature maps.
• Fully Connected Layer: Layer connecting all neurons in one layer to all neurons in the
  next.
• Overfitting: Model performs well on training data but poorly on new data.
• Activation Function: Introduces non-linearity to neural networks.
• Backpropagation: Algorithm to adjust weights in a neural network.
• Batch Normalization: Normalizes inputs to a layer for faster training.
• Dropout: Regularization technique to prevent overfitting.
• Hyperparameters: Parameters set before training, not learned.

Image Processing Techniques

• Histogram Equalization: Adjusting image contrast.
• Morphological Operations: Processing images based on shape.
• Canny Edge Detector: Edge detection algorithm.
• Harris Corner Detector: Corner detection algorithm.
• SIFT (Scale-Invariant Feature Transform): Feature detection and description
  algorithm.
• SURF (Speeded-Up Robust Features): Feature detection and description algorithm.
• HOG (Histogram of Oriented Gradients): Feature descriptor for object detection.
• LBP (Local Binary Patterns): Texture descriptor.
• Gabor Filters: Texture analysis.
• Fourier Transform: Frequency domain representation of an image.

Evaluation Metrics

• Precision: Ratio of correct positive predictions to total positive predictions.
• Recall: Ratio of correct positive predictions to actual positive cases.
• Accuracy: Overall correctness of a model.
• F1-Score: Harmonic mean of precision and recall.
• Mean Average Precision (mAP): Evaluation metric for object detection.
• Intersection over Union (IoU): Overlap between predicted and ground truth bounding
  boxes.
• False Positive Rate (FPR): Ratio of false positives to total negatives.
• False Negative Rate (FNR): Ratio of false negatives to total positives.
• True Positive Rate (TPR): Ratio of true positives to total positives.
• True Negative Rate (TNR): Ratio of true negatives to total negatives.