A-Z of Machine Learning and Computer Vision Terms

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A
A
AI Agent
AI Agent
AI Assistants
AI Assistants
AI Assisted Labeling
AI Assisted Labeling
Active Learning
Active Learning
Algorithm
Algorithm
Anchor Boxes
Anchor Boxes
Anomaly Detection
Anomaly Detection
Artificial Intelligence (AI)
Artificial Intelligence (AI)
Attribute
Attribute
B
B
Backpropagation
Backpropagation
Bagging
Bagging
Batch
Batch
Batch Normalization
Batch Normalization
Bayesian Network
Bayesian Network
Bias
Bias
Big Data
Big Data
Binary Classification
Binary Classification
Blur
Blur
Boosting
Boosting
Bounding Box
Bounding Box
C
C
COCO
COCO
Calibration
Calibration
Calibration Curve
Calibration Curve
Canonical Correlation Analysis (CCA)
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Case-Based Reasoning
Chain of Thought (CoT)
Chain of Thought (CoT)
ChatGPT
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary (Statistics & Machine Learning)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Class Imbalance
Clustering
Clustering
Collaborative Filtering
Collaborative Filtering
Computer Vision
Computer Vision
Computer Vision Model
Computer Vision Model
Concept Drift
Concept Drift
Conditional Random Field (CRF)
Conditional Random Field (CRF)
Confusion Matrix
Confusion Matrix
Constrained Clustering
Constrained Clustering
Contrastive Learning
Contrastive Learning
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
Cross-Validation
Cross-Validation
D
D
DICOM
DICOM
Data Approximation
Data Approximation
Data Augmentation
Data Augmentation
Data Drift
Data Drift
Data Error
Data Error
Data Mining
Data Mining
Data Operations
Data Operations
Data Pre-processing
Data Pre-processing
Data Quality
Data Quality
Dataset
Dataset
Decision Boundary
Decision Boundary
Decision List
Decision List
Decision Stump
Decision Stump
Decision Tree
Decision Tree
Deep Learning
Deep Learning
Deep Neural Networks
Deep Neural Networks
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dropout
Dynamic and Event-Based Classifications
Dynamic and Event-Based Classifications
E
E
Edge Cases
Edge Cases
Edge Computing
Edge Computing
Edge Detection
Edge Detection
Elastic Net
Elastic Net
Embedding Spaces
Embedding Spaces
Ensemble Learning
Ensemble Learning
Epoch
Epoch
Expectation-Maximization Algorithm (EM)
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
Extreme Learning Machine
F
F
F1 Score
F1 Score
FP-Growth Algorithm
FP-Growth Algorithm
Factor Analysis
Factor Analysis
False Positive Rate
False Positive Rate
Feature
Feature
Feature Engineering
Feature Engineering
Feature Extraction
Feature Extraction
Feature Hashing
Feature Hashing
Feature Learning
Feature Learning
Feature Scaling
Feature Scaling
Feature Selection
Feature Selection
Feature Vector
Feature Vector
Few-shot Learning
Few-shot Learning
Fisher’s Linear Discriminant
Fisher’s Linear Discriminant
Foundation Models
Foundation Models
Frame Rate
Frame Rate
Frames Per Second (FPS)
Frames Per Second (FPS)
Fully Connected Layer
Fully Connected Layer
Fuzzy Logic
Fuzzy Logic
G
G
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Adversarial Networks
Generative Pre-Trained Transformer
Generative Pre-Trained Transformer
Ghost Frames
Ghost Frames
Gradient Descent
Gradient Descent
G

Greyscale

A greyscale (grayscale) image is an image in which each pixel carries intensity information (brightness) only, with no color (chrominance) information. Essentially, it’s composed of shades of gray, ranging from black (0 intensity) to white (maximum intensity). In an 8-bit grayscale image, each pixel is represented by a value from 0 to 255 indicating its lightness. Many computer vision algorithms, especially older ones, often start by converting images to grayscale to simplify processing since it reduces data from three channels (RGB) to one while preserving essential structural information. Humans perceive color, but for tasks like edge detection or object recognition, color may be secondary to intensity patterns. Greyscale images are also common in certain sensors (like depth cameras or certain medical imaging modalities). Converting to grayscale typically uses a weighted sum of the R, G, B values (like 0.299R + 0.587G + 0.114B, which reflects human brightness perception).

Further Reading

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Region Proposal Network (RPN)
Class Boundary (Statistics & Machine Learning)
Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN)
Large Language Model (LLM)
Chain of Thought (CoT)
Foundation Models
Semantic Segmentation
Variance (Model Variance)
XAI (Explainable AI)
YOLO (You Only Look Once)
Weight Decay (L2 Regularization)
Text Generation Inference
True Positive Rate (TPR)
Type II Error (False Negative)
Type I Error (False Positive)
Triplet Loss
Transformers (Transformer Networks)
Tabular Data
Stream-Based Selective Sampling
Synthetic Data
Training Data
Support Vector Machine (SVM)
Sentiment Analysis
Surrogate Model
Supervised Learning
Semi-supervised Learning
Selective Sampling
Sliding Window Attention
Sensitivity and Specificity of Machine Learning
Scikit-Learn
Segment Anything Model (SAM)
Regularization Algorithms
ROC (Receiver Operating Characteristic) Curve
Scale Imbalance
Responsible AI
Regression (Regression Analysis)
Region-Based CNN (R-CNN)
Recall (Sensitivity or True Positive Rate)
Random Forest
RAG Architecture
Query Synthesis Methods
Query Strategy (Active Learning)
Predictive Model Validation
Prompt Injection
Prompt Engineering
PyTorch
Prompt Chaining
PR AUC
Precision
Pooling
Pose Estimation
Pool-Based Sampling
Pattern Recognition
Perceptron
Pixel
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Pandas and NumPy
Panoptic Segmentation
PACS (Picture Archiving and Communication System)
Overfitting
Outlier Detection
Object Tracking
Optical Character Recognition (OCR)
One-Shot Learning
Object Localization
Object Detection
Normalization
Noise
NIfTI
Natural Language Processing (NLP)
Neural Networks
Multi-Task Learning
Motion Detection
Motion Estimation
Model Validation
Latent Dirichlet Allocation (LDA)
Medical Image Segmentation
Model Parameters
Model Accuracy
Micro-Models
Mean Squared Error (MSE)
Keypoints
Mean Average Precision (mAP)
Machine Learning (ML)
Loss Function
Linear Regression
Linear Discriminant Analysis (LDA)
LIDAR
Learning Rate
Label Errors
Label
Intersection over Union (IoU)
Interpolation
Interpretability
Imbalanced Dataset
Image Processing
Image Restoration
Image Segmentation
Image Recognition

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