A-Z of Machine Learning and Computer Vision Terms

AI Assisted Labeling

Active Learning

Anomaly Detection

Artificial Intelligence (AI)

Backpropagation

Batch Normalization

Bayesian Network

Binary Classification

Calibration Curve

Canonical Correlation Analysis (CCA)

Case-Based Reasoning

Chain of Thought (CoT)

Chi-Squared Automatic Interaction Detection (CHAID)

Class Boundary (Statistics & Machine Learning)

Class Imbalance

Collaborative Filtering

Computer Vision

Computer Vision Model

Conditional Random Field (CRF)

Confusion Matrix

Constrained Clustering

Contrastive Learning

Convolutional Neural Networks (CNNs)

Cross-Validation

Data Approximation

Data Augmentation

Data Operations

Data Pre-processing

Decision Boundary

Deep Neural Networks

Dimensionality Reduction

Dynamic and Event-Based Classifications

Embedding Spaces

Ensemble Learning

Expectation-Maximization Algorithm (EM)

Extreme Learning Machine

FP-Growth Algorithm

Factor Analysis

False Positive Rate

Feature Engineering

Feature Extraction

Feature Hashing

Feature Learning

Feature Scaling

Feature Selection

Few-shot Learning

Fisher’s Linear Discriminant

Foundation Models

Frames Per Second (FPS)

Fully Connected Layer

Generative Adversarial Network (GAN)

Generative Adversarial Networks

Generative Pre-Trained Transformer

Gradient Descent

I

Image Processing

Image processing broadly refers to the manipulation or analysis of images using algorithms. It is a field at the intersection of signal processing and computer vision. Classic image processing tasks include enhancement (contrast stretching, histogram equalization), filtering (blurring, sharpening, edge detection), geometric transformations (rotation, scaling, warping), noise reduction (denoising), compression, and color space conversions. It often involves pixel-wise operations or small neighborhood operations (using convolution masks). Unlike high-level computer vision, which tries to infer semantic information (like “there’s a cat”), image processing might be more about improving the image or extracting low-level features (like detecting edges or corners). Many image processing algorithms are deterministic and well-defined (like Canny for edges or median filter for noise removal). It’s foundational for preparing images for analysis or for improving visual quality.

Further Reading

🔗 Research Paper 📄 Blog Post 📄 Blog Post

Explore Our Products

Lightly One

Data Selection & Data Viewer

Get data insights and find the perfect selection strategy

Lightly Train

Self-Supervised Pretraining

Leverage self-supervised learning to pretrain models

Lightly Edge

Smart Data Capturing on Device

Find only the most valuable data directly on devide

Ready to Get Started?

Experience the power of automated data curation with Lightly

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)

Transformers (Transformer Networks)

Stream-Based Selective Sampling

Support Vector Machine (SVM)

Sentiment Analysis

Surrogate Model

Supervised Learning

Semi-supervised Learning

Selective Sampling

Sliding Window Attention

Sensitivity and Specificity of Machine Learning

Segment Anything Model (SAM)

Regularization Algorithms

ROC (Receiver Operating Characteristic) Curve

Scale Imbalance

Regression (Regression Analysis)

Region-Based CNN (R-CNN)

Recall (Sensitivity or True Positive Rate)

RAG Architecture

Query Synthesis Methods

Query Strategy (Active Learning)

Predictive Model Validation

Prompt Injection

Prompt Engineering

Prompt Chaining

Pose Estimation

Pool-Based Sampling

Pattern Recognition

Parameter-Efficient Fine-Tuning (Prefix-Tuning)

Pandas and NumPy

Panoptic Segmentation

PACS (Picture Archiving and Communication System)

Outlier Detection

Object Tracking

Optical Character Recognition (OCR)

One-Shot Learning

Object Localization

Object Detection

Natural Language Processing (NLP)

Neural Networks

Multi-Task Learning

Motion Detection

Motion Estimation

Model Validation

Latent Dirichlet Allocation (LDA)

Medical Image Segmentation

Model Parameters

Mean Squared Error (MSE)

Mean Average Precision (mAP)

Machine Learning (ML)

Linear Regression

Linear Discriminant Analysis (LDA)

Intersection over Union (IoU)

Interpretability

Imbalanced Dataset

Image Processing

Image Restoration

Image Segmentation

Image Recognition