"Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence."
The process of identifying and classifying objects within an image or video based on their features and characteristics.
Image processing: A branch of computer science dealing with processing digital images using mathematical algorithms.
Feature extraction: The process of identifying important attributes or features in an image, such as edges or shapes, in order to capture its essence.
Machine learning: The study of algorithms and statistical models that enable computer systems to learn from experience without being explicitly programmed.
Deep learning: A subfield of machine learning that uses artificial neural networks to learn from large amounts of data.
Convolutional neural networks: A type of deep neural network commonly used for image recognition tasks.
Object localization: The task of identifying the location of objects within an image.
Object segmentation: The process of dividing an image into multiple segments, each containing a distinct object or part of an object.
Feature detection: The process of identifying and extracting specific features of an image, such as corners or edges.
Clustering: A machine learning technique used to group similar objects together based on similarity metrics such as distance or similarity in feature space.
Template matching: A technique used to compare an image against predefined templates to identify matching patterns or objects.
Object detection: The ability to identify and localize specific objects within an image, often with the aid of bounding boxes or other annotations.
Scale-invariant feature transform (SIFT): A method for detecting and describing local features in an image that is invariant to scale, rotation and illumination changes.
Speeded-up robust feature (SURF): An algorithm for detecting and describing local features in images that is designed to be faster than SIFT.
Histograms of oriented gradients (HOG): A technique for extracting features from an image based on the orientation of gradient vectors in local regions of the image.
Support vector machines (SVM): A type of supervised learning algorithm used for classification or regression analysis that attempts to find a hyperplane that separates data points into distinct categories.
Principal component analysis (PCA): A technique used to reduce the dimensionality of data by identifying the most important features or components.
Random forests: A type of machine learning algorithm that builds many decision trees and combines their output to make predictions.
Transfer learning: A technique used to apply knowledge learned from one task or domain to another, often used in object recognition to pretrain models on large datasets before fine-tuning on smaller, more specialized datasets.
Data augmentation: The process of applying transformations or perturbations to existing data in order to generate additional training examples and improve model robustness.
Evaluation metrics: Metrics used to assess the performance of object recognition models, such as precision, recall, F1 score, or mean average precision (mAP).
Face recognition: The ability to identify and recognize faces in images or videos.
Object detection: The ability to identify specific objects in an image or a video.
Image recognition: The ability to identify the content of images accurately.
Pattern recognition: The ability to identify patterns in an image, audio or data.
Semantic segmentation: The ability to assign each pixel of an image to a specific object class.
Instance segmentation: The ability to assign each instance of an object in an image to a specific object class.
Motion recognition: The ability to identify and track movements in a video.
Action recognition: The ability to identify and classify actions performed by humans or objects in a video.
Scene recognition: The ability to recognize the context of an image or video and identify the scene present in the image.
Recognition of handwritten characters: The ability to recognize and convert hand-written text into machine-readable format.
Object tracking: The ability to track and follow moving objects in real-time.
3D recognition: The ability to recognize and identify objects in a 3-dimensional space.
"Humans recognize a multitude of objects in images with little effort."
"...despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated."
"Objects can even be recognized when they are partially obstructed from view."
"This task is still a challenge for computer vision systems."
"Many approaches to the task have been implemented over multiple decades."