"Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence."
Object recognition is the cognitive process by which we identify and categorize visual stimuli as familiar objects or entities.
Psychophysics: How physical stimuli are translated into perceptual experience, and how different stimuli are compared by the brain.
Computational systems and algorithms: Analyzing images and identifying features that are relevant to an object, and using these features to identify the object.
Machine learning: Training algorithms to recognize different objects by providing large datasets of images and the corresponding labels.
Neural networks: Simulating the structure of the brain to create artificial systems that are capable of recognizing objects.
Statistical models: Using probability theory to model the distribution of objects in the world, and predict which objects are most likely to be present in a given context.
Cognitive psychology: Understanding how people perceive objects and how this translates into behavior, attention, and decision making.
Visual attention: Understanding how the brain selectively attends to objects in a scene and how this affects object recognition.
Object permanence: Understanding how the brain tracks objects over time and across different viewpoints, and how this affects object recognition.
Object recognition in different sensory modalities: Understanding how the brain recognizes objects in different sensory modalities such as touch, hearing, and smell, as well as how this relates to vision.
Real-world applications: Understanding how object recognition is used in various real-world applications, including robotics, autonomous vehicles, medical imaging, and security systems.
Feature detection: This type of object recognition involves detecting basic visual features like edges, corners, and lines in an image.
Template matching: Here, an image is compared with a set of pre-existing templates to find a match, which makes it useful in detecting specific objects.
Edge detection: This method involves detecting boundaries between different regions in an image to segment them.
Color-based recognition: In this type of recognition, specific colors are used to identify and distinguish between different objects.
Texture recognition: This type focuses on identifying an object based on its unique textural patterns, such as roughness, smoothness, or grain.
Object categorization: This approach classifies objects into specific categories based on attributes such as size, shape, or function.
Object tracking: With the help of a camera, this method recognizes an object and tracks it as it moves through a scene.
Shape analysis: This method identifies the significant features of an object's shape and uses them to distinguish it from other shapes.
Object segmentation: By identifying and separating objects from their background, this method makes object recognition much easier.
Facial recognition: This type focuses on identifying specific facial features to distinguish between different individuals.
Object detection: This method identifies the presence of specific objects in an image or video by localizing the object using bounding boxes.
Motion recognition: This process involves identifying moving objects and tracking their motions to recognize them.
Semantic segmentation: This method analyzes an image using deep learning algorithms to identify and categorize every pixel across the image.
Pattern recognition: By detecting recurring patterns in an image, this method can identify specific objects.
Context-based recognition: This method focuses on recognizing objects based on their context, such as their surroundings or the events taking place in the scene.
"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."