Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. 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.

Approaches based on CAD-like object models

Recognition by parts

Appearance-based methods

Edge matching

Divide-and-Conquer search

Greyscale matching

Gradient matching

Histograms of receptive field responses

Large modelbases

Feature-based methods

Interpretation trees

Hypothesize and test

Pose consistency

Pose clustering

Invariance

Geometric hashing

Scale-invariant feature transform (SIFT)

Speeded Up Robust Features (SURF)

Bag of words representations

Genetic algorithm

Genetic algorithms can operate without prior knowledge of a given dataset and can develop recognition procedures without human intervention. A recent project achieved 100 percent accuracy on the benchmark motorbike, face, airplane and car image datasets from Caltech and 99.4 percent accuracy on fish species image datasets.

Other approaches

Applications

Object recognition methods has the following applications:

Surveys