Object recognition is a 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.

A qualitative way to consider the complexity of the object recognition task would consider the following factors:

• Scene constancy: The scene complexity will depend on whether the images are acquired in similar conditions (illumination, background, camera parameters, and viewpoint ) as the models.
• Image-models spaces: In some applications, images may be obtained such that three-dimensional objects can be considered two-dimensional.
• Number of objects: If the number of objects is very small, one may not need the hypothesis formation stage. A sequential exhaustive matching may be acceptable. Hypothesis formation becomes important for a large number of objects.
• Occlusion: If there is only one object in an image, it may be completely visible. With an increase in the number of objects in the image, the probability of occlusion increases. Occlusion results in the absence of expected features and the generation of unexpected features.