Object recognition
The following outline is provided as an overview of and topical guide to object recognition:
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.
- Use example images (called templates or exemplars) of the objects to perform recognition
- Objects look different under varying conditions:
- A single exemplar is unlikely to succeed reliably. However, it is impossible to represent all appearances of an object.
1. Edge matching
2. Divide-and-Conquer search
3. Greyscale matching
4. Gradient matching
5. Histograms of receptive field responses
6. Large modelbases
- a search is used to find feasible matches between object features and image features.
- the primary constraint is that a single position of the object must account for all of the feasible matches.
- methods that extract features from the objects to be recognized and the images to be searched.
1. Interpretation trees
2. Hypothesize and test
3. Pose consistency
5. Invariance
7. Scale-invariant feature transform (SIFT)
8. Speeded Up Robust Features (SURF)
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.
Object recognition methods has the following applications:
...
Wikipedia