This project has the goal of introducing and exploring the different feature detectors, descriptors, and matchers available in OpenCV. A simple framework is provided to handle feature detection and matching and several sample applications using image features are then presented.
- OpenCV 2.4.9
- libconfig++
Simply run make on the base directory to build the sample applications.
Several sample applications are made available in order to explore possible use cases using image features in OpenCV.
Presents some of the main functionalities of the framework.
Identifies an object and detects it across several images. Based on the tutorial available in the following site.
Run the program with the following command:
objectDetect <path to object image> <path to test dir>
(i.e.) objectDetect ./data/box.png ./data/objectInScene
Application to identify the frames from a video stream where there is insignificant changes in the scene represented. Possibly useful as a preemptive step to eliminate frames from further processing in a more complex program.
Explore the applicability of image features against other image characteristics.
Program to identify the entity represented in an image. First, image templates are defined as representatives of each of the entities considered, then, a test set is matched against the templates.
Two use cases are provided:
* Identify monuments across different views (entityIdentifier data/findMonuments.cfg);
* Identify movies from different poster representations (entityIdentifier data/findMovies.cfg).
Further work should be explored in the decision process for identifying each match and in the efficiency of the overall process. As it is it can not scale to a real world database of entities. (See "Scalable Recognition with a Vocabulary Tree" for a possibility of a scalable image matching algorithm.)