Point feature matching
In image processing, point feature matching is an effective method to detect a specified target in a cluttered scene. This method detects single objects rather than multiple objects. For instance, by using this method, one can recognize one specific person in a cluttered scene, but not any other person.
The algorithm is based on comparing and analyzing point correspondences between the reference image and the target image. If any part of the cluttered scene shares correspondences greater than the threshold, that part of the cluttered scene image is targeted and considered to include the reference object there.[1]
Video stabilization
In addition to object detection, point feature also helps in improving video stabilization. To achieve this, it usually follows these steps: reading frames, identifying salient points, corresponding points, accurate correspondence, and frame correction.[2]
Identify salient points
The purpose of identifying the corresponding salient points that exist between two frames is to reduce distortion.[2][3] Corner detection is used to identify salient points. To find corner values, Harris Corner Detector (one of the fastest algorithms for detecting corners) can be used.
Corresponding points
In this step, by extracting a matrix of 9 x 9 blocks for each point, cost to include them in the solution can be computed. The lowest cost reveals the object.[2]
Accurate correspondence
Using the random sample consensus algorithm (RANSAC), incorrect point correspondences with strong estimation of changing in location in the image can be determined.[4]
See also
References
- ↑ "Object Detection in a Cluttered Scene Using Point Feature Matching - MATLAB & Simulink". https://www.mathworks.com/help/vision/examples/object-detection-in-a-cluttered-scene-using-point-feature-matching.html.
- ↑ 2.0 2.1 2.2 Abdullah, L. M.; Tahir, N. Md; Samad, M. (July 2012). "Video stabilization based on point feature matching technique". 2012 IEEE Control and System Graduate Research Colloquium: 303–307. doi:10.1109/ICSGRC.2012.6287181. ISBN 978-1-4673-2036-8.
- ↑ Anu Suneja and Gaurav Kumar . “An Experimental Study of Edge Detection Methods in Digital Image”, Global Journal of Computer Science and Technology, 10(2), 2010.
- ↑ Tordoff, B; Murray, DW. "Guided sampling and consensus for motion estimation." 7th European Conference on Computer Vision, 2002.
 |
