Bounding Box Intersection over Union (IoU) Measure
(Redirected from Intersection over Union)
Jump to navigation
Jump to search
An Bounding Box Intersection over Union (IoU) Measure is an image bounding performance measure based on ...
- …
- Counter-Example(s):
- See: Jaccard Index, Visual Object Detection.
References
2018
2016
- http://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/
- QUOTE: interested in building a custom object detector using the HOG + Linear SVM framework for his final year project. He understands the steps required to build the object detector well enough — but he isn’t sure how to evaluate the accuracy of his detector once it’s trained. His professor mentioned that he should use the Intersection over Union (IoU) method for evaluation, …
...
...
Figure 2: Computing the Intersection of Union is as simple as dividing the area of overlap between the bounding boxes by the area of union (thank you to the excellent Pittsburg HW4 assignment for the inspiration for this figure).
- QUOTE: interested in building a custom object detector using the HOG + Linear SVM framework for his final year project. He understands the steps required to build the object detector well enough — but he isn’t sure how to evaluate the accuracy of his detector once it’s trained. His professor mentioned that he should use the Intersection over Union (IoU) method for evaluation, …
2014
- (Zitnick & Dollár, 2014) ⇒ C Lawrence Zitnick, and Piotr Dollár. (2014). “Edge Boxes: Locating Object Proposals from Edges.” In: European Conference on Computer Vision.
- QUOTE: The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection.
Figure 2: Computing the Intersection of Union is as simple as dividing the area of overlap between the bounding boxes by the area of union (thank you to the excellent Pittsburg HW4 assignment for the inspiration for this figure).
- QUOTE: The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection.