What are True Positive and False Positive with reference to image processing

A true positive result is when PowerAI Vision correctly labels or categorizes an image. For example, categorizing an image of a cat as a cat. False positive. A false positive result is when PowerAI Vision labels or categorizes an image when it should not have.

1. What are true positives and false positives?
2. What is true positive rate and false positive rate?
3. What is false positive and false negative edge pixels?

What are true positives and false positives?

A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class.

What is true positive rate and false positive rate?

The true positive rate (TPR, also called sensitivity) is calculated as TP/TP+FN. TPR is the probability that an actual positive will test positive. The true negative rate (also called specificity), which is the probability that an actual negative will test negative. It is calculated as TN/TN+FP.

What is false positive and false negative edge pixels?

the false positive (FP) are the pixels considered by the segmentation in the object, but which in reality are not part of it, finally, the false negative (FN) are the pixels of the object that the segmentation has classified outside.