False Positives(FP) , False Negatives(FN) , True Positives(TP) and True Negatives(TN) are the kind of evaluation metrics which are used to define difference between the prediction made by Humans.
False Positives(FP) , False Negatives(FN) , True Positives(TP) and True Negatives(TN) are the kind of evaluation metrics which are used to define difference between the prediction made by Humans( technically named as Ground Truth) and Machines(technically known as Result of Method).
Consider the above image in order to have better understanding of the concepts. Here we have taken an example of edge detection done for any image.
The edge predicted by Humans is marked in red circle (also known as Ground truth[GT]) . Also the prediction of edge made by the machine is marked in blue circle(also known as Results of Method [ROM]).
In the above scenario we can say that the intersection of GT and ROM i.e. region A is the correct estimation of presence of edge by machine and human and hence it is called True Positives.
In the second case we can consider that the region D which is not under both GT and ROM is the area which is not containing edges in the original image as per both machine and human and it is known as True Negatives.
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