Artificial Intelligence and Machine Vision

Recall = TP / (TP + FN) Precision = TP / (TP + FP) SOLVE F1 Score = Recall * Precision /  (Recall + Precision) F1 = (TP / (TP + FN) * TP / (TP + FP))  / ( TP / (TP + FN) + TP / (TP +FP) ) F1 = ( TP^2 / (TP + FP) + (FP + FN)  )  / (TP (TP + FN) / (TP + FP) (TP + FN))  + ( TP (TP + FP) / (TP + FP) (TP + FN) ) F1 =  TP^2 / (2 TP^2 + FNTP + FPTP) F1 =  TP^2 / (TP^2 ( TP^2 + FN / TP + FP / TP)) F1 =  1 / (TP^2 + FN / TP + FP / TP) Thus,  F1 Score is a harmonic mean.   

Recover the camera matrices from estimated fundamental matrix F Use the camera matrices to compute the point correspondences. Without K1 and K2 (internal calibration), recover a projective transforme With K1 and K2, recover the metric reconstruction. The epipolar constraint defines a line, since x2^T F x1 = l1^T x1 = 0 An epipolar line corresponding to the point x2, because l1^T x1 = 0 Thus x2 is on the epipolar line A point e is epipole, when the epipolar lines meet at this point e. Must satisfy: F e1 = 0 or e2^T F = 0