The procedures of white points detection and localization are practically complex on noisy images. In this paper, we propose an algorithm that detects and localizes white points on 3D film images. The proposed algorithm uses the fast Fourier transform to convert the binarized image into real and imaginary parts to obtain the number of white points along the horizontal and vertical. We determine the sorted coordinates of the white points by adding a brute-force solution to the coordinates obtained from the real part of the image. These sorted coordinates are obtained by subtracting the error between the Euclidean distances of the normalized coordinates along the vertical and horizontal direction. The proposed algorithm with and without brute-force achieved an average detection ratio of 0.98 and 0.88 respectively, while the others underperformed. We perform various experiments using the existing algorithms such as template matching, thresholding, and an iterative method to validate the performance of our algorithm. We also compare the rule-based algorithms that detect and localize objects in noisy images with the proposed one to determine the reliability of our algorithm. The experimental results indicate that the proposed algorithm performs better than the template matching, thresholding, and iterative algorithm.