Evaluating the performance of a thresholding filter on handwritten images classification task
In this paper we evaluate the effect of a thresholding filter on the accuracy and training times of a deep neural network. The filter increases the brightness of each pixel in the input image and then applies a threshold condition that zeroes out values exceeding a preset value. Although the filter is lossy, we demonstrate improved learning performance under some use cases.
By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
Upon acceptance of their manuscript, the Authors further agree to grant SPP the non-exclusive, worldwide, and royalty-free rights to record, edit, copy, reproduce, publish, distribute, and use all or part of the manuscript for any purpose, in any media now existing or developed in the future, either individually or as part of a collection.
All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.