H
Hamid R. Tizhoosh
Researcher at University of Waterloo
Publications - 319
Citations - 9545
Hamid R. Tizhoosh is an academic researcher from University of Waterloo. The author has contributed to research in topics: Image retrieval & Image segmentation. The author has an hindex of 41, co-authored 291 publications receiving 7786 citations. Previous affiliations of Hamid R. Tizhoosh include University of Toronto & Otto-von-Guericke University Magdeburg.
Papers
More filters
Journal ArticleDOI
Window memoization: toward high-performance image processing software
TL;DR: A new performance improvement technique, window memoization, for software implementations of local image processing algorithms, which minimizes the number of redundant computations performed on an image by identifying similar neighborhoods of pixels in the image and skipping the computations that are not necessary.
Proceedings ArticleDOI
Choquet integral-based aggregation of image template matching algorithms
TL;DR: A fuzzy integral-based aggregated template matching system is developed on the basis of Choquet integral using belief, plausibility, and probability measure, while being interpreted as an optimistic, a pessimistic, and a noninteracting aggregation, respectively.
Proceedings ArticleDOI
Binary codes for tagging x-ray images via deep de-noising autoencoders
TL;DR: In this paper, the authors used a deep de-noising autoencoder (DDA) with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes.
Book ChapterDOI
Two Frameworks for Improving Gradient-Based Learning Algorithms
TL;DR: This chapter proposes opposite transfer functions as a means to improve the numerical conditioning of neural networks and extrapolate two backpropagation-based learning algorithms for improvement in accuracy and generalization ability on common benchmark functions.
Proceedings ArticleDOI
Weighted Voting-Based Robust Image Thresholding
TL;DR: A new robust image thresholding technique is introduced that uses fusion of some well-known thresholding methods by applying weighted voting at the decision level to improve robustness of thresholding approach by participating several methods.