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Ali Selman Aydin
Researcher at Stony Brook University
Publications - 16
Citations - 291
Ali Selman Aydin is an academic researcher from Stony Brook University. The author has contributed to research in topics: Computer science & Screen reader. The author has an hindex of 5, co-authored 15 publications receiving 244 citations. Previous affiliations of Ali Selman Aydin include Istanbul Şehir University.
Papers
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Proceedings Article
Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping
TL;DR: This work proposes a weighted DTW method that weights joints by optimizing a discriminant ratio and demonstrates the recognition performance of the proposed weightedDTW with respect to the conventional DTW and state-of-the-art Kinect.
Journal ArticleDOI
Robust gesture recognition using feature pre-processing and weighted dynamic time warping
TL;DR: A weighted DTW method is proposed that weights joints by optimizing a discriminant ratio to make the gesture recognition mechanism robust to variations due to different camera or body orientations or to different skeleton sizes between the reference gesture sequences and the test gesture sequences.
Proceedings ArticleDOI
High Impact Academic Paper Prediction Using Temporal and Topological Features
TL;DR: A novel technique to predict a paper's future impact by using temporal and topological features derived from citation networks, and the results of empirical evaluations show that the proposed framework performs significantly better than the state of the art approaches.
Proceedings ArticleDOI
CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data
TL;DR: A fully-convolutional semantic segmentation network based on the SegNet architecture is used that is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments.
Proceedings ArticleDOI
SaIL: saliency-driven injection of ARIA landmarks
TL;DR: This work proposes SaIL, a scalable approach that automatically detects the important sections of a web page, and then injects ARIA landmarks into the corresponding HTML markup to facilitate quick access to these sections.