S
Sara Tehsin
Researcher at University of the Sciences
Publications - 9
Citations - 42
Sara Tehsin is an academic researcher from University of the Sciences. The author has contributed to research in topics: Filter (signal processing) & Dynamic priority scheduling. The author has an hindex of 4, co-authored 8 publications receiving 33 citations. Previous affiliations of Sara Tehsin include College of Electrical and Mechanical Engineering & HITEC University.
Papers
More filters
Journal ArticleDOI
Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition
Sara Tehsin,Saad Rehman,Muhammad Omer Bin Saeed,Farhan Riaz,Ali Hassan,Muhammad Abbas,Rupert Young,Mohammad S. Alam +7 more
TL;DR: The OT parameters are optimized using particle swarm optimization with respect to two different cost functions to achieve the best possible result for each scenario.
Proceedings ArticleDOI
Improved maximum average correlation height filter with adaptive log base selection for object recognition
Sara Tehsin,Saad Rehman,Ahmad Bilal Awan,Qaiser Chaudry,Muhammad Abbas,Rupert Young,Afia Asif +6 more
TL;DR: This paper proposes some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations and shows improved correlation and target detection accuracies.
Proceedings ArticleDOI
Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
TL;DR: The proposed correlation filter based mechanism provides the capability to suppress noise, clutter and occlusion and has shown improved performance over some of the other variant correlation filters which are discussed in the result section.
Proceedings ArticleDOI
Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
TL;DR: In this article, a comparative analysis of logarithmic zero aliasing optimal trade off correlation filters has been carried out for different types of target distortions and the zero-aliasing Maximum Average Correlation Height (MACH) filter has been identified as the best choice based on their research for achieving enhanced results in the presence of any type of variance.
Proceedings ArticleDOI
Detection of moving human using optimized correlation filters in homogeneous environments
TL;DR: The experimental tests of the proposed methodology validate that better accuracy can be achieved if the proposed optimized approach is utilized for moving human detection in real-time systems.