M
Mansour Jamzad
Researcher at Sharif University of Technology
Publications - 137
Citations - 1690
Mansour Jamzad is an academic researcher from Sharif University of Technology. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 21, co-authored 132 publications receiving 1515 citations. Previous affiliations of Mansour Jamzad include Waseda University.
Papers
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Journal ArticleDOI
Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes
Majid Dowlati,Majid Dowlati,Seyed Saeid Mohtasebi,Mahmoud Omid,Seyed Hadi Razavi,Mansour Jamzad,Miguel de la Guardia +6 more
TL;DR: Gills color changes were more precise than those found for eyes in order to evaluate the fish freshness and can be used as a green, low cost and easy method for fast and on-line assessing ofFish freshness in food industry.
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Real time classification and tracking of multiple vehicles in highways
Roya Rad,Mansour Jamzad +1 more
TL;DR: A real-time method for extracting a few traffic parameters in highways such as, lane change detection, vehicle classification and vehicle counting, and a real time method for multiple vehicles tracking that has the capability of occlusion detection is explained.
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Motion blur identification in noisy images using mathematical models and statistical measures
TL;DR: This paper has presented a novel algorithm to estimate linear motion blur parameters such as direction and length using Radon transform to find direction and bispectrum modeling to find the length of motion.
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Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum
TL;DR: A novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts is presented, which works highly satisfactory for SNR dB and supports lower SNR compared with other algorithms.
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A SVM-based model-transferring method for heterogeneous domain adaptation
TL;DR: A new SVM-based model-transferring method, in which a max-margin classifier is trained on labeled target samples and is adapted using the offset of the source classifier, abbreviated as HMCA, which can handle heterogeneous domains.