Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Behzad, Muzammil"'
In this paper, we present a sparsity-aware deep network for automatic 4D facial expression recognition (FER). Given 4D data, we first propose a novel augmentation method to combat the data limitation problem for deep learning. This is achieved by pro
Externí odkaz:
http://arxiv.org/abs/2002.03157
We propose a novel landmarks-assisted collaborative end-to-end deep framework for automatic 4D FER. Using 4D face scan data, we calculate its various geometrical images, and afterwards use rank pooling to generate their dynamic images encapsulating i
Externí odkaz:
http://arxiv.org/abs/1910.05445
This paper proposes a novel 4D Facial Expression Recognition (FER) method using Collaborative Cross-domain Dynamic Image Network (CCDN). Given a 4D data of face scans, we first compute its geometrical images, and then combine their correlated informa
Externí odkaz:
http://arxiv.org/abs/1905.02319
In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems
Externí odkaz:
http://arxiv.org/abs/1806.09980
Autor:
Behzad, Muzammil
In this paper, we propose a novel image denoising algorithm exploiting features from both spatial as well as transformed domain. We implement intensity-invariance based improved grouping for collaborative support-agnostic sparse reconstruction. For c
Externí odkaz:
http://arxiv.org/abs/1805.00472
Autor:
Behzad, Muzammil
Routing Protocols are engaged in a vigorous fashion to boost up energy efficiency in WSNs. In this paper, we propose a novel routing protocol; Minimum distance Based Energy efficiency using Hemisphere Zoning with Advanced Divide-and-Rule scheme (M-BE
Externí odkaz:
http://arxiv.org/abs/1804.00898
In this paper, we combat the problem of performance optimization in wireless sensor networks. Specifically, a novel framework is proposed to handle two major research issues. Firstly, we optimize the utilization of resources available to various node
Externí odkaz:
http://arxiv.org/abs/1712.04259
Publikováno v:
In Neurocomputing 11 October 2021 458:297-307
In this paper, we propose a novel image denoising algorithm using collaborative support-agnostic sparse reconstruction. An observed image is first divided into patches. Similarly structured patches are grouped together to be utilized for collaborativ
Externí odkaz:
http://arxiv.org/abs/1609.02932
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