The temperature monitoring system (TMS) aims to reduce the infection spread and outbreak of COVID-19 through early detection. Conventional and currently deployed TMS have high implementation cost and require a substan...
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The temperature monitoring system (TMS) aims to reduce the infection spread and outbreak of COVID-19 through early detection. Conventional and currently deployed TMS have high implementation cost and require a substantial amount of space. Also, the performance often depends on the accuracy of the thermal camera. To address this, we propose Edge TMS wherein a multitask cascaded convolutional neural networks (MTCNNs)based TMS is deployed on an edge AI device. To overcome the resource constraints of edge AI devices, an optimization method is applied to compress MTCNN up to 100x. The compressed MTCNN is deployed on the local PC, Jetson Xavier, Jetson TX2, and Jetson Nano which yields pruning-per-reduction ratio (PPRR) values of 1.21, 1.63, 1.99, and 2.10, respectively. We proposed the PPRR metric to measure the performance of the compressed model. Low PPRR values indicate an improvement in the hardware performance and computational efficiency of the optimized model. The optimized model deployed in all the Jetson series achieved an average percent power reduced (%R) of 53.18% with a percent difference of 35.9% from the results of the local PC.
Feature-based method for detecting landmarks from facial images was designed. The method was based on extracting oriented edges and constructing edge maps at two resolution levels. Edge regions with characteristic edg...
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Feature-based method for detecting landmarks from facial images was designed. The method was based on extracting oriented edges and constructing edge maps at two resolution levels. Edge regions with characteristic edge pattern formed landmark candidates. The method ensured invariance to expressions while detecting eyes. Nose and mouth detection was deteriorated by happiness and disgust.
A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA...
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A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA-AAMs, while requiring similar implementation effort, consistently outperform standard search with regard to convergence speed by a factor of four.
Some of these so-called AIoT applications include intelligent imageprocessing in smart factories to monitor machinery conditions and control raw material inventory, identifying abnormalities in medical images, and au...
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Some of these so-called AIoT applications include intelligent imageprocessing in smart factories to monitor machinery conditions and control raw material inventory, identifying abnormalities in medical images, and automatic real-time scanning and recognition of license plates in traffic to locate stolen cars.
The topics covered in this special issue include (i) intelligent imageprocessing applications and services to fulfill the real-time processing and performance demands, (ii) real-time deep learning and machine learning solutions to improve computational speed and increase recognition rates at network edges, (iii) new frameworks to optimize real-time AIoT imageprocessing, and (iv) combining intelligent real-time imageprocessing with edge computing, fog computing, and relevant techniques to balance the computational workloads between IoT devices and the server side.
Fan and Guan [1] have developed a deep face verification framework based on SIFT (scale invariant feature transform) and CNN (convolutional neural network) methods.
Hyper-Resolution, a new technique for super-resolution reconstruction of images, is based on matching low-resolution target image details to their high-resolution counterparts from an image database. Central to the al...
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Hyper-Resolution, a new technique for super-resolution reconstruction of images, is based on matching low-resolution target image details to their high-resolution counterparts from an image database. Central to the algorithm is a novel transform of image content from the orthogonal pixel space to a parametric space structured around edges. This approach offers improved quality, more flexibility and significantly faster performance than previous work in the field. Implementation strategies for achieving this efficiency are carefully outlined. The algorithm is evaluated by controlled assessment, qualitative evaluation, and applications to facial detail reconstruction and identification. The algorithm is finally analyzed through the comparison with alternative techniques. (c) 2005 Elsevier Ltd. All rights reserved.
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