Security surveillance applications utilizing parallel video-processing techniques in the spatial domain

Deligiannidis, Leonidas; Arabnia, Hamid R.

doi:10.1016/b978-0-12-802045-6.00008-9

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…The use of video monitoring allowed for a quick response to emergencies and threats to the safety of machines and people in the technological process. On the other hand, the use of more advanced video monitoring techniques may bring further benefits in the supervision of industrial processes, as described by the authors of the publication (Tsakanikas and Dagiuklas, 2018, Kardas and Ccekli, 2017, Deligiannidis and Arabnia, 2015.…”

Section: Discussionmentioning

confidence: 99%

Visual Monitoring as a Tool in Industrial Security Engineering. Case Study

Ignac-Nowicka

2021

Multidisciplinary Aspects of Production Engineering

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The article presents the use of video monitoring in a production company on the example of municipal thermal power plant. After analyzing the hazards and work inconvenience occurring in the analyzed enterprise, video surveillance zones have been designated with the division into the indoor and outdoor monitoring system. Video surveillance is provided for production, auxiliary and delivery processes. Moreover, video monitoring performs a control function over the occupation health in workstations at risk of explosion or fire. In addition, observation points have been set up in the visual monitoring system, among which the internal ones are served by the dispatcher responsible for the production process, while the external are operated by security guards of the facility.

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Section: Discussionmentioning

confidence: 99%

Visual Monitoring as a Tool in Industrial Security Engineering. Case Study

Ignac-Nowicka

2021

Multidisciplinary Aspects of Production Engineering

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“…In the frequency domain, a multi-scale transformation is applied to the source images to obtain the transformation space coefficients. Then, the image merge rules (fusion rules max and average) are applied using the coefficients and finally reverse transformation is performed to obtain the fusion image [37,38].…”

Section: Proposed Fusion Methodsmentioning

confidence: 99%

Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

Abas¹,

Baykan²

2021

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Focus is limited and singular in many image capture devices. Therefore, different focused objects at different distances are obtained in a single image taken. Image fusion can be defined as the acquisition of multiple focused objects in a single image by combining important information from two or more images into a single image. In this paper, a new multi-focus image fusion method based on Bat Algorithm (BA) is presented in a Multi-Scale Transform (MST) to overcome limitations of standard MST Transform. Firstly, a specific MST (Laplacian Pyramid or Curvelet Transform) is performed on the two source images to obtain their low-pass and high-pass bands. Secondly, optimization algorithms were used to find out optimal weights for coefficients in low-pass bands to improve the accuracy of the fusion image and finally the fused multi-focus image is reconstructed by the inverse MST. The experimental results are compared with different methods using reference and non-reference evaluation metrics to evaluate the performance of image fusion methods.

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“…And measuring, not only the outcome or end point, but also kinetics and their changes is becoming increasingly important to understand plants as a whole and become more precise at experimental designs. One of the newest developments is automatic image acquisition [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants

Navarro

Pérez

Weiß

et al. 2016

Sensors

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Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

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Security surveillance applications utilizing parallel video-processing techniques in the spatial domain

Cited by 13 publications

References 7 publications

Visual Monitoring as a Tool in Industrial Security Engineering. Case Study

Visual Monitoring as a Tool in Industrial Security Engineering. Case Study

Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants

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