Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks

Kouadria, Nasreddine; Mechouek, Khaoula; Harize, Saliha; Doghmane, Noureddine

doi:10.1016/j.compeleceng.2018.11.010

Cited by 26 publications

(11 citation statements)

References 16 publications

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“…Also, we will examine the recognition capabilities and retrieval efficiency using other signature feature extraction methods such as wavelet transformation. 16 Local DTT Software High-energy consumption Zuo et al 31 Distributed Distributed compression Software High-energy consumption Chefi et al 32 Local Object extraction scheme Hardware Low-energy but high implementation cost Nikolakopoulos et al 33 Local Quad-tree decomposition Software Moderate-energy consumption Zam et al 23 Distributed Face-detection algorithm using discriminative vectors Software High-energy consumption…”

Section: Discussionmentioning

confidence: 99%

“…Leila et al 13 proposed a hardware implementation of compression, which is considered an unscalable solution for large-scale sensor networks because it raises the implementation cost. The novel discrete Tchebichef transform (DTT) 16 was applied to the region of interest (ROI) instead of the whole image. This approach enhances the discrete cosine transform (DCT) to improve its algorithm complexity and energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…Kouadria et al 16 applied an ROI-based image compression using the DTT. The DTT compression technique is an alternative to the DCT due to its low complexity and good energy consumption.…”

Section: Energy Consumption Efficiency Analysismentioning

confidence: 99%

See 2 more Smart Citations

A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things

Al-Sabhan

Soudani

AlMusallam

2022

International Journal of Distributed Sensor Networks

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The availability of low-cost embedded devices for multimedia sensing has encouraged their integration with low-power wireless sensors to create systems that enable advanced services and applications referred to as the Internet of Multimedia Things. Image-based sensing applications are challenged by energy efficiency and resource availability. Mainly, image sensing and transmission in Internet of Multimedia Things severely deplete the sensor energy and overflow the network bandwidth with redundant data. Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms’ computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. In this article, we propose a new approach of distributed event-based sensing scheme over a set of nodes forming a processing cluster to balance the processing load. This approach is intended to reduce per-node energy consumption in one sensing cycle. The conducted experiments show that our novel method based on the general Fourier descriptor decreases the energy consumption in the camera node to only 2.4 mJ, which corresponds to 75.32% of energy-saving compared to the centralized approach, promising to prolong the network lifetime significantly. In addition, the scheme achieved more than 95% accuracy in target recognition.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things

Al-Sabhan

Soudani

AlMusallam

2022

International Journal of Distributed Sensor Networks

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“…As mentioned above, the histogram of oriented gradients (HOG) [28][29][30] based on the region of interest (ROI) [31][32][33] has a good detection performance.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine

Zhang

2021

Mathematical Problems in Engineering

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In order to solve the problems such as big errors, lack of universality, and too much time consuming occurred in the recognition of overlapped fruits, an improved fuzzy least square support vector machine (FLS-SVM) is established based on the fruit ROI-HOG feature. First, the RGB image is transformed into saturation and value (HSV) image, and then the regions of interest (ROI) are detected from HSV color information. Finally, the histogram of oriented gradients (HOG) feature of ROI will be used as the input of FLS-SVM pattern recognizer to realize the recognition of picking fruit. In addition, the verified FLS-SVM is used to investigate the recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature. The results reveal that the vector sizes are effectively reduced and a higher detection speed is achieved without compromising accuracy relative to conventional approaches. Similarly, the detection accuracy for the learning samples, the isolated fruit, the overlapped fruit, and the background can achieve 99.50%, 96.0%, 89.9%, and 97.0%, respectively, which shows the good performance of the proposed improved ROI-HOG feature recognition method.

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“…Traditionally, the main purpose of using the transition region-based extraction method for image segmentation, and active contour segmentation is to reduce the complexity of image operations [1]. Among them, Nasreddine Kouadria et al used the region-based segmentation method to reduce the image compression efficiency by 50% using the Discrete Tchebichef Transform (DTT) image compression technology based on RoI, and the transition region-based approach is a hybrid segmentation technique [2]. Nilamani Bhoi et al initially decomposed grayscale images in the wavelet-domain and applied two existed transition region methods to the approximation.…”

Section: Introductionmentioning

confidence: 99%

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Wang

Dey

et al. 2020

Biocybernetics and Biomedical Engineering

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Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.

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Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks

Cited by 26 publications

References 16 publications

A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things

A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things

Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

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