Application of Multi-Sensor Image Fusion of Internet of Things in Image Processing

Li, Hong; Liu, Shuying; Duan, Qun; Li, Weibin

doi:10.1109/access.2018.2868227

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…Recognition and isolation guarantee spotting (RIGS) demonstrates to determining the execution of ontology enabled IoT in intelligent agriculture with the lower level characteristics such as dynamic functioning for accuracy and reliability [26,57]. Moreover, the middle level with the static functioning for generalization.…”

Section: Recognition and Isolation Guarantee Spotting (Rigs)mentioning

confidence: 99%

“…Middle level establishing generalizable (reduced to a time-variant of detection process) by concentrating the time-varying features in the occurrence of a change for the worse identification [24]. After a specified (that would take by the middle level) period or an especially fewer delay, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation [25,26]. The relation between affected and unaffected sekai-ichi apple (as in the case of one causing the other or sharing features with it) is finalized.…”

Section: Introductionmentioning

confidence: 99%

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An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

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Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized-separation ratio for time-variant of the separation process.

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Section: Recognition and Isolation Guarantee Spotting (Rigs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

Sanjeevi

Kumar

Prasanna

et al. 2020

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Li et al [28] discussed the analysis of multispectral and panchromatic images with the help of a fusion process in a multi-sensor IoT environment. This fusion method helps mitigate the distortions in the image due to different spectrum ranges acting in the same image.…”

Section: Related Workmentioning

confidence: 99%

“…Superpixel segmentation and spectral weight distribution methods are adopted to control the errors in analyzing degraded images. Unlike the work in [28], Rui et al [29] introduced the concept of deep learning for analyzing multispectral image analysis. In an IoT environment, the analyzing device is trained with the processed image using deep learning.…”

Section: Related Workmentioning

confidence: 99%

CMRS: A Classifier Matrix Recognition System for Traffic Management and Analysis in a Smart City Environment

et al. 2019

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The application of the Internet of Things (IoT) in a smart city improves its efficiency in terms of communication and installation costs by scaling geographical distance through intelligent devices and digital information. Different applications in a smart city, including health care, road safety, industry and home automation, rely on the IoT. Considering the significance of the IoT in smart city road applications, this manuscript introduces a classifier matrix recognition system (CMRS) for improving realtime traffic optimization. This classifier matrix system performs an independent and matching analysis of the real-time traffic images and constructs a decision factor for deriving its conclusion. The conclusion is served as responding notifications through the connected IoT systems for the users employing roadsidecommunication-assisted applications. CMRS exploits the advantages of block classification and matrix operations for improving the correlation accuracy and similarity index. The experimental results indicate that the proposed CMRS improves correlation accuracy with a high similarity index and less processing time and dissimilarity rate. INDEX TERMS Block classification, image recognition, IoT, ITS, smart city, traffic management. I. INTRODUCTION The rapid development of the IoT is resulting in its services being used in different real-world applications. This communication platform is capable of connecting various elements, including humans, computers, machines, tiny sensors and other objects [1], [2]. More specifically, the connected devices are heterogeneous in nature, which facilitates pervasive access to resources, applications and services [3], [4]. The intelligence of the communicating devices in this paradigm provides anywhere and anytime access to resources the end user requests [5]. Because the interconnection of this platform incorporates heterogeneous devices, it supports various communication technologies ranging from device-todevice (D2D) to distributed cloud computing [6]. The independent operations of the devices are assisted via a wireless communication medium in which the protocol and technology are adopted appropriately. Distributed and concurrent The associate editor coordinating the review of this manuscript and approving it for publication was Chao Chen.

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“…A convolutional neural networks (CNN)-based method [15] is proposed by Liu et al for the first time, in which the activity level measurement and fusion rule can be jointly generated using CNN model. In [16], an advanced multi-sensor image fusion based on application layer of Internet of Things is proposed by Li et al to retain the spectral information of multispectral image.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Multi-Focus Image Fusion Framework Robust to Image Mis-Registration

Kang

Zhang

et al. 2019

IEEE Access

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Multi-focus image fusion technique is to yield a composite image with all objects in focus. However, most of fusion methods do not obtain the satisfactory performance when the focused objects in the source images are not registered. In this paper, a novel classification and probability optimization based multi-focus image fusion method is proposed, which consists of two main steps. First, by the integration of multinomial logistic regression classifier and random walker based optimization in a two-stage framework, we can extract the focused regions of each source image. Then, in order to construct the fused image, the focused regions are combined together. Experimental results show that compared with other advanced fusion methods, the proposed method can obtain competitive performance in terms of subjective and objective evaluations. INDEX TERMS Multi-focus image fusion, multinomial logistic regression classifier, probability optimization, random walker.

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Application of Multi-Sensor Image Fusion of Internet of Things in Image Processing

Cited by 10 publications

References 35 publications

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses

CMRS: A Classifier Matrix Recognition System for Traffic Management and Analysis in a Smart City Environment

A Two-Stage Multi-Focus Image Fusion Framework Robust to Image Mis-Registration

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