Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

Wang, Xiaoqin; Şekercioğlu, Y. Ahmet; Drummond, Tom; Frémont, Vincent; Natalizio, Enrico; Fantoni, Isabelle

doi:10.3390/s18082430

Cited by 3 publications

(3 citation statements)

References 61 publications

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“…The corn leaf images that will be utilised as input in image identification are in the YCbCr colour space. The actual colour displayed is determined by the RGB colourants used to present the corn leaf images, but these RGB are inefficient as a representation for storage and transmission due to their high mutual redundancy (Wang et al, 2018). YCbCr distinguishes between a luma (Y) component that can be stored in high resolution or transmitted at high bandwidth and two chroma components (Cb and Cr) that can be bandwidthreduced, sub-sampled, compressed, or otherwise treated separately to improve algorithm efficiency.…”

Section: 60mentioning

confidence: 99%

Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

ENTUNI

Zulcaffle

2022

BJRST

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Corn is a vital commodity in Malaysia because it is a key component of animal feed. The retention of the wholesome corn yield is essential to satisfy the rising demand. Like other plants, corn is susceptible to pathogens infection during the growing period. Manual observation of the diseases nevertheless takes time and requires a lot of work. The aim of this study was to propose an automatic approach to identify corn leaf diseases. The dataset used comprises of the images of diseased corn leaf comprising of blight, grey spot and rust as well as healthy corn leaf in YCbCr colour space representation. The DenseNet-201 algorithm was utilised in the proposed method of identifying corn leaf diseases. The training and validation analysis of distinctive epoch values of DenseNet-201 were also used to validate the proposed method, which resulted in significantly higher identification accuracy. DenseNet-201 succeeded 95.11% identification accuracy and it outperformed the prior identification methods such as ResNet-50, ResNet-101 and Bag of Features. The DenseNet-201 also has been validated to function as anticipated in identifying corn leaf diseases based on the algorithm validation assessment.

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Section: 60mentioning

confidence: 99%

Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

ENTUNI

Zulcaffle

2022

BJRST

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“…The depth pixels are predicted by searching similar surfaces from the neighboring frame of depth data. Wang et al [14][15] proposed a prediction method for depth data by calculating the camera movement from the neighboring frame. Our previous study [16] predicts the depth pixels through a plane modeling that is an intra-picture prediction method by a plane estimation.…”

Section: Background and Previous Workmentioning

confidence: 99%

“…Therefore, the inter-and intra-picture prediction for depth data should consider the relationship of the adjacent pixels depending on the surface type. The methods for the surface motion estimating between consecutive frames [13] and for calculating the threedimensional motion of the camera [14][15] are proposed to predict adjacent pixels by considering the surface type.…”

Section: Introductionmentioning

confidence: 99%

Efficient Depth Data Coding Method Based on Plane Modeling for Intra Prediction

2021

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In this paper, we propose a depth data coding method by plane modeling. The plane modeling is a prediction method based on a plane estimation from depth pixels in a block. The plane modeling improves the intra-picture prediction for depth data comparing with intra modes of conventional coding standards. The plane modeling coefficients, which are the information about the estimated plane, are required to be provided during the depth data coding. The plane modeling coefficients are predicted from neighboring depth pixels. A prediction error of the plane modeling coefficients is calculated through the plane modeling for the selected pixels. If the prediction error is below a certain threshold, then the plane modeling coefficients are applied to the plane modeling for the block. From the simulation results, we verify that the proposed method achieves up to 6.76% bit rate saving in the same coding distortion condition compared to VVC test model.

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Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning

Lee

Kwon

2022

Sensors

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In this paper, we propose an intra-picture prediction method for depth video by a block clustering through a neural network. The proposed method solves a problem that the block that has two or more clusters drops the prediction performance of the intra prediction for depth video. The proposed neural network consists of both a spatial feature prediction network and a clustering network. The spatial feature prediction network utilizes spatial features in vertical and horizontal directions. The network contains a 1D CNN layer and a fully connected layer. The 1D CNN layer extracts the spatial features for a vertical direction and a horizontal direction from a top block and a left block of the reference pixels, respectively. 1D CNN is designed to handle time-series data, but it can also be applied to find the spatial features by regarding a pixel order in a certain direction as a timestamp. The fully connected layer predicts the spatial features of the block to be coded through the extracted features. The clustering network finds clusters from the spatial features which are the outputs of the spatial feature prediction network. The network consists of 4 CNN layers. The first 3 CNN layers combine two spatial features in the vertical and horizontal directions. The last layer outputs the probabilities that pixels belong to the clusters. The pixels of the block are predicted by the representative values of the clusters that are the average of the reference pixels belonging to the clusters. For the intra prediction for various block sizes, the block is scaled to the size of the network input. The prediction result through the proposed network is scaled back to the original size. In network training, the mean square error is used as a loss function between the original block and the predicted block. A penalty for output values far from both ends is introduced to the loss function for clear network clustering. In the simulation results, the bit rate is saved by up to 12.45% under the same distortion condition compared with the latest video coding standard.

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Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

Cited by 3 publications

References 61 publications

Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

Identification of Corn Leaf Diseases Comprising of Blight, Grey Spot and Rust Using DenseNet-201

Efficient Depth Data Coding Method Based on Plane Modeling for Intra Prediction

Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning

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