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Pixel Difference Networks for Efficient Edge Detection
[article]
2021
arXiv
pre-print
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To address these issues, we propose a simple, lightweight yet effective architecture named Pixel Difference Network (PiDiNet) for efficient edge detection. ...
Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. ...
The authors also wish to acknowledge CSC IT Center for Science, Finland, for computational resources. ...
arXiv:2108.07009v1
fatcat:f5dmlyls2jad5hseeo2mvwwdhm
Convolutional Neural Network for Edge Detection in SAR Grayscale Images
2014
IOSR Journal of VLSI and Signal processing
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There is not a single edge detector that has both efficiency and reliability. Neural networks are a powerful technology for classification and edge detection of the images. ...
At each level, the outputs of multiple networks are fused for robust and accurate estimation. ...
Neural Network model for edge detection as in Fig. 9 . ...
doi:10.9790/4200-04217583
fatcat:fctklbslfzhntoowxsphqhtury
Planning pesticides usage for herbal and animal pests based on intelligent classification system with image processing and neural networks
2018
ITM Web of Conferences
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Firstly, images are processed using different image processing techniques that images have specific distinguishing geometric patterns. The second stage is neural network phase for classification. ...
A backpropagation neural network is used for training and testing with processed images. System is tested, and experiment results show efficiency and effective classification rate. ...
Different image processing techniques are applied to images in order to highlight the edges and prepare them for the back propagation neural networks. ...
doi:10.1051/itmconf/20181601004
fatcat:yb4penka4jcprha3z6cwi7khlu
Automated Edge Detection Using Convolutional Neural Network
2013
International Journal of Advanced Computer Science and Applications
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The edge detection on the images is so important for image processing. ...
Currently, there is not a single edge detector that has both efficiency and reliability. ...
It was trained with different edge and non edge patterns several times so that it is able to automatically detect edges in any test image efficiently. ...
doi:10.14569/ijacsa.2013.041003
fatcat:undvifavlnhezfrm53nkrccdli
CNN based lane detection with instance segmentation in edge-cloud computing
2020
Journal of Cloud Computing: Advances, Systems and Applications
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The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models. ...
In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. ...
In
Pixel fitting The output of the two-branch network is a set of pixels for each lane line. ...
doi:10.1186/s13677-020-00172-z
fatcat:a7h4opft6bgb7kk74cxs3oc5jy
Pixel-wise Deep Learning for Contour Detection
[article]
2015
arXiv
pre-print
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We address the problem of contour detection via per-pixel classifications of edge point. ...
To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each ...
For contour detection, the central task is to decide whether an underlying pixel is an edge point or not. Thus, it would be convenient that the deep network could yield per-pixel features. ...
arXiv:1504.01989v1
fatcat:sotrgv53qbeybhtb6odhct63ca
Contour Detection Using Cost-Sensitive Convolutional Neural Networks
[article]
2015
arXiv
pre-print
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We address the problem of contour detection via per-pixel classifications of edge point. ...
To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each ...
Different from Ganin & Lempitsky (2014) , we strive for designing fine-tuning mechanisms with a small dataset for adapting an ImageNet pre-trained convolutional neural network for producing per-pixel ...
arXiv:1412.6857v5
fatcat:2qjsdluo4jdzbl4azlqiapydhi
InstanceCut: from Edges to Instances with MultiCut
[article]
2016
arXiv
pre-print
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The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instance-aware edge detection model. ...
Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches. ...
In our work the instance-aware edge detection outputs a probability for each pixel, whether it touches a boundary. ...
arXiv:1611.08272v1
fatcat:znvvi6lk3vbrlcehspspjg5uhm
InstanceCut: From Edges to Instances with MultiCut
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instanceaware edge detection model. ...
Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches.Our approach, ...
In our work the instance-aware edge detection outputs a probability for each pixel, whether it touches a boundary. ...
doi:10.1109/cvpr.2017.774
dblp:conf/cvpr/KirillovLASR17
fatcat:creey7633fchzb6kguggzqkcea
Neural based Post Processing Filtering Technique for Image Quality Enhancement
2012
International Journal of Computer Applications
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Extensive simulation results show that the proposed Post Processing Technique can be used for efficient enhancement of digital images corrupted by impulse noise without distorting useful information in ...
The proposed filter is an intelligent filter obtained by aptly combining a Nonlinear Filter (NF), Modified Canny Edge Detector (MCED) and a Feed forward Adaptive Neural (FAN) Network. ...
Canny edge detected output data's are also contain binary values of 1 for edges and binary value of 0 for homogenous region of image respectively. ...
doi:10.5120/4591-6787
fatcat:u7ev6rs55fay3jnv7puswsrflu
Survey of Image Edge Detection
2022
Frontiers in Signal Processing
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Edge detection technology aims to identify and extract the boundary information of image pixel mutation, which is a research hotspot in the field of computer vision. ...
This paper dedicates to making a comprehensive analysis of edge detection technology and aims to offer reference and guidance for the relevant personnel to follow up easily the current developments of ...
Su et al. (2021) have designed a simple, lightweight and efficient edge detection architecture called Pixel Difference Network (PiDiNet) to address these issues. ...
doi:10.3389/frsip.2022.826967
fatcat:66qfer3dszdslaz7vjzmldy3bi
Prediction Performance of Support Vector Machines with Fused Data in Road Scene Analysis
2015
International Journal of Transportation
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In this paper, two information sources of edge information and pixel gray value have been combined to detect vehicle in road scene images and see how the fused data affect the predictive performance in ...
Automatic video-based vehicle detection is one of the main research topics in Intelligent Transportation Systems (ITS) and is a key element for automatic traffic surveillance systems. ...
The binarization of Sobel edge detection image may provide a different source of information as input vectors for learning models. ...
doi:10.14257/ijt.2015.3.3.04
fatcat:3dx6rnxxcjbyhbiaoeqkzhcv2a
SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss
[article]
2019
arXiv
pre-print
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We introduce a novel semantic edge detection network, which allows to match the predicted and ground truth segmentation masks. ...
pixel-wise cross-entropy loss. ...
Structure learning through edge detection The canonical loss for training deep networks for semantic segmentation is the Per-Pixel Cross Entropy (PPCE) loss L P P CE . ...
arXiv:1905.01892v1
fatcat:73evjcp47ba2he3vpummbjylja
Text Extraction and Recognition from Image using Neural Network
2012
International Journal of Computer Applications
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Extraction and recognition of text from image is an important step in building efficient indexing and retrieval systems for multimedia databases. ...
Our primary objective is to make an unconstrained image indexing and retrieval system using neural network. We adopt HSV based approaches for color reduction. This approach show impressive results. ...
images, it is not so efficient for detecting text with complex and cluttered background, small font. ...
doi:10.5120/4927-7156
fatcat:jz4mjvyutbaenmxnxljfmat44y
PixelNet: Towards a General Pixel-level Architecture
[article]
2016
arXiv
pre-print
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We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. ...
Interestingly, our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context, surface normal estimation on NYUDv2 dataset, and edge detection on BSDS without contextual ...
AB and XC would like to thank Abhinav Shrivastava and Saining Xie for useful discussion. ...
arXiv:1609.06694v1
fatcat:mghgk6zh6zepzmct6xxhedxnii
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