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Pixel Difference Networks for Efficient Edge Detection [article]

Zhuo Su, Wenzhe Liu, Zitong Yu, Dewen Hu, Qing Liao, Qi Tian, Matti Pietikäinen, Li Liu
2021 arXiv   pre-print
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

Mohamed A. El-Sayed, Hamida A. M. Sennari
2014 IOSR Journal of VLSI and Signal processing  
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

Kamil Dimililer, Yoney Kirsal Ever, N. Bardis
2018 ITM Web of Conferences  
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

Mohamed A., Yarub A., Mohamed A.
2013 International Journal of Advanced Computer Science and Applications  
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

Wei Wang, Hui Lin, Junshu Wang
2020 Journal of Cloud Computing: Advances, Systems and Applications  
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]

Jyh-Jing Hwang, Tyng-Luh Liu
2015 arXiv   pre-print
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]

Jyh-Jing Hwang, Tyng-Luh Liu
2015 arXiv   pre-print
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]

Alexander Kirillov, Evgeny Levinkov, Bjoern Andres, Bogdan Savchynskyy, Carsten Rother
2016 arXiv   pre-print
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

Alexander Kirillov, Evgeny Levinkov, Bjoern Andres, Bogdan Savchynskyy, Carsten Rother
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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

R. Pushpavalli, G. Sivaradje, E. Srinivasan, S.Himavathi S.Himavathi
2012 International Journal of Computer Applications  
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

Rui Sun, Tao Lei, Qi Chen, Zexuan Wang, Xiaogang Du, Weiqiang Zhao, Asoke K. Nandi
2022 Frontiers in Signal Processing  
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

Daehyon Kim
2015 International Journal of Transportation  
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]

Yifu Chen, Arnaud Dapogny, Matthieu Cord
2019 arXiv   pre-print
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

C. Misra, P. K. Swain, J. K. Mantri
2012 International Journal of Computer Applications  
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]

Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan
2016 arXiv   pre-print
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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