scholarly journals Efficient Depth Map Creation with a Lightweight Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 479
Author(s):  
Join Kang ◽  
Seong-Won Lee
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Stereo Matching ◽  
Learning Algorithm ◽  
Vision System ◽  
Depth Map ◽  
Small Scale ◽  
Depth Information ◽  
Deep Learning Algorithm ◽  
Convolution Filter

Finding depth information with stereo matching using a deep learning algorithm for embedded systems has recently gained significant attention owing to emerging high-performance mobile graphics processing units (GPUs). Several researchers have proposed feasible small-scale CNNs that can run on a local GPU, but they still suffer from low accuracy and/or high computational requirements. In the method proposed in this study, pooling layers with padding and an asymmetric convolution filter are used to reduce computational costs and simultaneously maintain the accuracy of disparity. The patch size and number of layers are adjusted by analyzing the feature and activation maps. The proposed method forms a small-scale network algorithm suitable for a vision system at the edge and still exhibits high-disparity accuracy and low computational loads as compared to existing stereo-matching networks.

scholarly journals Research on improved wavelet convolutional wavelet neural networks

2020 ◽  
Author(s):  
Jing-Wei Liu ◽  
Fang-Ling Zuo ◽  
Ying-Xiao Guo ◽  
Tian-Yue Li ◽  
Jia-Ming Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
High Performance ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Deep Learning Algorithm ◽  
Main Work ◽  
Good Ability ◽  
Fully Connected

AbstractConvolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

scholarly journals Segmentation for Complex Background Images using Deep Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 1746-1750
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Vision System ◽  
Automated Segmentation ◽  
Computer Vision System ◽  
Important Stage ◽  
Deep Learning Algorithm ◽  
Complex Background ◽  
Learning Techniques

Segmentation is an important stage in any computer vision system. Segmentation involves discarding the objects which are not of our interest and extracting only the object of our interest. Automated segmentation has become very difficult when we have complex background and other challenges like illumination, occlusion etc. In this project we are designing an automated segmentation system using deep learning algorithm to segment images with complex background.

scholarly journals Depth from a Motion Algorithm and a Hardware Architecture for Smart Cameras

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 53 ◽  
Author(s):  
Abiel Aguilar-González ◽  
Miguel Arias-Estrada ◽  
François Berry
Keyword(s):  
Optical Flow ◽  
Autonomous Navigation ◽  
High Performance ◽  
Stereo Matching ◽  
Depth Estimation ◽  
Hardware Architecture ◽  
Absolute Difference ◽  
Depth Information ◽  
Matching Problem ◽  
Smart Cameras

Applications such as autonomous navigation, robot vision, and autonomous flying require depth map information of a scene. Depth can be estimated by using a single moving camera (depth from motion). However, the traditional depth from motion algorithms have low processing speeds and high hardware requirements that limit the embedded capabilities. In this work, we propose a hardware architecture for depth from motion that consists of a flow/depth transformation and a new optical flow algorithm. Our optical flow formulation consists in an extension of the stereo matching problem. A pixel-parallel/window-parallel approach where a correlation function based on the sum of absolute difference (SAD) computes the optical flow is proposed. Further, in order to improve the SAD, the curl of the intensity gradient as a preprocessing step is proposed. Experimental results demonstrated that it is possible to reach higher accuracy (90% of accuracy) compared with previous Field Programmable Gate Array (FPGA)-based optical flow algorithms. For the depth estimation, our algorithm delivers dense maps with motion and depth information on all image pixels, with a processing speed up to 128 times faster than that of previous work, making it possible to achieve high performance in the context of embedded applications.

scholarly journals A High-Performance Deep Learning Algorithm for the Automated Optical Inspection of Laser Welding

2020 ◽  
Vol 10 (3) ◽  
pp. 933 ◽  
Author(s):  
Yatao Yang ◽  
Longhui Pan ◽  
Junxian Ma ◽  
Runze Yang ◽  
Yishuang Zhu ◽  
...  
Keyword(s):  
Laser Welding ◽  
High Performance ◽  
Learning Algorithm ◽  
Production Quality ◽  
Great Success ◽  
Defect Classification ◽  
Training Time ◽  
Deep Learning Algorithm ◽  
Positive Rate ◽  
Automated Optical Inspection

The battery industry has been growing fast because of strong demand from electric vehicle and power storage applications.Laser welding is a key process in battery manufacturing. To control the production quality, the industry has a great desire for defect inspection of automated laser welding. Recently, Convolutional Neural Networks (CNNs) have been applied with great success for detection, recognition, and classification. In this paper, using transfer learning theory and pre-training approach in Visual Geometry Group (VGG) model, we proposed the optimized VGG model to improve the efficiency of defect classification. Our model was applied on an industrial computer with images taken from a battery manufacturing production line and achieved a testing accuracy of 99.87%. The main contributions of this study are as follows: (1) Proved that the optimized VGG model, which was trained on a large image database, can be used for the defect classification of laser welding. (2) Demonstrated that the pre-trained VGG model has small model size, lower fault positive rate, shorter training time, and prediction time; so, it is more suitable for quality inspection in an industrial environment. Additionally, we visualized the convolutional layer and max-pooling layer to make it easy to view and optimize the model.

scholarly journals Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes

2021 ◽  
Vol 22 (18) ◽  
pp. 10019
Author(s):  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
Plasmodium Falciparum ◽  
Deep Learning ◽  
Unknown Function ◽  
Heterogeneous Network ◽  
High Performance ◽  
Striated Muscle ◽  
Learning Algorithm ◽  
Enrichment Analysis ◽  
Biomedical Science ◽  
Deep Learning Algorithm

Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring interactions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of Plasmodium falciparum genes based on connection profiles in a heterogeneous network between human and Plasmodium falciparum proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model’s predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, PF3D7_1248700 and PF3D7_0401800 were found to be involved with muscle contraction and striated muscle tissue development, while PF3D7_1303800 and PF3D7_1201000 were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science.

A Stereo Matching System

2013 ◽  
Vol 5 (2) ◽  
pp. 1-8
Author(s):  
Yingpeng Yang
Keyword(s):  
Stereo Matching ◽  
Learning Algorithm ◽  
Moving Average ◽  
Three Dimensional ◽  
Depth Map ◽  
Stereo Image ◽  
Image Feature ◽  
Matching Algorithm ◽  
Depth Cues ◽  
Computer Vision Technology

Determination of the depth of the image feature distinctive automation and other industries of machine vision and computer vision technology in everyday life are becoming increasingly popular. Some techniques have been proposed to extract from the current depth of a 2D image of the feature, which defines a particular object or structure of the information. In many cases, these techniques are automatic, such as a suitable carrier moving average depth identify objects placed in the 2D image. For this intensive depth cues to solve two stereo matching algorithm using a machine learning algorithm. Other methods, relative to the camera based on the motion of the object have been proposed and analyzed by estimating the optical flow calculation depth map. The method of dense and sparse three-dimensional surface of the object to provide the three-dimensional information. This paper discusses the evaluation of the depth cues, through intensive two standard fast algorithm for real-time stereo image matching algorithm.

User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Keondo Lee ◽  
Seong-Eun Kim ◽  
Junsang Doh ◽  
Keehoon Kim ◽  
Wan Kyun Chung
Keyword(s):  
Deep Learning ◽  
Piezoelectric Actuator ◽  
Cell Sorting ◽  
High Performance ◽  
Learning Algorithm ◽  
Cell Sorter ◽  
Operational Procedure ◽  
Deep Learning Algorithm ◽  
Sorting Technique ◽  
User Friendly

The image-activated cell sorter employs a significantly simplified operational procedure based on a syringe connected to a piezoelectric actuator and high-performance inference with TensorRT Integration.

scholarly journals Deep learning neural networks to differentiate Stafne’s bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254997
Author(s):  
Ari Lee ◽  
Min Su Kim ◽  
Sang-Sun Han ◽  
PooGyeon Park ◽  
Chena Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Imaging System ◽  
Panoramic Radiography ◽  
Learning Algorithm ◽  
Odontogenic Cysts ◽  
Surgical Removal ◽  
Imaging Features ◽  
Deep Learning Algorithm ◽  
Dentigerous Cysts

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne’s bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne’s bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

scholarly journals Intelligent Vehicle Design based on PaddlePaddle and Deep Learning

2021 ◽  
Vol 2132 (1) ◽  
pp. 012003
Author(s):  
Song He ◽  
Hao Xue ◽  
Lejiang Guo ◽  
Xin Chen ◽  
Jun Hu
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Real Field ◽  
Intelligent Vehicle ◽  
Traffic Signs ◽  
Automatic Data ◽  
Scheme Design ◽  
Automatic Driving ◽  
Deep Learning Algorithm

Abstract ABSTRACT.In order to visualize the applications of deep learning based intelligent vehicle in the real field vividly, especially in the unmanned cases in which it realizes the integration of various technologies such as automatic data acquisition, data model construction, automatic curve detection, traffic signs recognition, verification of the unmanned driving, etc. A M-typed Model intelligent vehicle that is embedded with a high-performance board from Baidu named Edge Board is adopted by this study. The vehicle is trained under the PaddlePaddle deep learning frame and Baidu AI Studio Develop platform. Through the autonomous control scheme design and the non-stop study on the deep learning algorithm, an intelligent vehicle model based on PaddlePaddle deep learning is here. The vehicle has the function of automatic driving on the simulated track. In addition, it can distinguish several traffic signs and make feedbacks accordingly.

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