Estimating Pig Weight with Digital Image Processing using Deep Learning

2018 ◽  
Author(s):  
Sirimonpak Suwannakhun ◽  
Patasu Daungmala
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Digital Image Processing ◽  
Digital Image

scholarly journals Digital image processing with deep learning for automated cutting tool wear detection

2020 ◽  
Vol 48 ◽  
pp. 947-958
Author(s):  
Thomas Bergs ◽  
Carsten Holst ◽  
Pranjul Gupta ◽  
Thorsten Augspurger
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Tool Wear ◽  
Digital Image Processing ◽  
Digital Image ◽  
Cutting Tool ◽  
Cutting Tool Wear

scholarly journals Plataforma para apoio a modelagem de próteses com base em Processamento Digital de Imagens e Deep Learning

2021 ◽  
Author(s):  
Bianka Tallita Passos ◽  
Moira Cristina Cubas Fatiga Tillmann ◽  
Anita Maria da Rocha Fernandes
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
High Resolution ◽  
Image Classification ◽  
Digital Image Processing ◽  
Medical Practice ◽  
Digital Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Specific Label

Medical practice in general, and dentistry in particular, generatesdata sources, such as high-resolution medical images and electronicmedical records. Digital image processing algorithms takeadvantage of the datasets, enabling the development of dental applicationssuch as tooth, caries, crown, prosthetic, dental implant, andendodontic treatment detection, as well as image classification. Thegoal of image classification is to comprehend it as a whole and classifythe image by assigning it to a specific label. This work presentsthe proposal of a tool that helps the dental prosthesis specialist toexchange information with the laboratory. The proposed solutionuses deep learning to classify image, in order to improve the understandingof the structure required for modeling the prosthesis. Theimage database used has a total of 1215 images. Of these, 60 wereseparated for testing. The prototype achieved 98.33% accuracy.

scholarly journals Plant diseases and pests detection based on deep learning: a review

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jun Liu ◽  
Xuewei Wang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Digital Image Processing ◽  
Digital Image ◽  
Future Trend ◽  
Plant Diseases ◽  
Detection Methods ◽  
Learning Technology ◽  
Practical Applications ◽  
Advantages And Disadvantages

AbstractPlant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

scholarly journals DIGITAL IMAGE PROCESSING AND RECOGNITION USING PYTHON

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
D. Sri Shreya
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Digital Image Processing ◽  
Digital Image ◽  
Gaussian Function ◽  
Numerical Data ◽  
Input Image ◽  
Image Blurring ◽  
Learning Concept

In this project, the primary aim will be the conversion of images into Grayscale in which conversion of pixels to array takes place and apply Blur effect using The Gaussian blur which is a type of image-blurring filter that uses a Gaussian function which also expresses the normal distribution in statistics for calculating the transformation to apply to each pixel in the image. The above two processesare applied to the input images. These two above mentioned processes can be achieved by utilizing the most relevant python libraries and functions, followed by conversion of the digital image to numerical data and then, applying the effects to the image to get back the image with applied effects in it. Face recognition refers to matching a face present in an input image from the training/pre-saved dataset and by applying Deep Learning Concept. This will be achieved by defining a function to read and convert images to data, apply the python function, and then, recreating the image with results.

scholarly journals Automated Shape Analysis and DEM Study on Graded Crushed Stone

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hao Bai ◽  
Ruidong Li ◽  
Xiangyu Hu ◽  
Fei Chen ◽  
Zhiyong Liao
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Discrete Element Method ◽  
Digital Image Processing ◽  
Shape Analysis ◽  
Digital Image ◽  
Particle Shape ◽  
Void Ratio ◽  
Road Construction ◽  
Crushed Stone

Graded crushed stone (GCS), as a cheap and essential component, is of great importance in road construction. The irregularity and variability of particle shape is known to affect the packing characteristics of GCS, such as compactness and void ratio. In this study, the realistic particle outline is first automatically extracted based on digital image processing and deep learning algorithms. Then, the elongation (EI), roundness (Rd), and roughness (Rg) of GCS are quantified by shape evaluation algorithms. Moreover, based on the establishment of the GCS shape library, the gravity deposition with various elongations is simulated using the discrete element method to study the packing features of GCS. The elongation effects on the macroscopic and microscopic quantities are explored. Finally, the shear behavior of GCS is studied. The results illustrate that elongation has a significant effect on the packing of GCS.

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