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.

An Improved LBP Blockwise Method for Face Recognition

2018 ◽  
Vol 7 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Nikhil Kumar ◽  
Sunny Behal
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Human Brain ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing System ◽  
Automatic Processing ◽  
Image Databases ◽  
Similar Kind

Face recognition is considered as one of toughest and most crucial leading domains of digital image processing. The human brain also uses a similar kind of technique for face recognition. When scrutinizing a face, the human brain signifies the result. Aside from AN automatic processing system, this technique is very sophisticated, owing to the image variations on account of the picture varieties in as far as area, size, articulation, and stance. In this article, the authors have used the options of native binary pattern and uniform native binary pattern for face recognition. They compute a number of classifiers on publicly available benchmarked ORL image databases to validate the proposed approach. The results clearly show that the proposed LBP-piece shrewd strategy has outperformed the traditional LBP system.

scholarly journals Implementation of HSV- based Thresholding Method for Iris Detection

2021 ◽  
Vol 3 (1) ◽  
pp. 98-104
Author(s):  
Fajrul Islami
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Input Image ◽  
Test Method ◽  
Threshold Method ◽  
Processing Stage ◽  
Image Thresholding ◽  
Image Objects ◽  
Intensity Changes

Image thresholding is one of the most frequently used methods in image processing to perform digital image processing. Image thresholding has a technique that can separate the image object from its background. This is a technique that is quite good and effective for segmenting love. In this study, the threshold method used will be combined with the HSV mode for color detection. The threshold method will separate the object and the image background, while HSV will help improve the segmentation results based on the Hue, Saturation, Value values to be able to detect objects more accurately. Segmentation is carried out using the original input image without pre-processing or direct segmentation. As we know that in digital image processing, there are steps that are usually done to get a good input image, namely pre-processing. In this pre-processing stage, processes such as image conversion and image intensity changes are carried out so that the input image is better. Therefore, even though the input image is used without going through the pre-processing stage, the object can be segmented properly based on the color type of the object. The results of this segmentation can later be used for recognition and identification of image objects. The results of the test method for object segmentation achieved a color similarity level of 25%, with an accuracy rate of 75% in detecting uniform color objects. So that this method can be one of the most effective methods in segmenting image objects without pre-processing or direct thresholding

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 FACE RECOGNITION UNTUK SISTEM KEAMANAN RUANGAN ARSIP UNIVERSITAS AISYAH PRINGSEWU MENGGUNAKAN MATLAB

2020 ◽  
Vol 2 (2) ◽  
pp. 152-155
Author(s):  
Agustinus Eko Setiawan
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Digital Image Processing ◽  
Digital Image

Ruang arsip adalah tempat penyimpanan dokumen-dokumen penting, maka dalam hal ini tidak semua orang bisa mengakses ruangan tersebut. Sebagai keamanan ruangan arsip, dalam penelitian ini peneliti akan membuat sistem keamananan ruangan. Sistem keamanan yang akan dibangun yaitu face recognition, face recognition merupakan salah satu bidang yang terdapat pada digital image processing dimana peruntukan dari face recognition itu sendiri bermacam-macam dimulai dari sistem kehadiran maupun sistem keamanan, dalam penelitian ini akan dirancang sebuah sistem pengenalan wajah sebagai akses untuk keluar masuk ruangan arsip di Universitas Aisyah Pringsewu, dimana sistem tersebut dirancang dengan menggunakan bahasa pemrograman Matlab R2013a. Yang nantinya diharapkan dapat terintegrasi dengan suatu sistem kendali yang berfungsi sebagai alat pengunci pintu. Dalam penelitian ini proses identifikasi wajah dilakukan hanya sebatas simulasi dengan hasil penelitian mencapai sekitar 90% dari 10 percobaan.

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.

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