CNN for Image Identification of Hiragana Based on Pattern Recognition using CNN

Hiragana is one of the letters in Japanese. In this study, CNN (Convolutional Neural Network) method used as identication method, while he preprocessing used thresholding. Then carry out the normalization stage and the filtering stage to remove noise in the image. At the training stage use maxpooling and danse methods as a liaison in the training process, wherea in testing stage using the Adam Optimizer method. Here, we use 1000 images from 50 hiragana characters with a ratio of 950: 50, 950 as training data and 50 data as testing data. Our experiment yield accuracy in 95%.

Case study on application of digital radiography in cable

Because of the frequent occurrence of power cable fault, rapid and accurate fault diagnosis is an important subject in this field. In this paper, the DR detection cases of cables and related components with different voltage levels are described and analyzed based on the research work of radiographic detection carried out by our research group in the field of cables and their accessories in the recent three years. The results show that the technology can effectively detect and analyze the internal damage of cable outer breaking point, the ablative defect of cable buffer layer, the size and position deviation of cable joint. Due to the large number of cable layers and material types, the paper also gives some solutions to the problem of shielding copper core and some examples of abnormal image identification. Cable ontology, cable joints and other accessories produced by different manufacturers have certain structural differences due to numerous processes and procedures. It is necessary to continue to carry out research on DR testing for cable engineering structural parts of different types, establish relevant standard comparison atlas and provide reference for the application of DR technology in the field of cable testing.

A comparative study on image-based snake identification using machine learning

Automated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients. In this study, for the first time, it’s been attempted to compare the accuracy of a series of state-of-the-art machine learning methods, ranging from the holistic to neural network algorithms. The study is performed on six snake species in Lar National Park, Tehran Province, Iran. In this research, the holistic methods [k-nearest neighbors (kNN), support vector machine (SVM) and logistic regression (LR)] are used in combination with a dimension reduction approach [principle component analysis (PCA) and linear discriminant analysis (LDA)] as the feature extractor. In holistic methods (kNN, SVM, LR), the classifier in combination with PCA does not yield an accuracy of more than 50%, But the use of LDA to extract the important features significantly improves the performance of the classifier. A combination of LDA and SVM (kernel = 'rbf') is achieved to a test accuracy of 84%. Compared to holistic methods, convolutional neural networks show similar to better performance, and accuracy reaches 93.16% using MobileNetV2. Visualizing intermediate activation layers in VGG model reveals that just in deep activation layers, the color pattern and the shape of the snake contribute to the discrimination of snake species. This study presents MobileNetV2 as a powerful deep convolutional neural network algorithm for snake image classification that could be used even on mobile devices. This finding pave the road for generating mobile applications for snake image identification.

BUSCA DE PALAVRAS CHAVE EM IMAGENS DE LIVROS IMPRESSOS USANDO VISÃO COMPUTACIONAL

With the increasingly frequent use of books in digital format, people search for the desired subjects in a faster way compared to the search in physical books. This work aimed to develop a computational resource in the form of an application for Android smartphones, which, based on an image captured from a page in a book, performs searches by keywords. The purpose of using the application is to help the reader to find the desired information quickly. We use Computer Vision techniques with the aid of the OpenCV library in the development of algorithms to perform segmentation, correction of the perspective of the book page image, identification and rectification of the wavy lines, recognition and character classification. The results shown were promising with a hit rate of over 88%.