scholarly journals Mineral Photos Recognition Based on Feature Fusion and Online Hard Sample Mining

Minerals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1354
Author(s):  
Liqin Jia ◽  
Mei Yang ◽  
Fang Meng ◽  
Mingyue He ◽  
Hongmin Liu
Keyword(s):  
Feature Fusion ◽  
Recognition Accuracy ◽  
Good Prospect ◽  
Special Equipment ◽  
Comprehensive Information ◽  
In The Wild ◽  
Image Set ◽  
Available Information ◽  
Fold Cross Validation ◽  
Geological Research

Mineral recognition is of importance in geological research. Traditional mineral recognition methods need professional knowledge or special equipment, are susceptible to human experience, and are inconvenient to carry in some conditions such as in the wild. The development of computer vision provides a possibility for convenient, fast, and intelligent mineral recognition. Recently, several mineral recognition methods based on images using a neural network have been proposed for this aim. However, these methods do not exploit features extracted from the backbone network or available information of the samples in the mineral dataset sufficiently, resulting in low recognition accuracy. In this paper, a method based on feature fusion and online hard sample mining is proposed to improve recognition accuracy by using only mineral photo images. This method first fuses multi-resolution features extracted from ResNet-50 to obtain comprehensive information of mineral photos, and then proposes the weighted top-k loss to emphasize the learning of hard samples. Based on a dataset consisting of 14,986 images of 22 common minerals, the proposed method with 10-fold cross-validation achieves a Top1 accuracy of 88.01% on the validation image set, surpassing those of Inception-v3 and EfficientNet-B0 by a margin of 1.88% and 1.29%, respectively, which demonstrates the good prospect of the proposed method for convenient and reliable mineral recognition using mineral photos only.

scholarly journals 3D Motion Trajectories Recognition using Optimal Set of Geometric Primitives, Angular and Statistical Features

2019 ◽  
Vol 9 (2) ◽  
pp. 2972-2979
Keyword(s):  
Cross Validation ◽  
Recognition Accuracy ◽  
Svm Classifier ◽  
Statistical Features ◽  
3D Motion ◽  
Motion Trajectories ◽  
Optimal Subset ◽  
Optimal Set ◽  
Geometric Primitives ◽  
Fold Cross Validation

This paper presents the 3D motion trajectories (lower case 3D alphabetic characters) recognition using optimal set of geometric primitives, angular and statistical features. It has been observed that the different combinations of these features have not been used in the literature for recognition of 3D characters. The standard dataset named “CHAR3D” has been used for analysis purpose. The dataset consists of 2858 character samples and each character sample is 3 dimensional pen tip velocity trajectory. In this dataset only single pen down segmented characters have been considered. The recognition has been performed using Random Forest (RF) and multiclass support vector machine (SVM) classifier on the optimal subset of extracted features. The best obtained recognition accuracy of 83.4% has been recorded using 3D points, angular and statistical features at 10 fold cross validation using SVM classifier. Moreover, the highest recognition accuracy of 96.88% has been recorded using an optimal subset of 32 dimensional features namely, geometric primitives, angular and statistical features at 10 fold cross validation by RF classifier.

An Explainable Framework for Diagnosis of COVID-19 Pneumonia via Transfer Learning and Discriminant Correlation Analysis

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-16
Author(s):  
Siyuan Lu ◽  
Di Wu ◽  
Zheng Zhang ◽  
Shui-Hua Wang
Keyword(s):  
Correlation Analysis ◽  
Feature Fusion ◽  
Classification Performance ◽  
Image Features ◽  
Image Feature ◽  
Functional Link ◽  
Image Representations ◽  
Learning Machine ◽  
Lung Window ◽  
Fold Cross Validation

The new coronavirus COVID-19 has been spreading all over the world in the last six months, and the death toll is still rising. The accurate diagnosis of COVID-19 is an emergent task as to stop the spreading of the virus. In this paper, we proposed to leverage image feature fusion for the diagnosis of COVID-19 in lung window computed tomography (CT). Initially, ResNet-18 and ResNet-50 were selected as the backbone deep networks to generate corresponding image representations from the CT images. Second, the representative information extracted from the two networks was fused by discriminant correlation analysis to obtain refined image features. Third, three randomized neural networks (RNNs): extreme learning machine, Schmidt neural network and random vector functional-link net, were trained using the refined features, and the predictions of the three RNNs were ensembled to get a more robust classification performance. Experiment results based on five-fold cross validation suggested that our method outperformed state-of-the-art algorithms in the diagnosis of COVID-19.

scholarly journals Towards Optimization of Boosting Models for Formation Lithology Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yunxin Xie ◽  
Chenyang Zhu ◽  
Yue Lu ◽  
Zhengwei Zhu
Keyword(s):  
Cross Validation ◽  
Confusion Matrix ◽  
Petroleum Engineering ◽  
Gradient Boosting ◽  
Gas Field ◽  
Lithology Identification ◽  
Extreme Gradient Boosting ◽  
Evaluation Matrix ◽  
Fold Cross Validation ◽  
Geological Research

Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.

Underwater Sediments Echoes Recognition Based on KECCA + PLS

2013 ◽  
Vol 310 ◽  
pp. 629-633
Author(s):  
Bo Wen Luo ◽  
Bu Yan Wan ◽  
Wei Bin Qin ◽  
Ji Yu Xu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Classification Model ◽  
Nuclear Space ◽  
Nonlinear Feature

In order to solve the nonlinear feature fusion of underwater sediments echoes, the shortage of Enhanced Canonical Correlation Analysis (ECCA) was analyzed and made ECCA extend to Kernel ECCA (KECCA) in the nuclear space, a multi-feature nonlinear fusion classification model with KECCA combining with Partial Least-Square (PLS ) was put forward。In the process of identifying four types of underwater sediment such as Basalt, Volcanic breccia, Cobalt crusts and Mudstone, the results showed that the recognition accuracy could be further improved for the KECCA + PLS model.

Complete Guide to Finding the Mammals of Australia

2015 ◽  
Author(s):  
David Andrew
Keyword(s):  
Wild Mammal ◽  
Recent Growth ◽  
In The Wild ◽  
Field Workers ◽  
Available Information ◽  
First Time ◽  
Biological Field ◽  
Dolphin Watching

Australia has a rich and unique array of animals, including the largest diversity of marsupials on earth. The recent growth in ecotourism has increased the popularity of mammal-spotting, particularly whale and dolphin-watching, but also spotting of perennial tourist favourites such as koalas and kangaroos. Birdwatchers have for many years known of sites where special or difficult-to-see species may be reliably located. However, despite their comparative abundance and spectacular diversity, many of Australia's unique mammals remain under-appreciated because there has been little available information on where to see them – until now. For the first time ever, The Complete Guide to Finding the Mammals of Australia advises interested amateurs and professionals where to locate many of Australia's mammals. The book describes Australia's best mammal-watching sites state-by-state. It also includes a complete, annotated taxonomic list with hints on finding each species (or why it won't be easy to see); sections on travel and logistics in Australia; and appendices with hints on finding and photographing mammals. This book will be of interest to anyone wanting to observe or photograph Australian mammals in the wild, mammal enthusiasts, biological field workers and volunteers, tourists and ecotourists.

Collisions and Damage of Offshore Structures: A State-of-the-Art

1985 ◽  
Vol 107 (3) ◽  
pp. 297-314 ◽  
Author(s):  
C. P. Ellinas ◽  
S. Valsgard
Keyword(s):  
State Of The Art ◽  
Offshore Structures ◽  
The State ◽  
Design Parameters ◽  
Future Research ◽  
Research Activity ◽  
Comprehensive Information ◽  
Major Interest ◽  
Offshore Industry ◽  
Available Information

Over the recent years, following the very rapid increase in the construction and installation of offshore structures, there has been a considerable growth of interest in the assessment of the probabilities and consequences of collision and damage of such structures. This is reflected by the very large number of papers published over the last 15 yr and the multitude of conferences and meetings held on the subject. Many research programs have been completed or are in progress at many centers and institutions over the world. Accidental loading and damage are now accepted design parameters recommended for consideration in a number of Codes for the design in offshore structures. This paper reviews the state-of-the-art with respect to the probabilities and consequences of collisions and accidental loading in general, and methods for the assessment of the design of steel offshore structures against damage. Most of the available information in the field of offshore collisions and accidental loading emanates from research and experience related to ship safety. However, in this paper emphasis is placed on research activity and available information concerned with offshore structures, such as platforms, semisubmersibles, etc. There is a considerable amount of information available on methods for evaluating the extent and effects on damage of these structures and in estimating their residual strength in the damaged condition. As this is an area currently of major interest in the offshore industry, the paper presents comprehensive information and some new results relating to all major structural components. The state-of-the-art with regards to methods and principles for design against damage is also reviewed and commented upon. The paper concludes with general recommendations and indications of areas where future research could be most usefully directed.

scholarly journals Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality

2022 ◽  
Vol 18 (1) ◽  
pp. 1-31
Author(s):  
Guohao Lan ◽  
Zida Liu ◽  
Yunfan Zhang ◽  
Tim Scargill ◽  
Jovan Stojkovic ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Image Recognition ◽  
Large Scale ◽  
Data Augmentation ◽  
Recognition Accuracy ◽  
Image Distortion ◽  
Mobile Augmented Reality ◽  
The Real ◽  
In The Wild ◽  
Mobile Ar

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2020 ◽  
Author(s):  
dongshen ji ◽  
yanzhong zhao ◽  
zhujun zhang ◽  
qianchuan zhao
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

Test of English vocabulary recognition based on natural language processing and corpus system

2020 ◽  
pp. 1-12
Author(s):  
Duan Longjiang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Experimental Conditions ◽  
Adaptive Kernel ◽  
Update Rate ◽  
Scale Detection ◽  
Vocabulary Recognition

English vocabulary recognition has certain applications in both learning and life. The existing English vocabulary recognition model is limited by a variety of factors, which will result in a more complicated recognition process and a low recognition accuracy. In order to improve the effect of English vocabulary recognition, based on natural language processing algorithms and corpus systems, this paper proposes a multi-feature fusion adaptive kernel-related filter tracking algorithm for the problems of kernel-related filtering algorithms. Moreover, based on the KCF algorithm, this paper improves the algorithm from three parts: feature fusion, adaptive change of update rate, and scale detection. In addition, this paper explores whether the vocabulary recognition of different rhythms will affect the reaction time and accuracy of the second language vocabulary recognition when the test subjects are in the experimental conditions with similar characters and different voices. The research results show that the model constructed in this paper performs well in the recognition of English words.

scholarly journals Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4333
Author(s):  
Pengfei Zhao ◽  
Lijia Huang ◽  
Yu Xin ◽  
Jiayi Guo ◽  
Zongxu Pan
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Recognition Method ◽  
Training Samples ◽  
Model Training ◽  
Sar Target Recognition

At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.

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