scholarly journals Image Feature Analysis and Dynamic Measurement of Plantar Pressure Based on Fusion Feature Extraction

2021 ◽  
Vol 38 (6) ◽  
pp. 1829-1835
Author(s):  
Ji Zou ◽  
Chao Zhang ◽  
Zhongjing Ma ◽  
Lei Yu ◽  
Kaiwen Sun ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Plantar Pressure ◽  
Dynamic Measurement ◽  
Image Features ◽  
Image Feature ◽  
Feature Analysis ◽  
Fusion Algorithm ◽  
Massive Datasets ◽  
Processing Power ◽  
Fusion Feature

Footprint recognition and parameter measurement are widely used in fields like medicine, sports, and criminal investigation. Some results have been achieved in the analysis of plantar pressure image features based on image processing. But the common algorithms of image feature extraction often depend on computer processing power and massive datasets. Focusing on the auxiliary diagnosis and treatment of foot rehabilitation of foot laceration patients, this paper explores the image feature analysis and dynamic measurement of plantar pressure based on fusion feature extraction. Firstly, the authors detailed the idea of extracting image features with a fusion algorithm, which integrates wavelet transform and histogram of oriented gradients (HOG) descriptor. Next, the plantar parameters were calculated based on plantar pressure images, and the measurement steps of plantar parameters were given. Finally, the feature extraction effect of the proposed algorithm was verified, and the measured results on plantar parameters were obtained through experiments.

scholarly journals Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Feature Identification ◽  
Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

scholarly journals Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tianming Song ◽  
Xiaoyang Yu ◽  
Shuang Yu ◽  
Zhe Ren ◽  
Yawei Qu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Medical Image ◽  
Medical Images ◽  
Clinical Analysis ◽  
Image Features ◽  
Processing Method ◽  
The Body ◽  
Image Feature ◽  
The One

Medical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of medical image feature extraction, there are some problems, such as inconspicuous feature extraction and low feature preparation rate. Combined with the learning idea of convolution neural network, the image multifeature vectors are quantized in a deeper level, which makes the image features further abstract and not only makes up for the one-sidedness of single feature description but also improves the robustness of feature descriptors. This paper presents a medical image processing method based on multifeature fusion, which has high feature extraction effect on medical images of chest, lung, brain and liver, and can better express the feature relationship of medical images. Experimental results show that the accuracy of the proposed method is more than 5% higher than that of other methods, which shows that the performance of the proposed method is better.

An Improved FastICA Algorithm and its Application in Image Feature Extraction

2011 ◽  
Vol 204-210 ◽  
pp. 1485-1489
Author(s):  
Li Juan Chen ◽  
Xiang Jun Zou ◽  
Bing Bing Chen ◽  
Yan Chen ◽  
Jing Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Initial Point ◽  
Image Features ◽  
Image Feature ◽  
Higher Order Statistics ◽  
Image Feature Extraction ◽  
Fastica Algorithm ◽  
Litchi Fruit ◽  
Selection Of

There are most important features hiding in the higher-order statistics information of the images. They are extracted by classical fast-fixed independent component analysis (FastICA algorithm) which requires a large amount of calculation and it is sensitive on the selection of initial point. To overcome the two shortcomings, an improved FastICA algorithm is proposed and mathematical models are constructed. And they are applied to obtain the basic vectors from the images. Finally, take litchi fruit image in natural environment as an instance and experiment with Matlab software. The results show that there are less computation and stronger stability of the improved FashICA algorithm used to extract image features.

scholarly journals Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Chuanbao Niu ◽  
Mingzhu Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Recognition System ◽  
Image Features ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Ancient Ceramics ◽  
Ceramic Vessel ◽  
Ancient Ceramic

This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.

DISCRIMINATORY SPARSE CODING AND ITS APPLICATION TO FACE RECOGNITION

2003 ◽  
Vol 03 (03) ◽  
pp. 503-521
Author(s):  
JUN SUN ◽  
WENYUAN WANG ◽  
QING ZHUO ◽  
CHENGYUAN MA
Keyword(s):  
Feature Extraction ◽  
Sparse Coding ◽  
Recognition Performance ◽  
Classification Performance ◽  
Image Features ◽  
Face Image ◽  
Image Feature ◽  
Training Samples ◽  
The Subject ◽  
Independent Features

Feature extraction is very important in the subject of pattern recognition. Sparse coding is an approach for extracting the independent features of an image. The image features extracted by sparse coding have led to better recognition performance as compared to those from traditional PCA-based methods. A new discriminatory sparse coding (DSC) algorithm is proposed in this paper to further improve the classification performance. Based on reinforcement learning, DSC encodes the training samples by individual class rather than by individual image as in sparse coding. Having done that it will produce a set of features with large and small intraclass variations, which is very suitable for recognition tasks. Experiments are performed on face image feature extraction and recognition. Compared with the traditional PCA- and ICA-based methods, DSC shows a much better recognition performance.

scholarly journals A Deep Learning Approach for Retinal Image Feature Extraction

2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Mohammed Enamul Hoque ◽  
Kuryati Kipli ◽  
Tengku Mohd Afendi Zulcaffle ◽  
Abdulrazak Yahya Saleh Al-Hababi ◽  
Dayang Azra Awang Mat ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Retinal Image ◽  
Image Data ◽  
Disease Diagnosis ◽  
Image Features ◽  
Image Feature ◽  
Diagnostic Tools ◽  
Retinal Images ◽  
Retinal Microvasculature

Retinal image analysis is crucially important to detect the different kinds of life-threatening cardiovascular and ophthalmic diseases as human retinal microvasculature exhibits remarkable abnormalities responding to these disorders. The high dimensionality and random accumulation of retinal images enlarge the data size, that creating complexity in managing and understating the retinal image data. Deep Learning (DL) has been introduced to deal with this big data challenge by developing intelligent tools. Convolutional Neural Network (CNN), a DL approach, has been designed to extract hierarchical image features with more abstraction. To assist the ophthalmologist in eye screening and ophthalmic disease diagnosis, CNN is being explored to create automatic systems for microvascular pattern analysis, feature extraction, and quantification of retinal images. Extraction of the true vessel of retinal microvasculature is significant for further analysis, such as vessel diameter and bifurcation angle quantification. This study proposes a retinal image feature, true vessel segments extraction approach exploiting the Faster RCNN. The fundamental Image Processing principles have been employed for pre-processing the retinal image data. A combined database assembling image data from different publicly available databases have been used to train, test, and evaluate this proposed method. This proposed method has obtained 92.81% sensitivity and 63.34 positive predictive value in extracting true vessel segments from the top first tier of colour retinal images. It is expected to integrate this method into ophthalmic diagnostic tools with further evaluation and validation by analysing the performance.

Quantitative CT imaging features for hepatocellular carcinoma (HCC) with b-catenin (CTNNB1) gene mutation.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 253-253
Author(s):  
Ahmed M. Khalaf ◽  
David T. Fuentes ◽  
Kareem Ahmed ◽  
Reham Abdel-Wahab ◽  
Manal Hassan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Gene Mutation ◽  
Liver Tissue ◽  
Quantitative Imaging ◽  
Image Features ◽  
Ct Imaging ◽  
Image Feature ◽  
Imaging Feature ◽  
Imaging Features ◽  
Ct Studies

253 Background: To determine whether CT imaging features can provide quantitative biomarkers to differentiate HCC with pathologic B-catenin gene mutation and those without mutation. Methods: Quantitative imaging features were extracted from a database of manually labeled liver with enhancing and non-enhancing tumor tissue,which were established using multiphasic CT images from 17 patients. CT studies were done before each patient underwent surgical removal of the HCC, which were subjected to pathologic analysis to evaluate B-catenin mutation.The mean period between the CT studies and the pathologic analyses was 18 days. According to the pathology results, the patients were divided into two groups: HCC with CTNNB1 mutation and HCC without. Image feature extraction included image gradients, co-occurrence matrix, and pixel neighborhood statistics of the first, second, and third moments. Pairwise analyses of the imaging features were performed on the mutated and non-mutated HCC images and the background liver tissue of both groups. Independent samples t-test and Mann Whitney U test were performed to quantitatively compare between the means of the imaging features extracted from the tumor tissues of both groups and those extracted from the background liver tissue of both groups. Results: Imaging feature analysis of the pairwise difference between the mutated and non-mutated HCC scans for multiple pixel-neighborhood image features are statistically significant.The top stratifying image features include the skewness (p = 0.02), energy (p = .03), and entropy (p = .03) during the venous and arterial phase. Conclusions: This preliminary study demonstrates the feasibility of quantitative imaging feature extraction from CE-CT imaging to differentiate between HCC with proven B-catenin gene mutation and those without mutation. Non-invasive methods of identifying HCC with B-catenin mutations may be clinically beneficial since B-catenin is an important potential target in novel cancer therapies, and identifying B-catenin mutations may also help provide information regarding prognosis.Verifying the quantitative features in larger patient populations is needed to confirm the results of this study.

scholarly journals Feature Extraction for Medical CT Images of Sports Tear Injury

2020 ◽  
Author(s):  
Qi Nie ◽  
Ye-bing Zou ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Feature Extraction ◽  
Auxiliary Information ◽  
Low Frequency ◽  
Frequency Component ◽  
Ct Images ◽  
Image Features ◽  
Image Feature ◽  
Ct Image ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Abstract Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast decomposition method. The high- and low-frequency information of CT images is enhanced by wavelet layered multi-directional image enhancement algorithm, and the multi-scale enhancement for medical CT images of sports tear injury is completed. Then, edge of the enhanced CT images is extracted using an image edge extraction algorithm based on extended mathematical morphology. Finally, based on the extracted edge information of CT images, feature extraction for medical CT images of sports tear injury is completed by the NSCT-GLCM based CT image feature extraction algorithm. Research results show that the proposed algorithm effectively extracts CT image features of sports tear injury and provides auxiliary information for doctor diagnosis.

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