Image Features Extraction, Selection and Fusion for Computer Vision

2016 ◽  
pp. 75-107
Author(s):  
Anca Apatean ◽  
Alexandrina Rogozan ◽  
Abdelaziz Bensrhair
Keyword(s):  
Computer Vision ◽  
Image Features ◽  
Features Extraction

scholarly journals Comparison of data analytics strategies in computer vision systems to predict pig body composition traits from 3D images

2020 ◽  
Vol 98 (8) ◽  
Author(s):  
Arthur F A Fernandes ◽  
João R R Dórea ◽  
Bruno Dourado Valente ◽  
Robert Fitzgerald ◽  
William Herring ◽  
...  
Keyword(s):  
Computer Vision ◽  
Data Analytics ◽  
Fourier Transforms ◽  
Predictive Ability ◽  
Image Features ◽  
Machine Learning Techniques ◽  
Finishing Pigs ◽  
Vision Systems ◽  
3D Images ◽  
Computer Vision Systems

Abstract Computer vision systems (CVS) have been shown to be a powerful tool for the measurement of live pig body weight (BW) with no animal stress. With advances in precision farming, it is now possible to evaluate the growth performance of individual pigs more accurately. However, important traits such as muscle and fat deposition can still be evaluated only via ultrasound, computed tomography, or dual-energy x-ray absorptiometry. Therefore, the objectives of this study were: 1) to develop a CVS for prediction of live BW, muscle depth (MD), and back fat (BF) from top view 3D images of finishing pigs and 2) to compare the predictive ability of different approaches, such as traditional multiple linear regression, partial least squares, and machine learning techniques, including elastic networks, artificial neural networks, and deep learning (DL). A dataset containing over 12,000 images from 557 finishing pigs (average BW of 120 ± 12 kg) was split into training and testing sets using a 5-fold cross-validation (CV) technique so that 80% and 20% of the dataset were used for training and testing in each fold. Several image features, such as volume, area, length, widths, heights, polar image descriptors, and polar Fourier transforms, were extracted from the images and used as predictor variables in the different approaches evaluated. In addition, DL image encoders that take raw 3D images as input were also tested. This latter method achieved the best overall performance, with the lowest mean absolute scaled error (MASE) and root mean square error for all traits, and the highest predictive squared correlation (R2). The median predicted MASE achieved by this method was 2.69, 5.02, and 13.56, and R2 of 0.86, 0.50, and 0.45, for BW, MD, and BF, respectively. In conclusion, it was demonstrated that it is possible to successfully predict BW, MD, and BF via CVS on a fully automated setting using 3D images collected in farm conditions. Moreover, DL algorithms simplified and optimized the data analytics workflow, with raw 3D images used as direct inputs, without requiring prior image processing.

scholarly journals Application of Deep Learning in Liver Pathological Image Diagnosis

2018 ◽  
Vol 232 ◽  
pp. 02051 ◽  
Author(s):  
Yan Wang ◽  
Jin Liu ◽  
Bing Yu
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Medical Image ◽  
Correct Diagnosis ◽  
Image Features ◽  
Classification Model ◽  
High Incidence ◽  
Diagnosis Method ◽  
Important Basis ◽  
Pathological Image

Hepatopathy is a kind of disease with high incidence, so the progress in the field of liver disease research is highly valued. Medical image, as an important part of medical diagnosis, provides an important basis for doctors to make correct diagnosis. Pathological images play a significant role in clinical application because they can facilitate doctors to clearly observe the degree of lesions and make accurate judgments. As an important component of computer vision, deep learning has been paid more and more attention by researchers. The application of computer aided technology in medical image detection has become an important application of computer vision. In view of this situation, an automatic diagnosis method of liver pathological images based on deep learning method is proposed. We analyze the image features, then design and train the classification model. The final results confirmed that this method can effectively classify the pathological images of liver and has a high accuracy rate.

Image Features Extraction and Fusion Based on Joint Sparse Representation

2011 ◽  
Vol 5 (5) ◽  
pp. 1074-1082 ◽  
Author(s):  
Nannan Yu ◽  
Tianshuang Qiu ◽  
Feng Bi ◽  
Aiqi Wang
Keyword(s):  
Sparse Representation ◽  
Image Features ◽  
Features Extraction ◽  
Joint Sparse Representation

scholarly journals An Image Detection Method Based on Parameter Optimization of Support Vector Machine

2021 ◽  
Vol 15 ◽  
pp. 306-314
Author(s):  
Zhanshen Feng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Parameter Optimization ◽  
Visual Processing ◽  
Image Data ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Image Detection

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

scholarly journals Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning

2021 ◽  
pp. 242-251
Author(s):  
Waishan Qiu ◽  
Wenjing Li ◽  
Xun Liu ◽  
Xiaokai Huang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Urban Design ◽  
Design Theory ◽  
Large Scale ◽  
Image Features ◽  
Subjective Measures ◽  
Visual Elements ◽  
Urban Amenities ◽  
Human Perceptions

AbstractRecently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.

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