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Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition.

ABSTRACT: In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG) features, we find that the world through the eyes of a computer is indeed different from human eyes, which assists researchers to see the reasons that cause a computer to make errors. Additionally, according to the visualization, we notice that the HOG features can obtain rich texture information. However, a large amount of background interference is also introduced. In order to enhance the robustness of the HOG feature, we propose an improved method for suppressing the background interference. On the basis of the original HOG feature, we introduce a principal component analysis (PCA) to extract the principal components of the image colour information. Then, a new hybrid feature descriptor, which is named HOG-PCA (HOGP), is made by deeply fusing these two features. Finally, the HOGP is compared to the state-of-the-art HOG feature descriptor in four scenes under different illumination. In the simulation and experimental tests, the qualitative and quantitative assessments indicate that the visualizing images of the HOGP feature are close to the observation results obtained by human eyes, which is better than the original HOG feature for object detection. Furthermore, the runtime of our proposed algorithm is hardly increased in comparison to the classic HOG feature.

SUBMITTER: Jiao J

PROVIDER: S-EPMC5539970 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable-particularly in the early stages of the diseases. This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data. The irregular gait patterns of NDDs exhibited by vGRF data can indicate different variations of force patterns compared with healthy controls (HC). The classification algorithm in this study comprises three processes: a preprocessing, feature transformation and classification. In the preprocessing process, the 5-min vGRF data divided into 10-s successive time windows. In the feature transformation process, the time-domain vGRF data are modified into an image using a recurrence plot. The total recurrence plots are 1312 plots for HC (16 subjects), 1066 plots for ALS (13 patients), 1230 plots for PD (15 patients) and 1640 plots for HD (20 subjects). The principal component analysis (PCA) is used in this stage for feature enhancement. Lastly, the convolutional neural network (CNN), as a deep learning classifier, is employed in the classification process and evaluated using the leave-one-out cross-validation (LOOCV). Gait data from HC subjects and patients with amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) obtained from the PhysioNet Gait Dynamics in Neurodegenerative disease were used to validate the proposed algorithm. The experimental results included two-class and multiclass classifications. In the two-class classification, the results included classification of the NDD and the HC groups and classification among the NDDs. The classification accuracy for (HC vs. ALS), (HC vs. HD), (HC vs. PD), (ALS vs. PD), (ALS vs. HD), (PD vs. HD) and (NDDs vs. HC) were 100%, 98.41%, 100%, 95.95%, 100%, 97.25% and 98.91%, respectively. In the multiclass classification, a four-class gait classification among HC, ALS, PD and HD was conducted and the classification accuracy of HC, ALS, PD and HD were 98.99%, 98.32%, 97.41% and 96.74%, respectively. The proposed method can achieve high accuracy compare to the existing results, but with shorter length of input signal (Input of existing literature using the same database is 5-min gait signal, but the proposed method only needs 10-s gait signal).

Project description:IntroductionImage recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application.ObjectivesTo improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems.MethodsAs a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand.ResultsBy combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO2 emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life.ConclusionThe technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems.

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Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition.

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