Application of Locally Invariant Robust PCA for Underwater Image Recognition

Bi, Pengfei; Du, Xue

doi:10.1109/access.2021.3058761

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…Roopa and Asha proposed using the principal component analysis (PCA) to improve their diabetes disease prediction approach and achieved a 6.03% increase [60]. For their underwater image recognition study, Bi and Du proposed using the PCA to improve their image recognition rate and achieved a 20.3% increase after applying the PCA to their data [62]. To solve the irregular packing problem, Gua et al proposed a packing algorithm based on the PCA methodology which resulted in an increased filling rate, decreased packing time, and increased packing number as compared to the MGA method [63].…”

Section: Principal Component Analysismentioning

confidence: 99%

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Fernandes

Ullah

2022

IEEE Access

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Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress.

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Section: Principal Component Analysismentioning

confidence: 99%

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Fernandes

Ullah

2022

IEEE Access

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“…In the 1990s, telemetry methods rely upon this Global Positioning System (GPS) have all been developed to continue providing comprehensive information to studies conducted to handle environmental issues and evaluate policies [1]. Furthermore, the GPS has experienced numerous improvements in terms of volume and achievement and a significant reduction in costs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Diagnose the Animal Health Continuous Monitoring Using IoT Based Sensory Data

Kandepan¹,

Venkatesan²

2022

I2M

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Monitoring the health of humans in everyday life is quite challenging. Smart wearables have been increasingly important in recent technologies for monitoring and indicating our current health status, which is utilised to appropriately diagnose our health. Monitoring animals' health raises several concerns. We must wait for veterinary specialists to assess and diagnose whether the animals are suffering only in known regions. The result is delayed treatment and degradation of animal health. Therefore, primary health diagnosis is needed. However, mounting equipment was required to identify these features. This research leads to the recognition of the behaviour of animals through this technique, which facilitates the assessment of the health of animals. The use of Wireless Sensor Networks (WSN) and IoT features an Animals Smart Healthcare Monitoring (ASHM) system for this research to track animal behavior with high precision using sensors and to detect animals' health with great precision. We proposed designing a system that would track the movement and health of an animal. This can be achieved by developing a system with animal smart health monitoring system. The sensor can be mounted on the animal body to get the desired physiological parameters like temperature, heart rate, respiratory rate, ECG, and Blood pressure. This health surveillance system method has reduced the mean latency by 75% and provides results with an output accuracy of 98%.

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“…To further improve the performance of PCA algorithm, l 2,p -norm [19] is proposed. Bi et al [20] proposed locally invariant robust principal component analysis (LIRPCA), which uses l 2,p -norm to constrain PCA to solve the problem of underwater image recognition [21]. Although LIRPCA solves the problem of PCA in image reconstruction to a certain extent, it also reduces the influence of large distance as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Manifold Regularized Principal Component Analysis Method Using L2,p-Norm

et al. 2022

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The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on l2,p-norm PCA and manifold learning method. To improve robustness, the model method adopts l2,p-norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when p=0.5. Finally, the experimental results show that the method proposed in this paper is robust and effective.

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Application of Locally Invariant Robust PCA for Underwater Image Recognition

Cited by 8 publications

References 42 publications

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

A Novel Approach to Diagnose the Animal Health Continuous Monitoring Using IoT Based Sensory Data

Manifold Regularized Principal Component Analysis Method Using L2,p-Norm

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