Pearson's Correlation Coefficient for Discarding Redundant Information in Real Time Autonomous Navigation System

Neto,; Rittner,; Leite,; Zampieri,; Lotufo,; Mendeleck,

doi:10.1109/isic.2007.4359698

Cited by 10 publications

(12 citation statements)

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“…where µ 1 − µ 2 is the difference in the means of two classes, and σ 1 + σ 2 is the sum of their standard deviations. Considering that the USI is a linear combination of features, Pearson's Correlation Coefficient (PCC) analysis [34] was used to examine the linear correlation between two band ratios. The closer a correlation coefficient to 1 or −1 is, the more significant the linear relation is, indicating that one band ratio is more likely to be superseded by the other.…”

Section: Spectral Features Of Water and Non-water Typesmentioning

confidence: 99%

Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

Zhang

et al. 2018

Remote Sensing

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Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

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Section: Spectral Features Of Water and Non-water Typesmentioning

confidence: 99%

Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

Zhang

et al. 2018

Remote Sensing

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“…As a measure of the disparity level between the reference and wavelet-transformed vorticity fields (or images), we computed the Pearson correlation coefficient (R), a statistical measure comparing between two or more image matrices [58,59]. Vorticity (ω) and wavelet-transformed vorticity fields (ω) were treated as image matrices of the same order (m × n), and R ωω was computed using Equation (11).…”

Section: Estimation Of Shannon Entropy-aided Optimal Wavelet Scale Onmentioning

confidence: 99%

Shannon Entropy-Based Wavelet Transform Method for Autonomous Coherent Structure Identification in Fluid Flow Field Data

Bulusu

Plesniak

2015

Entropy

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Abstract:The coherent secondary flow structures (i.e., swirling motions) in a curved artery model possess a variety of spatio-temporal morphologies and can be encoded over an infinitely-wide range of wavelet scales. Wavelet analysis was applied to the following vorticity fields: (i) a numerically-generated system of Oseen-type vortices for which the theoretical solution is known, used for bench marking and evaluation of the technique; and (ii) experimental two-dimensional, particle image velocimetry data. The mother wavelet, a two-dimensional Ricker wavelet, can be dilated to infinitely large or infinitesimally small scales. We approached the problem of coherent structure detection by means of continuous wavelet transform (CWT) and decomposition (or Shannon) entropy. The main conclusion of this study is that the encoding of coherent secondary flow structures can be achieved by an optimal number of binary digits (or bits) corresponding to an optimal wavelet scale. The optimal wavelet-scale search was driven by a decomposition entropy-based algorithmic approach and led to a threshold-free coherent structure detection method. The method presented in this paper was successfully utilized in the detection of secondary flow structures in three clinically-relevant blood flow scenarios involving the curved artery model under a carotid artery-inspired, pulsatile inflow condition. These scenarios were: (i) a clean curved artery; (ii) stent-implanted curved artery; and (iii) an idealized Type IV stent fracture within the curved artery.Entropy 2015, 17 6618

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“…The discarding criteria was presented as a simple solution to improve the performance of a real-time navigation system by choosing, in an automatic way, which images should be discarded and which ones should be treated at the visual perception system [13]. It was a new approach using the PCC.…”

Section: Discarding Criteriamentioning

confidence: 99%

“…Based on Pearson's method, we have proposed a visualperception system based on an automatic image discarding method as a simple solution to improve the performance of a real-time navigation system by exploiting the temporal coherence between consecutive frames [13]. This idea is also presented in the key-frame selection technique [14].…”

Section: Introductionmentioning

confidence: 99%

Image processing using Pearson's correlation coefficient: Applications on autonomous robotics

Neto

Victorino

Fantoni

et al. 2013

2013 13th International Conference on Autonomous Robot Systems

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Autonomous robots have motivated researchers from different groups due to the challenge that it represents. Many applications for control of autonomous platform are being developed and one important aspect is the excess of information, frequently redundant, that imposes a great computational cost in data processing. Taking into account the temporal coherence between consecutive frames, we have proposed a set of tools based on Pearson's Correlation Coefficient (PCC): (i) a Discarding Criteria methodology was proposed and applied as (ii) a Dynamic Power Management solution; (iii) an environment observer method based on PCC selects automatically only the Regions-Of-Interest; and taking place in the obstacle avoidance context, (iv) a method for Collision Risk Estimation was proposed for vehicles in dynamic and unknown environments.Applying the PCC to these tasks has not been done yet, making the concepts unique. All these solutions have been evaluated from real data obtained by experimental vehicles.

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Pearson's Correlation Coefficient for Discarding Redundant Information in Real Time Autonomous Navigation System

Cited by 10 publications

References 0 publications

Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

Shannon Entropy-Based Wavelet Transform Method for Autonomous Coherent Structure Identification in Fluid Flow Field Data

Image processing using Pearson's correlation coefficient: Applications on autonomous robotics

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