Image enhancement for outdoor long‐range surveillance using IQ‐learning multiscale Retinex

Liu, Haoting; Lu, Hanqing

doi:10.1049/iet-ipr.2016.0972

Cited by 18 publications

(8 citation statements)

References 30 publications

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“…On the other hand, the constant tuning of laser parameters during cleaning will also decrease the lifetime and the stability of laser. Thus after the researches of the image features [47] and the laser cleaning effect [48], a two-stage algorithm is developed.…”

Section: F Discussionmentioning

confidence: 99%

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Liu

Yang

et al. 2020

IEEE Access

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An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user's demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

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Section: F Discussionmentioning

confidence: 99%

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Liu

Yang

et al. 2020

IEEE Access

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“…wherex 1 is the spare solution; A is a matrix for the entire training set; RA is the matrix of features; x is a coefficient vector; y is the features of the test set; ε spare is a given error tolerance, and the symbol ε spare is set to 0.05 in the experiments of this investigation; Ω test is the facial vector of a test face; Ω train is the facial vector of a train face; ε PCA is a given distance threshold, and the symbol ε PCA is set to 0.01 in the experiments of this investigation. As for the second evaluation index, three IQAM metrics are employed: the blind/ referenceless image spatial quality evaluator (BRISQUE) [34], the image region contrast degree (IRCD) [35], and the Brenner-based image sharpness (BIS) [36]. The BRISQUE can evaluate the image edges and details.…”

Section: Methodsmentioning

confidence: 99%

Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

2021

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A hierarchic clustering-based enhancement is proposed to solve the luminance compensation of face recognition in the dark field. First, the face image is divided into five levels by a clustering method. Second, the results above are mapped into three hierarchies according to the histogram thresholds. A low, a middle, and a high-intensity block are found. Third, two kinds of linear transforms are performed to the high and the low-intensity blocks. Finally, a center wrap function-based enhancement is carried out. Experiment results show our method can improve both the face recognition accuracy and image quality.

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“…Comparing with the data processing of the inertial navigation, the computation of the image processing algorithm is really complex; as a result, the visual navigation needs its processing unit to contain the high data processing performance and the large power consumption. Another problem is that the visual sensor is sensitive to the environment light [41]. All kinds of atmosphere phenomena will create great negative influences on its processing result.…”

Section: The Horizon Sensormentioning

confidence: 99%

Autonomous Navigation for Mars Exploration

Liu¹

2020

Mars Exploration - A Step Forward

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The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed.

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Image enhancement for outdoor long‐range surveillance using IQ‐learning multiscale Retinex

Cited by 18 publications

References 30 publications

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

Autonomous Navigation for Mars Exploration

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