- 标题
- 摘要
- 关键词
- 实验方案
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Blind Quality Index for Tone-Mapped Images Based on Luminance Partition
摘要： Tone-mapping operators (TMOs), which are designed to convert high dynamic range (HDR) images to standard low dynamic range (LDR) images for displaying on conventional devices, have gained extensive attention recently. The quality of tone-mapped images generated by different TMOs varies significantly, which depends upon the image contents and the parameter settings. A quality index that can accurately evaluate the performances of TMOs is thus highly needed. With this motivation, this paper presents a blind quality index based on luminance partition for tone-mapped images. It is based on the fact that the Human Visual System (HVS) has different sensitivities to image regions with different luminance levels. Specifically, two adaptive thresholds are first employed to segment an image into the dark, bright and normal areas. Then, we calculate the quality-aware features from different luminance areas: 1) local entropy feature is extracted from the dark and bright areas to measure the information loss due to the overexposure or underexposure during the tone mapping process; 2) local colorfulness feature is extracted from the normal area to evaluate the reproduction of colors. With the consideration that the perception of image quality depends on the combined effects of the salient local distortion and global quality degradation, the global contrast feature is also calculated and integrated for better evaluation performance. Moreover, to take advantage of the hierarchical characteristic of the HVS, all features are calculated under a multi-resolution framework. Eventually, the extracted features are mapped into an objective quality score based on the random forest regression. The proposed metric is shown to outperform those state-of-the-art metrics according to extensive experiments conducted on two publicly available databases.
关键词： tone-mapped image,multi-resolution representation,Tone-mapping operators,random forest regression,luminance partition,human visual system
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Comparative Evaluation of Polarimetric Distance Measures within the Random Forest Framework for the Classification of Polsar Images
摘要： Random Forests have been shown to be able to be applied directly to polarimetric synthetic aperture radar (PolSAR) data instead of to extracted hand-crafted features by adapting the internal node tests. This paper investigates different polarimetric distance measures and their potential to be used by Random Forests for the classification of PolSAR images. The experiments show that using distance measures tailored towards the statistics of PolSAR data outperforms the usage of individual hand-crafted polarimetric features and their combination. However, the differences between accuracies obtained by different suitable distance measures are insignificant allowing to take other aspects into consideration such as computational efficiency.
关键词： Random Forest,PolSAR,Polarimetric Distances,Classification,Feature learning
更新于2025-09-23 15:22:29
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Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)
摘要： Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3–77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108–126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and the accuracy assessment of LULC maps revealed that SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.
关键词： Yield estimation,SAR data,Acreage mapping,Random Forest classifier
更新于2025-09-23 15:22:29
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Fast 6D object pose refinement in depth images
摘要： Recovering 6D object pose has gained much focus, because of its application in robotic intelligent manipulation to name but a few. This paper presents an approach for 6D object pose refinement from noisy depth images obtained from a consumer depth sensor. Compared to the state of the art aimed at the same goal, the proposed method has high precision, high robustness to partial occlusions and noise, low computation cost and fast convergence. This is achieved by using an iterative scheme that only employs Random Forest to minimize a cost function of object pose which can quantify the misalignment between the ground truth and the estimated one. The random forest in our algorithm is learnt only using synthetic depth images rendered from 3D model of the object. Several experimental results show the superior performance of the proposed approach compared to ICP-based algorithm and optimization-based algorithm, which are generally used for 6D pose refinement in depth images. Moreover, the iterative process of our algorithm can be much faster than the state of the art by only using one CPU core.
关键词： Object pose refinement,Random forest,Depth images,6D pose estimation,Fast convergence
更新于2025-09-23 15:22:29
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Multi-scale sifting for mammographic mass detection and segmentation
摘要： Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance. In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture. It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance. The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns. This method can accurately segment masses of various shapes and sizes from the background tissue. To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost). The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique. This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP. On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost. On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost. The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.
关键词： Morphological sifting,Mammography,Breast mass detection and segmentation,Cascaded random forest,Ensemble learning
更新于2025-09-23 15:22:29
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Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
摘要： Non-invasive Glucose Measurement (NGM) technology makes great sense for the blood glucose management of patients with hyperglycemia or hypoglycemia. Individual Discrepancies (IDs), e.g., skin thickness and color, not only block the development of NGM, but also become the reason why NGM cannot be widely used. To solve this problem, our solution is designing an individual customized NGM model that can measure these discrepancies through multi-wavelength and tune parameters for glucose estimating. In this paper, an NGM prototype is designed, and a learning model for glucose estimating with automatically parameters tuning based on Independent Component Analysis (ICA) and Random Forest (RF) is presented. The clinic trial proves that the correlation coefficient between estimation and reference Blood Glucose Concentration (BGC) can reach 0.5 after merely 10 times of learning, and rise to 0.8 after about 60 times of learning.
关键词： Independent Component Analysis (ICA),random forest,non-invasive,blood glucose,diabetes
更新于2025-09-23 15:22:29
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BAT algorithm inspired retinal blood vessel segmentation
摘要： The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40-dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors’ proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm-based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.
关键词： digital retinal images,retinal blood vessel segmentation,structured analysis of the retina,feature extraction,BAT algorithm,random forest classifier
更新于2025-09-23 15:21:21
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[IEEE 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) - Lecco, Italy (2019.9.25-2019.9.27)] 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) - Luminaire Digital Design Flow with Delphi4LED LEDs Multi-Domain Compact Model
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-23 15:21:01
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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting
摘要： As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
关键词： ensemble,decision tree,bagging,Light GBM,lagged data,machine learning,random forest,XGBoost,photovoltaic power forecasting
更新于2025-09-23 15:21:01
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[IEEE 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) - Phuket, Thailand (2020.2.28-2020.2.29)] 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) - Performance Evaluation of Faults in a Photovoltaic Array Based on V-I and V-P Characteristic Curve
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-23 15:21:01