- 标题
- 摘要
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- 实验方案
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[IEEE 2019 16th International Conference on the European Energy Market (EEM) - Ljubljana, Slovenia (2019.9.18-2019.9.20)] 2019 16th International Conference on the European Energy Market (EEM) - Shapley-Value-Based Distribution of the Costs of Solar Photovoltaic Plant Grid Connection
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： PET/CT reconstruction,PET/CT,image reconstruction,Image quality assessment
更新于2025-09-16 10:30:52
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A cognitive approach for quality assessment in laser welding
摘要： Quality assessment in laser welding is of outmost importance. A plethora of in-line inspection techniques have been developed identifying melt pool geometry and weld defects for quality evaluation. This paper aims to introduce a cognitive assessment method for the prediction of weld quality and classification into different quality categories. The study corresponds to camera-based monitoring approaches utilizing thermal images obtained from process simulation models where artificial defects were inserted. A dimensionality reduction technique is deployed, and an image processing technique is afterwards implemented to identify weld defects based on specific melt pool features. A classification algorithm has also been developed and validated.
关键词： Laser processing,Defects recognition,Cognitive control,Quality assessment
更新于2025-09-16 10:30:52
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Quality assessment of laser speckle patterns for digital image correlation by a Multi-Factor Fusion Index
摘要： Digital image correlation based on laser speckle has its unique advantages in ultra-high temperature deformation measurement. The traditional quality assessment criteria of arti?cial speckle which consider only the contrast of the speckle pattern or the morphology of the speckle particles are not suitable for laser speckle. Here, a new index, called Multi-Factor Fusion Index (MFFI), which takes the inhomogeneity of gray distribution, the mean square deviation of gray and the standard deviation of speckle particles size into consideration, is proposed to evaluate the quality of laser speckle patterns. Multi-Factor Fusion Index was compared with three commonly used criteria, the mean intensity gradient, the mean intensity of the second derivative, and the pattern quality metric combining the sum of square of subset intensity gradients (SSSIG) and the secondary auto-correlation peak height. The results showed that the proposed index had better capability of assessing the quality of laser speckle patterns. And the in?uence by external factors such as apertures, laser powers and temperatures and material factors such as roughness and light re?ectance were analyzed by MFFI. Experiments on arti?cial speckle showed that MFFI was also suitable for arti?cial speckle patterns in most cases.
关键词： Quality assessment criteria,Laser speckle characteristics,Multi-Factor Fusion Index (MFFI),Digital image correlation (DIC)
更新于2025-09-12 10:27:22
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Sensors performance in laser-based manufacturing process quality assessment: a conceptual framework
摘要： Laser-based manufacturing processes are emerging in manufacturing. Thus, new requirements are created with respect to monitoring, control, and online quality assessment. This work describes a framework for both studying and designing the performance of an IR-vision sensor with respect to real-time on-axis laser-based process quality assessment. More specifically, utilizing physics model at machine level as well as experimental results, requirements are extracted for the efficiency metrics of the sensor. These requirements regard temporal and spatial characteristics of the sensor physical layer, as well as software (algorithmic) level specifications.
关键词： Electro-optical characterisation,Vision system,Laser process quality assessment,IR,Sensor performance
更新于2025-09-12 10:27:22
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Generating Image Distortion Maps Using Convolutional Autoencoders with Application to No Reference Image Quality Assessment
摘要： We present two contributions in this work: (i) a reference-free image distortion map generating algorithm for spatially localizing distortions in a natural scene, and (ii) no reference image quality assessment (NRIQA) algorithms derived from the generated distortion map. We use a convolutional autoencoder (CAE) for distortion map generation. We rely on distortion maps generated by the SSIM image quality assessment (IQA) algorithm as the “ground truth” for training the CAE. We train the CAE on a synthetically generated dataset composed of pristine images and their distorted versions. Specifically, the dataset was created by applying standard distortions such as JPEG compression, JP2K compression, Additive White Gaussian Noise (AWGN) and blur to the pristine images. SSIM maps are then generated on a per distorted image basis for each of the distorted images in the dataset and are in turn used for training the CAE. We first qualitatively demonstrate the robustness of the proposed distortion map generation algorithm over several images with both traditional and authentic distortions. We also demonstrate the distortion map’s effectiveness quantitatively on both standard distortions and authentic distortions by deriving three different NRIQA algorithms. We show that these NRIQA algorithms deliver competitive performance over traditional databases like LIVE Phase II, CSIQ, TID 2013, LIVE MD and MDID 2013, and databases with authentic distortions like LIVE Wild and KonIQ-10K. In summary, the proposed method generates high quality distortion map that are used to design robust NRIQA algorithms. Further, the CAE based distortion maps generation method can easily be modified to work with other ground truth distortion maps.
关键词： Convolutional neural network,no reference image quality assessment (IQA),human visual system (HVS),autoencoders
更新于2025-09-11 14:15:04
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[IEEE 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) - Cagliari (2018.5.29-2018.6.1)] 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) - A High-angular-resolution Turntable Data-set for Experiments on Light Field Visualization Quality
摘要： In this paper, we present a high-angular-resolution data-set created using a turntable arrangement. Seven distinct objects, positioned on an automated turntable, were captured from three camera positions for every half degree of rotation, generating 720 images for each camera position. For each object, the camera positions were registered to the coordinate system of the middle camera. Intrinsic parameters of the camera were also estimated. A data-set of this kind is instrumental for research in a variety of areas, such as light field visualization, manifold learning, visual quality assessment, evaluation of preferred object orientation etc. Due to the availability of three-view stereo, this data-set could be useful for studying view interpolation techniques.
关键词： quality assessment,turntable data-set,Light field,angular resolution
更新于2025-09-11 14:15:04
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[IEEE 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) - Cagliari (2018.5.29-2018.6.1)] 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) - Subjective Assessment of Post-Processing Methods for Low Light Consumer Photos
摘要： Consumer photos taken in low light conditions often suffer from substantial undesired capture artifacts, such as shakiness and sensor noise. In this paper, we use rank ordering method to assess the subjective preferences among different post-processing methods used to alleviate capture artifacts. The results show that most users prefer sharpened photos, even in the presence of substantial sensor noise. However, there are also systematic differences in individual preferences between users. Therefore, user preferences need to be considered in addition to the image characteristics, when selecting the post-processing algorithms and parameters for photo quality enhancement.
关键词： image quality,capture artifacts,rank ordering,subjective quality assessment
更新于2025-09-10 09:29:36
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[IEEE 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - Shanghai, China (2018.9.6-2018.9.7)] 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - No-reference image quality assessment based on dualchannel convolutional neural network
摘要： In recent years, convolutional neural networks have achieved more outstanding results and been widely used in the field of image quality assessment compared with the traditional handcraft method. This paper presents a no-reference image quality assessment method based on dual-channel convolutional neural network. The raw image is labeled by visual information fidelity and divided into multiple patches as input. After that, feature extraction is performed by two network channels with different pooling layers. The features are linearly stitched and sent to the fully connected layer. The experimental results on the LIVE database and the TID2008 database show that our model has the state-of-the-art performance and obtain a better consistency with human subjective assessment.
关键词： convolutional neural network,image quality assessment,dual-channel
更新于2025-09-10 09:29:36
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Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System
摘要： This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup’s imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup’s QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%.
关键词： Quality Assessment,Naive Bayes,Computer Vision,Graphical Model,Agriculture
更新于2025-09-10 09:29:36
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A New Image Quality Metric Using Compressive Sensing And A Filter Set Consisting of Derivative And Gabor Filters
摘要： This paper proposes an image quality metric (IQM) using compressive sensing (CS) and a filter set consisting of derivative and Gabor filters. In this paper, compressive sensing that is used for acquiring a sparse or compressible signal with a small number of measurements is used for measuring the quality between the reference and distorted images. However, an image is generally neither sparse nor compressible, so a CS technique cannot be directly used for image quality assessment. Thus, for converting an image into a sparse or compressible signal, the image is convolved with filters such as the gradient, Laplacian of Gaussian, and Gabor filters, since the filter outputs are generally compressible. A small number of measurements obtained by a CS technique are used for evaluating the image quality. Experimental results with various test images show the effectiveness of the proposed algorithm in terms of the Pearson correlation coefficient (CC), root mean squared error, Spearman rank order CC, and Kendall CC.
关键词： Difference Mean Opinion Score,Gabor Filter,Image Quality Assessment,Compressive Sensing,Derivative Filters
更新于2025-09-10 09:29:36