- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - High Open-Circuit Voltage in Wide-Bandgap Perovskite Photovoltaics with Passivation Layers Based on Large Cations
摘要： Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by ?tting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.
关键词： image processing,overlapping objects,machine vision,convex objects,Segmentation
更新于2025-09-23 15:21:01
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[IEEE 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC) - Greater Noida, India (2019.10.18-2019.10.19)] 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC) - Formulation of Efficiency of Inverters for Solar Photovoltaic Power Plants - Indian Case Study
摘要： Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by fitting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.
关键词： image processing,overlapping objects,machine vision,convex objects,Segmentation
更新于2025-09-23 15:21:01
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || Automated and Robust Geographic Atrophy Segmentation for Time Series SD-OCT Images
摘要： Geographic atrophy (GA), mainly characterized by atrophy of the retinal pigment epithelium (RPE), is an advanced form of age-related macular degeneration (AMD) which will lead to vision loss. Automated and robust GA segmentation in three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) images is still an enormous challenge. This paper presents an automated and robust GA segmentation method based on object tracking strategy for time series SD-OCT volumetric images. Considering the sheer volume of data, it is unrealistic for experts to segment GA lesion region manually. However, in our proposed scenario, experts only need to manually calibrate GA lesion area for the ?rst moment of each patient, and then the GA of the following moments will be automatically detected. In order to fully embody the outstanding features of GA, a new sample construction method is proposed for more e?ectively extracting histogram of oriented gradient (HOG) features to generate random forest models. The experiments on SD-OCT cubes from 10 eyes in 7 patients with GA demonstrate that our results have a high correlation with the manual segmentations. The average of correlation coe?cients and overlap ratio for GA projection area are 0.9881 and 82.62%, respectively.
关键词： HOG features,Spectral-domain optical coherence tomography,Image segmentation,Geographic atrophy
更新于2025-09-23 15:21:01
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || Large-Scale Structure from Motion with Semantic Constraints of Aerial Images
摘要： Structure from Motion (SfM) and semantic segmentation are two branches of computer vision. However, few previous methods integrate the two branches together. SfM is limited by the precision of traditional feature detecting method, especially in complicated scenes. As the research ?eld of semantic segmentation thrives, we could gain semantic information of high con?dence in each speci?c task with little e?ort. By utilizing semantic segmentation information, our paper presents a new way to boost the accuracy of feature point matching. Besides, with the semantic constraints taken from the result of semantic segmentation, a new bundle adjustment method with equality constraint is proposed. By exploring the sparsity of equality constraint, it indicates that constrained bundle adjustment can be solved by Sequential Quadratic Programming (SQP) e?ciently. The proposed approach achieves state of the art accuracy, and, by grouping the descriptors together by their semantic labels, the speed of putative matches is slightly boosted. Moreover, our approach demonstrates a potential of automatic labeling of semantic segmentation. In a nutshell, our work strongly veri?es that SfM and semantic segmentation bene?t from each other.
关键词： Sequential Quadratic Programming,Equality bundle adjustment,Semantic segmentation,Structure from Motion
更新于2025-09-23 15:21:01
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || Automatic Measurement of Cup-to-Disc Ratio for Retinal Images
摘要： Glaucoma is a chronic eye disease which results in irreversible vision loss, and the optic cup-to-disc ratio (CDR) is an essential clinical indicator in diagnosing glaucoma, which means precise optic disc (OD) and optic cup (OC) segmentation become an important task. In this paper, we propose an automatic CDR measurement method. The method includes three stages: OD localization and ROI extraction, simultaneous segmentation of OD and OC, and CDR calculation. In the ?rst stage, the morphological operation and the sliding window are combined to ?nd the OD location and extract the ROI region. In the second stage, an improved deep neural network, named U-Net+CP+FL, which consists of U-shape convolutional architecture, a novel concatenating path and a multi-label fusion loss function, is adopted to simultaneously segment the OD and OC. Based on the segmentation results, the CDR value can be calculated in the last stage. Experimental results on the retinal images from public databases demonstrate that the proposed method can achieve comparable performance with ophthalmologist and superior performance when compared with other existing methods. Thus, our method can be a suitable tool for automated glaucoma analysis.
关键词： OD&OC segmentation,Glaucoma diagnosis,Deep neural network,OD localization,Cup-to-disc ratio (CDR)
更新于2025-09-23 15:21:01
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Building Extraction from High-Resolution Remotely Sensed Imagery Based on Multi-subgraph Matching
摘要： Building extraction is still a dif?cult issue in the ?eld of remote sensing. In order to extract the buildings with similar structures ef?ciently, an algorithm based on multi-subgraph matching is proposed using only the panchromatic high-resolution remotely sensed imagery (RSI). Firstly, scale-invariant feature transform feature is detected within both RSI and building template, and the corresponding graphs are constructed. Then, binary matching rules are de?ned to reconstruct the graphs to reduce the complexity. At last, according to the homogeneity of the building top, disconnected subgraphs are isolated from the reconstructed graphs. To improve the algorithm accuracy, the matched subgraphs are optimized on the basis of the differences in the structure and size. For verifying the validity of the proposed method, nine representatives are chosen from GF-2 images covering Guangzhou, China. Experimental results show that the precision and recall of the proposed method are 97.73% and 87.16%, respectively, and its overall performance F1 is higher than the three other similar methods.
关键词： Building extraction,Remotely sensed imagery,Multi-subgraph matching,Graph segmentation,SIFT
更新于2025-09-23 15:21:01
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Image-based relighting using image segmentation and bootstrap strategy
摘要： Image-based relighting technologies enable us to recover the illumination effects of modeled scenes under new light conditions without complicated geometrical information. However, most of them are troubled by specialized devices and tedious sampling work. In this study, we propose an efficient and accurate image-based relighting method for the estimation of the light transport matrix of modeled scene, starting from a small number of images acquired with a fixed viewpoint and with lighting sampled over a uniform 2D grid. Especially, the image space is segmented based on the position and average color value of each pixel using K-means. The local coherence among the pixels can be considered to associate with pixel position and pixels’ albedo. The pixels of each cluster can be trained by several neural networks and the training scene datasets can be chosen using the bootstrap strategy. These tricks improve the regression performance. We validate our method with light transport data of several scenes containing complex lighting effects. The obtained results show that the proposed method is useful for practical applications and we can get more plausible rendered images with fewer input images in comparison to related techniques.
关键词： Image-based relighting,Image segmentation,K-means,Neural network,Bootstrap strategy
更新于2025-09-23 15:21:01
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Optical scanning holography for tumor extraction from brain magnetic resonance images
摘要： Tumor segmentation from magnetic resonance images (MRI) is an error-prone and time-absorbing process. Recently, optical methods have opened a new avenue to tack with the aforementioned problem. In this paper, we propose a novel architecture adapting the Optical Scanning Holography (OSH) to the detection of the abnormal tissue regions in MRI. The proposed method combines an o?-axis optical scan, performed by a heterodyne fringe pattern, and a MR image display ensured by a spatial light modulator. The output in-phase component of the scanned current is collected digitally. Hence, a high-precision distribution of biological tissues is extracted using this in-phase component. Its maximum position is exactly the one of the tumor. Meanwhile, this position is used in an Active Contour Model (ACM) to perform a fast segmentation of the extent corresponding to the tumors. Several images of brain tumors from BRATS database, with tumors having di?erent contrast and form, are used to test the proposed system. Parameters reverted by the optical process are used to investigate the detection performance. Further, in terms of tumor segmentation, the proposed OSH-ACM process has high performance metrics compared to some of recently published method. The underlying physics of the precision superiority, presented by the OSH-ACM, is the high-precision extraction of the abnormal tissue regions by the in-phase component of the scanned current.
关键词： Active contour,Optical Scanning Holography (OSH),Segmentation,Brain tumor detection,In-phase component of the scanned current
更新于2025-09-23 15:21:01
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[IEEE 2018 International Conference on System Science and Engineering (ICSSE) - New Taipei City, Taiwan (2018.6.28-2018.6.30)] 2018 International Conference on System Science and Engineering (ICSSE) - Optical SAR Fusion of Sentinel-2 Images for Mapping High Resolution Land Cover
摘要： Sentinel-2 is a very new programme of the European Space Agency (ESA) that is designed for fine spatial resolution global monitoring. Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of the South Solok region, West Sumatera. Data pre-processing was carried out using the European Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. The two main objectives of this study are to evaluate the potential use and synergetic effects of ESA Sentinel-1A C-band SAR and Sentinel-2A Optical data for classification and mapping of LCLU. As a result of the research, two main advantages. First, the pre-processing chain supported by sensor-specific toolboxes developed by ESA represents a reliable and fast approach for the preparation of ready-to-process imagery. Second, investigation to derive a methodological framework to integrate Sentinel-1 and Sentinel-2 imagery for land cover mapping by integrating of radar and optical imagery have been set up and tested.
关键词： segmentation,Sentinel-1,SAR,South Solok,Sentinel-2,land cover mapping,data fusion
更新于2025-09-23 15:21:01
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PhaseNet: A Deep Convolutional Neural Network for 2D Phase Unwrapping
摘要： Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2π) at each pixel by semantic segmentation, and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well known MATLAB’s unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that Deep Convolutional Neural Network (DCNN) can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.
关键词： Encoder,Decoder,Phase Unwrapping,Deep Convolutional Neural Network,Semantic Segmentation
更新于2025-09-23 15:21:01