- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Focal Boundary Guided Salient Object Detection
摘要： The performance of salient object segmentation has been significantly advanced by using deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations, and might hinder downstream applications that require precise object shapes. To address this issue, we propose a novel deep model—Focal Boundary Guided (Focal-BG) network. Our model is designed to jointly learn to segment salient object masks and detect salient object boundaries. Our key idea is that additional knowledge about object boundaries can help to precisely identify the shape of the object. Moreover, our model incorporates a refinement pathway to refine the mask prediction, and makes use of the focal loss to facilitate the learning of the hard boundary pixels. To evaluate our model, we conduct extensive experiments. Our Focal-BG network consistently outperforms state-of-the-art methods on five major benchmarks. We provide a detailed analysis of these results and demonstrate that our joint modeling of salient object boundary and mask helps to better capture shape details, especially in the vicinity of object boundaries.
关键词： Salient Object Segmentation,Deep Learning,Visual Saliency Detection,Boundary Detection
更新于2025-09-23 15:23:52
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Optical plasma boundary reconstruction based on least squares for EAST Tokamak
摘要： Reconstructing the shape and position of plasma is an important issue in Tokamaks. Equilibrium and fitting (EFIT) code is generally used for plasma boundary reconstruction in some Tokamaks. However, this magnetic method still has some inevitable disadvantages. In this paper, we present an optical plasma boundary reconstruction algorithm. This method uses EFIT reconstruction results as the standard to create the optimally optical reconstruction. Traditional edge detection methods cannot extract a clear plasma boundary for reconstruction. Based on global contrast, we propose an edge detection algorithm to extract the plasma boundary in the image plane. Illumination in this method is robust. The extracted boundary and the boundary reconstructed by EFIT are fitted by same-order polynomials and the transformation matrix exists. To acquire this matrix without camera calibration, the extracted plasma boundary is transformed from the image plane to the Tokamak poloidal plane by a mathematical model, which is optimally resolved by using least squares to minimize the error between the optically reconstructed result and the EFIT result. Once the transform matrix is acquired, we can optically reconstruct the plasma boundary with only an arbitrary image captured. The error between the method and EFIT is presented and the experimental results of different polynomial orders are discussed.
关键词： Least square,Global contrast,EAST Tokamak,Optical boundary reconstruction,Boundary detection
更新于2025-09-23 15:22:29
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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 - Deep Hybrid Wavelet Network for Ice Boundary Detection in Radra Imagery
摘要： This paper proposes a deep convolutional neural network approach to detect ice surface and bottom layers from radar imagery. Radar images are capable to penetrate the ice surface and provide us with valuable information from the underlying layers of ice surface. In recent years, deep hierarchical learning techniques for object detection and segmentation greatly improved the performance of traditional techniques based on hand-crafted feature engineering. We designed a deep convolutional network to produce the images of surface and bottom ice boundary. Our network take advantage of undecimated wavelet transform to provide the highest level of information from radar images, as well as multilayer and multi-scale optimized architecture. In this work, radar images from 2009-2016 NASA Operation IceBridge Mission are used to train and test the network. Our network outperformed the state-of-the-art accuracy.
关键词： Deep learning,Wavelet transform,Holistically nested edge detection,Radar,Ice Boundary detection
更新于2025-09-19 17:15:36
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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) - Encapsulation Techniques Using Electrically Conductive Adhesives for Building-Integrated Photovoltaics
摘要： Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs.
关键词： boundary detection and tracking,diffusion model,Wireless sensor networks (WSNs),source localization
更新于2025-09-19 17:13:59