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Semantic Segmentation of Radio-Astronomical Images
2021In the context of next-generation radio-astronomical visual surveys, automated object detection and segmentation are necessary tasks to support astrophysics research from observations. Indeed, identifying manually astronomical sources (e.g., galaxies) from the daunting amount of acquired images is largely unfeasible, greatly limiting the huge potential
Carmelo Pino +4 more
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A benchmark for semantic image segmentation
2013 IEEE International Conference on Multimedia and Expo (ICME), 2013Though quite a few image segmentation benchmark datasets have been constructed, there is no suitable benchmark for semantic image segmentation. In this paper, we construct a benchmark for such a purpose, where the ground-truths are generated by leveraging the existing fine granular ground-truths in Berkeley Segmentation Dataset (BSD) as well as using ...
Hui Li +3 more
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Optimized HRNet for image semantic segmentation
Expert Systems with Applications, 2021Abstract With the rapid development of deep learning, image semantic segmentation has made great progress and become a hot topic in scene understanding of computer vision. In this paper, we propose an optimized high-resolution net (HRNet) for image semantic segmentation.
Huisi Wu +3 more
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Semantic Segmentation of Fisheye Images
2019Semantic segmentation of fisheye images (e.g., from action-cameras or smartphones) requires different training approaches and data than those of rectilinear images obtained using central projection. The shape of objects is distorted depending on the distance between the principal point and the object position in the image. Therefore, classical semantic
Gregor Blott +2 more
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Effective image restoration for semantic segmentation
Neurocomputing, 2020Abstract Recent semantic segmentation algorithms are greatly accelerated by deep convolutional neural networks (DCNNs). Although most of them perform well on normal images, they are not robust to the degenerations of images. To boost the performance of semantic segmentation on degraded images, we present an effective image restoration framework based
Xuejing Niu +3 more
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Semantic image segmentation for pedestrian detection
Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, 2017A typical traffic monitoring system for pedestrian detection uses a stationary camera. In Advanced Driving Assistance Systems (ADAS), the camera is mounted in front of the vehicle's window so that the camera and the object move in any arbitrary direction. Semantic image segmentation is widely used for road scene interpretation.
Adi Nurhadiyatna, Sven Loncaric
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Semantic image segmentation with deep features
2018 26th Signal Processing and Communications Applications Conference (SIU), 2018Deep convolutional neural networks (CNN) have shown significant success in many classification problems including semantic image segmentation. However training of deep networks is time consuming and requires large training datasets. A network trained in one dataset could be applied to another task or dataset through transfer learning and retraining. As
Sercan Sunetci, Hasan F. Ates
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Pyramidal Model for Image Semantic Segmentation
2010 20th International Conference on Pattern Recognition, 2010We present a new hierarchical model applied to the problem of image semantic segmentation, that is, the association to each pixel in an image with a category label (e.g. tree, cow, building, ...). This problem is usually addressed with a combination of an appearance-based pixel classification and a pixel context model.
Giuseppe Passino +2 more
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Image Segmentation by semantic method
Pattern Recognition, 1987Abstract The problem of region detection is addressed. Linear and quadratic approximation schemes are used to approximate the regions in an image. A set of attributes, which represent the properties of a region, are defined. A distance function, which has structural as well as semantic part, is introduced.
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Collaborative Semantic Segmentation with Image Labels
Proceedings of the 3rd International Conference on Video and Image Processing, 2019Weakly-supervised semantic segmentation has recently received much attention since it needs less fine-grained annotations than fully-supervised learning. Most existing studies use attention maps from the classification network as supervision, which suffers from only locating small discriminative parts of objects and lacking precise boundaries.
Zhikang Li +2 more
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