Results 51 to 60 of about 147,704 (285)
Improving Spatial Codification in Semantic Segmentation [PDF]
, 2015 This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem.
Giró-i-Nieto, Xavier +5 more
core +2 more sources
Towards Open-Set Semantic Segmentation Of Aerial Images [PDF]
2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), 2020 Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images.
C. C. V. da Silva +4 more
openaire +5 more sources
Remote Sensing, 2022 With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation.
Yongxiu Zhou +5 more
doaj +1 more source
SEMANTIC SEGMENTATION OF BUILDING IN AIRBORNE IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Abstract. Building is a key component to the reconstructing of LoD3 city modelling. Compared to terrestrial view, airborne datasets have more occlusions at street level but can cover larger area in the urban areas. With the popularity of the Deep Learning, many tasks in the field of computer vision can be solved in easier and efficiency way.
S. Huang, F. Nex, Y. Lin, M. Y. Yang
openaire +4 more sources
Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions
Complexity, 2019 The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations.
Xinying Xu +4 more
doaj +1 more source
Semantic Segmentation of 3D Medical Images with 3D Convolutional Neural Networks
CLEI Electronic Journal, 2020 A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided.
Alejandra Márquez Herrera +2 more
doaj +1 more source
Semantic Image Segmentation with Contextual Hierarchical Models [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which
Mojtaba, Seyedhosseini, Tolga, Tasdizen
openaire +2 more sources
Applied Sciences, 2023 Aerial images are important for monitoring land cover and land resource management. An aerial imaging source which keeps its position at a higher altitude, and which has a considerable duration of airtime, employs wireless communications for sending ...
Kalupahanage Dilusha Malintha De Silva +1 more
doaj +1 more source
Learning Dilation Factors for Semantic Segmentation of Street Scenes
, 2017 Contextual information is crucial for semantic segmentation. However, finding the optimal trade-off between keeping desired fine details and at the same time providing sufficiently large receptive fields is non trivial. This is even more so, when objects
DE Rumelhart +4 more
core +1 more source
International Journal of Digital EarthIn the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive.
Zenan Yang +4 more
doaj +1 more source