Results 41 to 50 of about 3,600 (211)
ALFO: Adaptive Light Field Over-Segmentation
IEEE Access, 2021 Automatic image over-segmentation into superpixels has attracted increasing attention from researchers to apply it as a pre-processing step for several computer vision applications.
Maryam Hamad +3 more
doaj +1 more source
Remote Sensing, 2020 In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral
Lei Pan, Chengxun He, Yang Xiang, Le Sun
doaj +1 more source
Remote Sensing, 2018 Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High ...
Zeinab Gharibbafghi +2 more
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Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach
IEEE Access, 2021 Superpixel algorithms group visually coherent pixels and form an alternative representation of the regular structure of the pixel grid. This fundamental low-level computer vision preprocessing step greatly reduces the complexity of subsequent image ...
Serge Bobbia +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Wetland is of significant ecological value, which is very important and challenging for large-scale mapping. Sentinel-1 can continuously record wetland changes with its all-day, all-weather working capability.
Hui Yang +3 more
doaj +1 more source
Persistence-based resolution-independent meshes of superpixels [PDF]
, 2020 The over-segmentation problem is to split a pixel-based image into a smaller number of superpixels that can be treated as indecompasable regions to speed up higher level image processing such as segmentation or object detection.
Muszynski, G, Kurlin, V
core +1 more source
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network
Remote Sensing, 2022 Synthetic Aperture Radar (SAR) is the primary equipment used to detect oil slicks on the ocean’s surface. On SAR images, oil spill regions, as well as other places impacted by atmospheric and oceanic phenomena such as rain cells, upwellings, and internal
Xiaojian Liu +3 more
doaj +1 more source
IEEE Transactions on Image Processing, 2020 Superpixel segmentation is a fundamental computer vision technique that finds application in a multitude of high level computer vision tasks. Most state-of-the-art superpixel segmentation methods are unsupervised in nature and thus cannot fully utilize frequently occurring texture patterns or incorporate multiscale context.
Utkarsh Gaur, B. S. Manjunath
openaire +5 more sources
Superpixels, Occlusion and Stereo [PDF]
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011 Graph-based energy minimization is now the state of the art in stereo matching methods. In spite of its outstanding performance, few efforts have been made to enhance its capability of occlusion handling. We propose an occlusion constraint, an iterative optimization strategy and a mechanism that proceeds on both the digital pixel level and the super ...
Yuhang Zhang 0001 +3 more
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Sizes of Superpixels and their Effect on Interactive Segmentation [PDF]
, 2021 Semi-automated segmentation, also known as interactive image segmentation, is an algorithm that extracts a region of interest (ROI) from an image based on user input.
Muzaffar, Hamzah +3 more
core +2 more sources