Results 71 to 80 of about 5,165 (221)
Early smoke detection of forest fires based on SVM image segmentation
Journal of Forest Science, 2019 A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method
Ding Xiong, Lu Yan
doaj +1 more source
Journal of Applied Clinical Medical Physics, Volume 27, Issue 4, April 2026.Abstract Background Accurate classification of brain tumors is a major challenge in neuro‐oncology, as the heterogeneity of tumor morphology and the overlap of radiological features limit the effectiveness of conventional diagnostic approaches. Early and reliable tumor characterization is essential for treatment planning, prognosis, and improved ...
Mus'ab S. Alkasasbeh +7 more
wiley +1 more source
Learning Superpixel Relations for Supervised Image Segmentation
, 2014 In this paper we propose to extend the well known graph cut segmentation framework by learning superpixel relations and use them to weight superpixel-to-superpixel edges in a superpixel graph.
Costantino Grana +5 more
core +1 more source
Advanced Science, Volume 13, Issue 24, 27 April 2026.A conditional multi‐task deep learning framework is developed for designing and optimizing Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces (FHPEMs). This framework achieves joint spectro‐polarimetric learning and unified forward–inverse design.
Chenjie Gong +9 more
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019 Markov random field (MRF) based methods have been widely used in high spatial resolution (HSR) image classification. However, many existing MRF-based methods put more emphasis on pixel level contexts while less on superpixel level contextual information.
Yu Shen +3 more
doaj +1 more source
A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification
CAAI Transactions on Intelligence Technology, Volume 11, Issue 2, Page 464-482, April 2026.ABSTRACT Transformers have been widely applied to hyperspectral image classification, leveraging their self‐attention mechanism for powerful global modelling. However, two key challenges remain as follows: excessive memory and computational costs from calculating correlations between all tokens (especially as image size or spectral bands increase) and ...
Yuquan Gan +5 more
wiley +1 more source
, 2011 Superpixels are small image segments that are used in popular approaches to object detection and recognition problems. The superpixel approach is motivated by the observation that pixels within small image segments can usually be attributed the same ...
Moore, A.P.
core
Superpixels Optimized by Color and Shape [PDF]
, 2018 Image over-segmentation is formalized as the approximation problem when a large image is segmented into a small number of connected superpixels with best fitting colors. The approximation quality is measured by the energy whose main term is the sum of squared color deviations over all pixels and a regularizer encourages round shapes.
Vitaliy Kurlin, Donald Harvey
openaire +1 more source
Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.This systematic review of 352 studies establishes a comprehensive taxonomy for Interpretable Machine Learning, transitioning from foundational intrinsic models to advanced deep learning explanations. It reveals a critical paradigm shift toward “mechanistic interpretability” and actionable recourse, emphasizing that future AI systems must be rigorously ...
Shimon Fridkin, Michael Bendersky
wiley +1 more source
Texture-Aware Superpixel Segmentation
, 2019 International audienceMost superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture
Berthoumieu, Yannick +3 more
core +2 more sources