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[HTML][HTML] A survey on deep learning-based change detection from high-resolution remote sensing images
Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …
remote sensing analysis, and it has been widely used in many areas, such as resources …
Mininet: An efficient semantic segmentation convnet for real-time robotic applications
Efficient models for semantic segmentation, in terms of memory, speed, and computation,
could boost many robotic applications with strong computational and temporal restrictions …
could boost many robotic applications with strong computational and temporal restrictions …
Developments in deep learning for change detection in remote sensing: A review
Deep learning (DL) algorithms have become increasingly popular in recent years for remote
sensing applications, particularly in the field of change detection. DL has proven to be …
sensing applications, particularly in the field of change detection. DL has proven to be …
Neural architecture search for image saliency fusion
Saliency detection methods proposed in the literature exploit different rationales, visual
clues, and assumptions, but there is no single best saliency detection algorithm that is able …
clues, and assumptions, but there is no single best saliency detection algorithm that is able …
COCOA: combining color constancy algorithms for images and videos
We present an efficient combination strategy for color constancy algorithms. We define a
compact neural network architecture to process and combine the illuminant estimations of …
compact neural network architecture to process and combine the illuminant estimations of …
Evaluating CNN-based semantic food segmentation across illuminants
In this paper we aim to explore the potential of Deep Convolutional Neural Networks
(DCNNs) on food image segmentation where semantic segmentation paradigm is used to …
(DCNNs) on food image segmentation where semantic segmentation paradigm is used to …
EPNet: Efficient patch-based deep network for real-time semantic segmentation
PC vision is the one that causes the machine to comprehend the highlights of different
photographs and recording. The division of picture is progressively getting a matter of PC …
photographs and recording. The division of picture is progressively getting a matter of PC …
Ink classification in historical documents using hyperspectral imaging and machine learning methods
Ink identification using only spectral reflectance information poses significant challenges
due to material degradation, aging, and spectral overlap between ink classes. This study …
due to material degradation, aging, and spectral overlap between ink classes. This study …
Semantic segmentation network stacking with genetic programming
Semantic segmentation consists of classifying each pixel of an image and constitutes an
essential step towards scene recognition and understanding. Deep convolutional encoder …
essential step towards scene recognition and understanding. Deep convolutional encoder …
Training efficient semantic segmentation CNNs on multiple datasets
In the past few years, various datasets for semantic segmentation have been presented.
However, dense per-pixel groundtruths are difficult and expensive to obtain, therefore every …
However, dense per-pixel groundtruths are difficult and expensive to obtain, therefore every …