LAUSR

Pedestrian detection based on gradient and texture feature integration

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0 Published in 2017 at "Neurocomputing"

DOI: 10.1016/j.neucom.2016.09.085

Abstract: In this paper, based on feature integration, we proposed a new method for pedestrian detection. Firstly, we extracted the histogram of oriented gradients (HOG) feature and local binary pattern (LBP) feature from the original images… read more here.

Keywords: detection based; detection; feature integration; pedestrian detection ... See more keywords

Attentive feature integration network for detecting salient objects in images

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0 Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.05.083

Abstract: Abstract Benefiting from the development of convolutional neural networks, salient object detection has yielded a qualitative leap in performance. In recent years, most of deep learning based methods utilize multi-level features and obtain inferred saliency… read more here.

Keywords: saliency; feature; attentive feature; feature integration ... See more keywords

Functional characterization of correct and incorrect feature integration.

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1 Published in 2022 at "Cerebral cortex"

DOI: 10.1093/cercor/bhac147

Abstract: Our sensory system constantly receives information from the environment and our own body. Despite our impression to the contrary, we remain largely unaware of this information and often cannot report it correctly. Although perceptual processing… read more here.

Keywords: functional characterization; feature integration; parietal regions; integration ... See more keywords

FIBS-Unet: Feature Integration and Block Smoothing Network for Single Image Dehazing

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2 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3188860

Abstract: The dehazing algorithms are based on the hazy simulation equation to remove haze and restore the input image feature maps by estimating the intensity coefficient of the atmospheric light source and the scattering coefficient of… read more here.

Keywords: feature integration; fibs unet; feature; integration block ... See more keywords

MCFINet: Multidepth Convolution Network With Shallow-Deep Feature Integration for Semantic Labeling in Remote Sensing Images

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1 Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2021.3065039

Abstract: Semantic labeling in remote sensing images is an important and challenging technique, which has attracted increasing attention recently in earth detection, environmental protection, land utilization, and so on. However, it remains a challenge on how… read more here.

Keywords: semantic labeling; network; feature integration; remote sensing ... See more keywords