Results 121 to 130 of about 1,159 (170)

Multimodal medical image fusion based on interval gradients and convolutional neural networks. [PDF]

BMC Med Imaging
Gu X, Xia Y, Zhang J.
europepmc   +1 more source

Segmentation-free Radon transform algorithm to detect orientation and size of tissue structures in multiphoton microscopy images. [PDF]

J Biomed Opt
Brandt D, Nikishina AA, Bias A, Günther R, Hauser AE, Duda GN, Beckers IE, Niesner RA.   +7 more
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Update of machine learning for ultrasound diagnosis of metabolic dysfunction-associated steatotic liver disease: a bright future for deep learning. [PDF]

PeerJ
Li J, Chen J, Zeng X, Lyu G, Lin S, He S.   +5 more
europepmc   +1 more source

A hybrid machine learning framework for offline signature verification using gray wolf optimization. [PDF]

Sci Rep
Rathore NC, Juneja A, Kumar N, Kumar V, Dhaka A.   +4 more
europepmc   +1 more source

A new generative method for multi-focus image fusion of underwater micro bubbles. [PDF]

Sci Rep
Li X, Zong S, Duan Z, Yang S, Chen B, Lin Q.   +5 more
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Quantifying <i>in vivo</i> collagen reorganization during immunotherapy in murine melanoma with second harmonic generation imaging. [PDF]

Biophotonics Discov
Heaton AR, Burkard NJ, Sondel PM, Skala MC.   +3 more
europepmc   +1 more source

Improved imaging interfaces on a co-registered ultrasound and optical microscopy multiscale system. [PDF]

J Biomed Opt
Hale J, Cudworth N, Farrell R, Bhargava A, Eliceiri KW, Rosado-Mendez I.   +5 more
europepmc   +1 more source

Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification. [PDF]

J Imaging Inform Med
Rodriges Moreira LF, Backes AR.
europepmc   +1 more source

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Multiscale Curvelet Scattering Network

IEEE Transactions on Neural Networks and Learning Systems, 2023
Feature representation has received more and more attention in image classification. Existing methods always directly extract features via convolutional neural networks (CNNs). Recent studies have shown the potential of CNNs when dealing with images' edges and textures, and some methods have been explored to further improve the representation process ...
Jie Gao, Licheng Jiao, Fang Liu
exaly   +3 more sources

Quaternionic curvelet transform

Optik, 2017
Abstract In this paper, we extend the continuous curvelet transform to the space of quaternion valued functions using convolution. We prove that the quaternionic curvelet transform is consistent with the continuous curvelet transform of complex valued functions.
L. Akila, R. Roopkumar
openaire   +1 more source