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MACHINE LEARNING ALGORITHMS FOR IMAGE PROCESSING APPLICATIONS

Multi-channel Joint Sparse Learning Model for Non-rigid Three-dimensional Object Classification

Li Han, et al.; Liaoning Normal University

Abstract

In order to solve the issues of inadequate feature description and inefficient feature learning model existing in current classification methods, this article proposes a multi-channel joint sparse learning model for three-dimensional (3D) non-rigid object classification. First, the authors adopt a multi-level measurement of intrinsic properties to create complementary shape descriptors. Second, they build independent and informative bag of features (BoF) by embedding these shape descriptors into the visual vocabulary space. Third, a max-dependency and min-redundancy criterion is applied for optimal feature filtering on each BoF dictionary based on mutual...

Research on Spinal Canal Generation Method based on Vertebral Foramina Inpainting of Spinal CT Images by Using BEGAN

Li Ding, et al.; Shenzhen Institutes of Advanced Technology

Abstract

Spinal surgery is of high risk due to the possibility of neurologic damage, which may cause life-threatening sequelae. Although the emerging robotic-assisted spinal surgery provides better accuracy compared with traditional surgery, the construction of boundary constraints around the spinal canal for safety in surgery is still required. The establishment of a three-dimensional (3D) model of the spinal canal during preoperative preparation can facilitate the generation of surgical boundary constraints. This article presents a novel framework for spinal canal generation based on spinal CT image inpainting by using the boundary equilibrium generative adversarial network (BEGAN)...

Estimating Pigment Concentrations from Spectral Images Using an Encoder-Decoder Neural Network

Kensuke Fukumoto, Norimichi Tsumura, and Roy Berns; Chiba University

Abstract

A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder-decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka-Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used...

Uncertainty Rank for Streamline Ensembles

Brad E. Hollister and Alex Pang

A Disparity Refinement in Stereo Matching based on Mean-shift Segmentation and Spatiotemporal Domain

Hui-Yu Huang and Zhe-Hao Liu

Three-dimensional Reconstruction of Microscopic Image Based on Multi-ST Algorithm

Min Zhu, Rongfu Zhang, Pei Ma, Xuedian Zhang, and Qi Guo

Apple Leaf Disease Identification Through Region-of-Interest-Aware Deep Convolutional Neural Network

Hee-Jin Yu, Chang-Hwan Son, and Dong Hyuk Lee

Medical Image Segmentation based on U-Net: A Review

Getao Du, Xu Cao, Jimin Liang, Xueli Chen, and Yonghua Zhan

Affective Effect of Multi-source Portraits Under Various Illuminants on One Scene

Yuchun Yan and Hyeon-Jeong Suk

Advanced Image Analysis for Learning Underlying Partial Differential Equations for Anomaly Identification

Andrew Miller, Jan Petrich, and Shashi Phoha

MACHINE LEARNING ALGORITHMS FOR IMAGE PROCESSING APPLICATIONS

Multi-channel Joint Sparse Learning Model for Non-rigid Three-dimensional Object Classification

Li Han, et al.; Liaoning Normal University

Abstract

In order to solve the issues of inadequate feature description and inefficient feature learning model existing in current classification methods, this article proposes a multi-channel joint sparse learning model for three-dimensional (3D) non-rigid object classification. First, the authors adopt a multi-level measurement of intrinsic properties to create complementary shape descriptors. Second, they build independent and informative bag of features (BoF) by embedding these shape descriptors into the visual vocabulary space. Third, a max-dependency and min-redundancy criterion is applied for optimal feature filtering on each BoF dictionary based on mutual...

Research on Spinal Canal Generation Method based on Vertebral Foramina Inpainting of Spinal CT Images by Using BEGAN

Li Ding, et al.; Shenzhen Institutes of Advanced Technology

Abstract

Spinal surgery is of high risk due to the possibility of neurologic damage, which may cause life-threatening sequelae. Although the emerging robotic-assisted spinal surgery provides better accuracy compared with traditional surgery, the construction of boundary constraints around the spinal canal for safety in surgery is still required. The establishment of a three-dimensional (3D) model of the spinal canal during preoperative preparation can facilitate the generation of surgical boundary constraints. This article presents a novel framework for spinal canal generation based on spinal CT image inpainting by using the boundary equilibrium generative adversarial network (BEGAN)...

Estimating Pigment Concentrations from Spectral Images Using an Encoder-Decoder Neural Network

Kensuke Fukumoto, Norimichi Tsumura, and Roy Berns; Chiba University

Abstract

A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder-decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka-Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used...