Deep Learning Image Segmentation Based on Adaptive Total Variation Preprocessing.

This article proposes a two-stage image segmentation method based on the MS model, aiming to enhance the segmentation accuracy of images with complex structure and background. In the first stage, in order to obtain the smooth approximate solution of the image by minimizing the energy functional, an anisotropic regularization term formed by the combination of the gradient operator and an adaptive weighted matrix is introduced. Different weights in both horizontal and vertical directions can be provided by the adaptive weighting matrix according to the gradient information, so that the curve diffuses along the directions of local feature tangents of the objects.

A novel stochastic resonance based deep residual network for fault diagnosis of rolling bearing system.

Rolling bearings constitute one of the most vital components in mechanical equipment, monitoring and diagnosing the condition of rolling bearings is essential to ensure safe operation. In actual production, the collected fault signals typically contain noise and cannot be accurately identified. In the paper, stochastic resonance (SR) is introduced into a spiking neural network (SNN) as a feature enhancement method for fault signals with varying noise intensities, combining deep learning with SR to enhance classification accuracy.

MSL-Net: Sharp Feature Detection Network for 3D Point Clouds.

As a significant geometric feature of 3D point clouds, sharp features play an important role in shape analysis, 3D reconstruction, registration, localization, etc. Current sharp feature detection methods are still sensitive to the quality of the input point cloud, and the detection performance is affected by random noisy points and non-uniform densities. In this paper, using the prior knowledge of geometric features, we propose a Multi-scale Laplace Network (MSL-Net), a new deep-learning-based method based on an intrinsic neighbor shape descriptor, to detect sharp features from 3D point clouds.

Bitstream-based Perceptual Quality Assessment of Compressed 3D Point Clouds.

With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model.

Deep Spiking Residual Shrinkage Network for Bearing Fault Diagnosis.

Bearing fault diagnosis of electrical equipment has been a popular research area in recent years because there are often some faults during continuous operation in production due to the harsh working environment. However, the traditional fault signal processing methods rely on highly expert experience, and some parameters are difficult to be optimized by machine-learning methods. Thus, the satisfactory recognition accuracy of fault diagnosis cannot be achieved in the above methods.

Iterative deep neural networks based on proximal gradient descent for image restoration.

The algorithm unfolding networks with explainability of algorithms and higher efficiency of Deep Neural Networks (DNN) have received considerable attention in solving ill-posed inverse problems. Under the algorithm unfolding network framework, we propose a novel end-to-end iterative deep neural network and its fast network for image restoration. The first one is designed making use of proximal gradient descent algorithm of variational models, which consists of denoiser and reconstruction sub-networks.

Prediction of Drug-Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding.

Nowadays, drug-target interactions (DTIs) prediction is a fundamental part of drug repositioning. However, on the one hand, drug-target interactions prediction models usually consider drugs or targets information, which ignore prior knowledge between drugs and targets. On the other hand, models incorporating priori knowledge cannot make interactions prediction for under-studied drugs and targets.

A no-Reference Stereoscopic Image Quality Assessment Network Based on Binocular Interaction and Fusion Mechanisms.

In contemporary society full of stereoscopic images, how to assess visual quality of 3D images has attracted an increasing attention in field of Stereoscopic Image Quality Assessment (SIQA). Compared with 2D-IQA, SIQA is more challenging because some complicated features of Human Visual System (HVS), such as binocular interaction and binocular fusion, must be considered. In this paper, considering both binocular interaction and fusion mechanisms of the HVS, a hierarchical no-reference stereoscopic image quality assessment network (StereoIF-Net) is proposed to simulate the whole quality perception of 3D visual signals in human cortex, including two key modules: BIM and BFM.

New dual method for elastica regularization.

The Euler's elastica energy regularizer has been widely used in image processing and computer vision tasks. However, finding a fast and simple solver for the term remains challenging. In this paper, we propose a new dual method to simplify the solution.

Multimodal Medical Supervised Image Fusion Method by CNN.

This article proposes a multimode medical image fusion with CNN and supervised learning, in order to solve the problem of practical medical diagnosis. It can implement different types of multimodal medical image fusion problems in batch processing mode and can effectively overcome the problem that traditional fusion problems that can only be solved by single and single image fusion. To a certain extent, it greatly improves the fusion effect, image detail clarity, and time efficiency in a new method.

Craniofacial similarity analysis through sparse principal component analysis.

The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data.

CPU-GPU mixed implementation of virtual node method for real-time interactive cutting of deformable objects using OpenCL.

Purpose: Surgical simulators need to simulate interactive cutting of deformable objects in real time. The goal of this work was to design an interactive cutting algorithm that eliminates traditional cutting state classification and can work simultaneously with real-time GPU-accelerated deformation without affecting its numerical stability.

Methods: A modified virtual node method for cutting is proposed.

Blurred palmprint recognition based on stable-feature extraction using a Vese-Osher decomposition model.

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese-Osher (VO) decomposition model to recognize blurred palmprints effectively.

Active contour model coupling with higher order diffusion for medical image segmentation.

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model.

Automatic identification and morphometry of optic nerve fibers in electron microscopy images.

The neuroanatomical morphology of the optic nerve is an important description for understanding different aspects like topological distribution of nerves. Manual identification and morphometry has been usually considered as tedious, time consuming, and susceptible to error. A method that automates the identification and analysis of axons from electron micrographic images is presented.