Derivation and analysis of a filtered backprojection algorithm for cone beam projection data

The authors address the problem of three-dimensional image reconstruction from cone beam projections. Modifying a result due to A.A. Kirillov (Soviet Math. Dokl., vol. 2, p.268-9, 1961), the authors derive an inversion formula for the case where the cone vertices form an unbounded curve. For the special case in which the cone vertices form a circle, an approximate reconstruction formula is developed and shown to be essentially equivalent to the practical cone-beam algorithm of L.A. Feldkamp et al. (1984). For this approximate inverse, the authors derive the resulting spatially varying point spread function, examine the effect of bandlimiting due to sampling, and compare the resulting image quality as a function of the radius of the circle formed by the cone vertices.     

Fast‐moving coin recognition using deep learning

Multimedia Tools and Applications - Deep learning has made essential contributions to the development of visual object detection and recognition. Identifying fast-moving objects from the viewpoint...     

An ultrahigh-resolution image encryption algorithm using random super-pixel strategy

Multimedia Tools and Applications - Almost all existing image encryption algorithms are only suitable for low-resolution images in the standard image library. When they are used to encrypt...     

Image super-resolution using supervised multi-scale feature extraction network

Multimedia Tools and Applications - Image super-resolution using deep convolutional networks have recently achieved great successes. However, previous studies have failed to consider the spatial...     

Full-scaled deep metric learning for pedestrian re-identification

Multimedia Tools and Applications - The pedestrian re-identification problem (i.e., re-id) is essential and pre-requisite in multi-camera video surveillance studies, provided the fact that...     

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Machine Intelligence Research - One of the most significant challenges in the neuroscience community is to understand how the human brain works. Recent progress in neuroimaging techniques have...     

基于粒子群优化BP神经网络的超声波缺陷位置预测

海洋设备的无损检测对于保障设备安全使用至关重要,由于设备所处的海洋环境特殊,要实现对海洋设备缺陷位置的准确估计难度较大.论文提出了一种基于粒子群优化BP神经网络(PSO-BP)预测设备缺陷位置的方法.通过模拟了海洋检测环境,用钢板作为实验对象,用超声波探伤仪对缺陷钢板进行了数据采集,获取了水下钢板的实时数据.通过粒子群算法对BP网络进行优化后对数据进行分析,对比缺陷出现的实际位置和预测位置,证明了粒子群优化的BP神经网络对于超声波检测的缺陷出现位置具有较好的预测效果.