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...     

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...     

一种基于IHS和NSST的多光谱图像融合方法

文中针对丛林环境背景下的迷彩人员检测效果差的问题,提出一种基于多光谱图像融合的迷彩人员检测方法.以可见光图像和特征波段图像为数据源,首先将可见光图像变换到相关性最小的IHS颜色空间,提取出亮度分量I;然后对于特征波段图像和亮度分量I进行多级NSST分解变换图像融合,其中特征波段之间采用局部能量取大值融合规则低频分量融合,与亮度分量之间采用自适应模糊逻辑原则进行低频分量融合.高频分量均使用模值取大的融合规则.最后进行NSST逆变换和IHS逆变换得到融合结果.经实验表明,与其他经典和主流多尺度变换图像融合算法相比,该方法具备较好的图像质量评价指标,并且在迷彩人员检测实验中,与可见光图像结果相比,具有较好的检测结果.     

大内跨小边跨框架结构预应力筋优化布置方法

预应力筋的优化布置是大内跨小边跨的多跨预应力混凝土框架结构合理设计计算的关键.为满足控制截面承载力最大的需要,以往常将大跨所需预应力筋布置为连续曲线,并按水平直线延伸通过其相邻的两小跨,按这种布置方式仅使大跨内产生竖向向上的预应力等效荷载,可较好平衡大跨的竖向外荷载;而若将大跨及相邻的两小跨统一考虑,预应力筋按连续曲线布置,张拉产生的预应力等效荷载不但可有效平衡大跨的竖向外荷载而且也可平衡小跨的竖向外荷载,可以使荷载平衡得到优化实现.以一典型工程为例,分析了在相同条件下两种预应力筋布置过程中张拉引起的综合弯矩、主弯矩和次弯矩的分布和量值上差异,比较了两类布筋形式对正截面承载力计算结果的影响,验证了对大内跨小边跨框架结构统一考虑后整体上按三段连续正反抛物线预应力筋布置的合理性.     

承德地方企业与高校合作公共政策

我国70%左右的科技人员集中在高校、科研院所。高等学校具有全国最强的创新资源和优势。承德市经济基础薄弱、技术水平较低,但是具有丰富的自然资源,特别是山林、水和旅游资源丰富,随着高速公路网和高铁的建设,已经成为京津冀和东北、内蒙的交通枢纽。如果能够科学合理地选择合作方式,建立基于市场的长期有效的激励机制就能够把这两种优势有效地结合起来,产生巨大的发展动力,有力地促进创新型城市的发展。