Monocular Visual SLAM for Markerless Tracking Algorithm to Augmented Reality

Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean,river and lake exploration,but it is affected by properties of water and the optics.In order to solve the lower-resolution underwater image formed by the influence of water and light,the image super-resolution reconstruction technique is applied to the underwater image processing.This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology.We research the degradation model of underwater images,and analyze the lower-resolution factors of underwater images in different situations,and compare different traditional super-resolution image reconstruction algorithms.We further show that the algorithm of super-resolution using deep convolution networks(SRCNN)which applied to super-resolution underwater images achieves good results.

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice(Oryza sativa L.)

Grain size traits are critical agronomic traits which directly determine grain yield,but the genetic bases of these traits are still not well understood.In this study,a total of 154 chromosome segment substitution lines(CSSLs)population derived from a cross between a japonica variety Koshihikari and an indica variety Nona Bokra was used to investigate grain length(GL),grain width(GW),length-width ratio(LWR),grain perimeter(GP),grain area(GA),and thousand grain weight(TGW)under four environments.QTL mapping analysis of six grain size traits was performed by QTL IciMapping 4.2 with an inclusive composite interval mapping(ICIM)model.A total of 64 QTLs were identified for these traits,which mapped to chromosomes 1,2,3,4,6,7,8,10,11,and 12 and accounted for 1.6%–27.1%of the total phenotypic variations.Among these QTLs,thirty-six loci were novel and seven QTLs were identified under four environments.One locus containing the known grain size gene,qGL3/GL3.1/OsPPKL1,also have been found.Moreover,five pairs of digenic epistatic interactions were identified except for GL and GP.These findings will facilitate fine mapping of the candidate gene and QTL pyramiding to genetically improve grain yield in rice.