Image mosaicking for polyhedral scene and in particular singly visible surfaces

Image mosaic construction is about stitching together a number of images about the same scene to construct a single image with a larger field of view. The majority of the previous work was rooted at the use of a single image-to-image mapping termed planar homography for representing the imaged scene. However, the mapping is applicable only to cases where the imaged scene is either a single planar surface, or very distant from the cameras, or imaged under a pure rotation of the camera, and that greatly limits the range of applications of the mosaicking methods. This paper presents a novel mosaicking solution for scenes that are polyhedral (thus consisting of multiple surfaces) and that are pictured possibly in closed range of the camera. The solution has two major advantages. First, it requires only a few correspondences over the entire scene, not correspondences over every surface patch in it to work. Second, it conquers a seemingly impossible task—warping image data of surfaces that are visible in only one of the input images, which we refer to as the singly visible surfaces, to another viewpoint to constitute the mosaic there. We also provide a detail analysis of what determines whether a singly visible surface could be mosaicked or not. Experimental results on real image data are presented to illustrate the performance of the method.     

Maximum-likelihood registration of range images with missing data

Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This paper presents a maximum likelihood method for registration of scenes with unmatched or missing data. Using ray casting, correspondences are formed between valid and missing points in each view. These correspondences are used to classify points by their visibility properties, including occlusions, field of view, and shadow regions. The likelihood of each point match is then determined using statistical properties of the sensor, such as noise and outlier distributions. Experiments demonstrate a high rates of convergence on complex scenes with varying degrees of overlap.     

期刊目次信息自动推送系统的设计与实现

介绍针对利用网络获取的期刊目次信息,运用Microsoft Visual Basic编程语言,实现期刊目次信息向用户自动推送的完全自动化。不仪可以将文献工作人员从以前繁琐的手动工作中解放出来,还使用户享受到便捷的目次报道服务。介绍期刊目次信息自动推送系统的设计思想、工作流程及实现方法。     

一种新的覆盖粗糙集及其模糊性度量

在覆盖近似空间中定义了一类新的模糊集,给出了该类模糊集的模糊性度量,讨论模糊集及其模糊度量的性质,最后通过实例给出直观解释.     

Reducing control points in lofted B-spline surface interpolation using common knot vector determination

A new algorithm for reducing control points in lofted surface interpolation to rows of data points is presented in this paper. The key step of surface lofting is to obtain a set of compatible B-spline curves interpolating each row. Given a set of points and their parameterization, a necessary and sufficient condition is proposed to determine the existence of interpolating B-spline curves defined on a given knot vector. Based on this condition, we first properly construct a common knot vector that guarantees the existence of interpolating B-spline curves to each row of points. Then we calculate a set of interpolating B-spline curves defined on the common knot vector by energy minimization. Using this method, fewer control points are employed while maintaining a visually pleasing shape of the lofted surface. Several experimental results demonstrate the usability and quality of the proposed method.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

An improved Henry gas solubility optimization for optimization tasks

Applied Intelligence - The henry gas solubility optimization (HGSO) is a new nature-inspired algorithm that mimics Henry Gas Solubility to solve global optimization problems. The main changes of...     

Designing Metallic and Insulating Nanocrystal Heterostructures to Fabricate Highly Sensitive and Solution Processed Strain Gauges for Wearable Sensors

All‐solution processed, high‐performance wearable strain sensors are demonstrated using heterostructure nanocrystal (NC) solids. By incorporating insulating artificial atoms of CdSe quantum dot NCs into metallic artificial atoms of Au NC thin film matrix, metal–insulator heterostructures are designed. This hybrid structure results in a shift close to the percolation threshold, modifying the charge transport mechanism and enhancing sensitivity in accordance with the site percolation theory. The number of electrical pathways is also manipulated by creating nanocracks to further increase its sensitivity, inspired from the bond percolation theory. The combination of the two strategies achieves gauge factor up to 5045, the highest sensitivity recorded among NC‐based strain gauges. These strain sensors show high reliability, durability, frequency stability, and negligible hysteresis. The fundamental charge transport behavior of these NC solids is investigated and the combined site and bond percolation theory is developed to illuminate the origin of their enhanced sensitivity. Finally, all NC‐based and solution‐processed strain gauge sensor arrays are fabricated, which effectively measure the motion of each finger joint, the pulse of heart rate, and the movement of vocal cords of human. This work provides a pathway for designing low‐cost and high‐performance electronic skin or wearable devices.     

Prediction of NSCLC recurrence from microarray data with GEP

Lung cancer is one of the deadliest diseases in the world. Non‐small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.Inspec keywords: lung, cancer, lab‐on‐a‐chip, genetics, patient diagnosisOther keywords: NSCLC recurrence prediction, microarray data, GE programming, nonsmall cell lung cancer, cancer recurrence, histopathology data, microarray gene expression, prognostic genes