Part-level 3-D object classification with improved interpretation tree

For classifying unknown 3-D objects into a set of predetermined object classes, a part-level object classification method based on the improved interpretation tree is presented. The part-level representation is implemented, which enables a more compact shape description of 3-D objects. The proposed classification method consists of two key processing stages： the improved constrained search on an interpretation tree and the following shape similarity measure computation. By the classification method, both whole match and partial match with shape similarity ranks are achieved; especially, focus match can be accomplished, where different key parts may be labeled and all the matched models containing corresponding key parts may be obtained. A series of experiments show the effectiveness of the presented 3-D object classification method.

STABILITY OF 3-D DISTURBANCE WAVES ON BOUNDARY LAYER WITH DISTRIBUTED LOCAL ROUGHNESS

The numerical solution of the stable basic flow on a 3-D boundary layer is obtained by using local ejection, local suction, and combination of local ejection and suction to simulate the local rough wall. The evolution of 3-D disturbance T-S wave is studied in the spatial processes, and the effects of form and distribution structure of local roughness on the growth rate of the 3-D disturbance wave and the flow stability are discussed. Numerical results show that the growth of the disturbance wave and the form of vortices are accelerated by the 3-D local roughness. The modification of basic flow owing to the evolvement of the finite amplitude disturbance wave and the existence of spanwise velocity induced by the 3-D local roughness affects the stability of boundary layer. Propagation direction and phase of the disturbance wave shift obviously for the 3-D local roughness of the wall. The flow stability characteristics change if the form of the 2-D local roughness varies.

THREE-IMAGE MATCHING FOR 3-D LINEAR OBJECT TRACKING

This paper will discuss strategies for trinocular image rectification and matching for linear object tracking.It is well known that a pair of stereo images generates two epipolar images.Three overlapped images can yield six epipolar images in situations where any two are required to be rectified for the purpose of image matching.In this case,the search for feature correspondences is computationally intensive and matching complexity increases.A special epipolar image rectification for three stereo images,which simplifies the image matching process,is therefore proposed.This method generates only three rectified images,with the result that the search for matching features becomes more straightforward.With the three rectified images,a particular line_segment_based correspondence strategy is suggested.The primary characteristics of the feature correspondence strategy include application of specific epipolar geometric constraints and reference to three_ray triangulation residuals in object space.

Exploring Local Regularities for 3D Object Recognition

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles（L-MSDA）, localized minimizing standard deviation of segment magnitudes（L-MSDSM）, localized minimum standard deviation of areas of child faces （L-MSDAF）, localized minimum sum of segment magnitudes of common edges （L-MSSM）, and localized minimum sum of areas of child face （L-MSAF）. Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities： L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

3-D Reconstruction and Visualization of Laser-Scanned Trees by Weighted Locally Optimal Projection and Accurate Modeling Method

This paper presents a method to reconstruct 3-D models of trees from terrestrial laser scan(TLS)point clouds.This method uses the weighted locally optimal projection(WLOP)and the AdTree method to reconstruct detailed 3-D tree models.To improve its representation accuracy,the WLOP algorithm is introduced to consolidate the point cloud.Its reconstruction accuracy is tested using a dataset of ten trees,and the one-sided Hausdorff distances between the input point clouds and the resulting 3-D models are measured.The experimental results show that the optimal projection modeling method has an average one-sided Hausdorff distance(mean)lower by 30.74%and 6.43%compared with AdTree and AdQSM methods,respectively.Furthermore,it has an average one-sided Hausdorff distance(RMS)lower by 29.95%and 12.28%compared with AdTree and AdQSM methods.Results show that the 3-D model generated fits closely to the input point cloud data and ensures a high geometrical accuracy.

THREE-DIMENSIONAL CONFORMAL RADIATION THERAPY FOR LOCALLY RECURRENT NASOPHARYNGEAL CARCINOMA

Objective: To investigate the safety and effectiveness of three-dimensional conformal radiation therapy (3-D CRT) for locally recurrent nasopharyngeal carcinoma (NPC). Methods: From April 1998 to March 2000, 34 patients who had undergone previous external beam radiation therapy were retreated with 3-D CRT for locally recurrent NPC (33 poorly differentiated squamous cell carcinomas, 1 adenoma). The patients were re-staged according to Huaqing staging system with the following distribution: T1N0M0 in 5 cases, T2N0M0 in 11 cases, T3N0M0 in 12 cases, T4N0M0 in 6 cases. The maximal dimension of the gross tumor volume (GTV) ranged from 1.0 cm to 5.0 cm (median: 2.9 cm). CT simulation and 3-D planning were used to ensure full and conformal coverage of the planning target volume (PTV) by treated volume, while minimizing the absorbed dose of the adjacent normal tissue. 5–7 static conformal coplanar or noncoplanar portals were delivered for each fraction irradiation. The total dose delivered ranged from 65–70 Gy, with 2.5 Gy per fractionation, one fractionation per day, 5 days a week. Median follow-up time from 3-D CRT was 25 months (range: 12–36 months). Results: Over the follow-up period, local recurrence was observed in 3 patients, regional failure in 3, distant metastasis in 3, and six patients died; 88.2% (30/34) of the patient maintained local control, 82.4% (28/34) survived, and 76.5% (26/34) survived with no evidence of tumor. Acute complications were minor and few. The overall incidence of late complication was 20.6% (7/34), and severe complication was 14.7% (5/34), after re-irradiation with 3-D CRT. Conclusion: 3-D CRT is safety and effectiveness for most of the patients with locally recurrent NPC. Our preliminary results indicate a high local control rate and a low complication rate. The long-term curative effect and sequelae await further study.

Securing Copyright Using 3D Objects Blind Watermarking Scheme

Recently,securing Copyright has become a hot research topic due to rapidly advancing information technology.As a host cover,watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary times.Digital media covers may often take any form,including audio,video,photos,even DNA data sequences.In this work,we present a new methodology for watermarking to hide secret data into 3-D objects.The technique of blind extraction based on reversing the steps of the data embedding process is used.The implemented technique uses the features of the 3-D object vertex’discrete cosine transform to embed a grayscale image with high capacity.The coefficient of vertex and the encrypted picture pixels are used in the watermarking procedure.Additionally,the extraction approach is fully blind and is dependent on the backward steps of the encoding procedure to get the hidden data.Correlation distance,Euclidean distance,Manhattan distance,and the Cosine distance are used to evaluate and test the performance of the proposed approach.The visibility and imperceptibility of the proposed method are assessed to show the efficiency of our work compared to previous corresponding methods.

Exponential Convergence Theory of the Multipole and Local Expansions for the 3-D Laplace Equation in Layered Media

In this paper,we establish the exponential convergence theory for the multipole and local expansions,shifting and translation operators for the Green's function of 3-dimensional Laplace equation in layered media.An immediate application of the theory is to ensure the exponential convergence of the FMM which has been shown by the numerical results reported in[27].As the Green's function in layered media consists of free space and reaction field components and the theory for the free space components is well known,this paper will focus on the analysis for the reaction components.We first prove that the density functions in the integral representations of the reaction components are analytic and bounded in the right half complex wave number plane.Then,by using the Cagniard-de Hoop transform and contour deformations,estimates for the remainder terms of the truncated expansions are given,and,as a result,the exponential convergence for the expansions and translation operators is proven.

Atmospheric Pressure-Aware Seamless 3-D Localization and Navigation for Mobile Internet Devices

This paper describes a seamless three-dimensional （3-D） localization and navigation system for smartphones. The smartphone includes an atmospheric pressure sensor to measure the user＇s altitude that is combined with the outdoor Global Positioning System （GPS） and indoor WiFi-APs localization systems in a seamless 3-D localization system. The smartphone software also provides seamless navigation services by updating map information for both indoor and outdoor locations through the mobile Internet. The indoor floor information calculated from the altitude information is used to project localization anchor nodes, e.g., WiFi-AP, on different floors onto the user＇s floor with an indoor 3-D localization algorithm using projection distances based on a Received Signal Strength （RSS） algorithm. Tests show that the 3-D method reduces systematic errors and achieves much higher accuracy than the traditional two-dimensional localization method.

3D Depth Measurement for Holoscopic 3D Imaging System

Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms.

Efficient View-Based 3-D Object Retrieval via Hypergraph Learning

View-based 3-D object retrieval has become an emerging topic in recent years,especially with the fast development of visual content acquisition devices,such as mobile phones with cameras.Extensive research efforts have been dedicated to this task,while it is still difficult to measure the relevance between two objects with multiple views.In recent years,learning-based methods have been investigated in view-based 3-D object retrieval,such as graph-based learning.It is noted that the graph-based methods suffer from the high computational cost from the graph construction and the corresponding learning process.In this paper,we introduce a general framework to accelerate the learning-based view-based 3-D object matching in large scale data.Given a query object Q and one object O from a 3-D dataset D,the first step is to extract a small set of candidate relevant 3-D objects for object O.Then multiple hypergraphs can be constructed based on this small set of 3-D objects and the learning on the fused hypergraph is conducted to generate the relevance between Q and O,which can be further used in the retrieval procedure.Experiments demonstrate the effectiveness of the proposed framework.

Computation of Edge-Edge-Edge Events Based on Conicoid Theory for 3-D Object Recognition

The availability of a good viewpoint space partition is crucial in three dimensional （3-D） object recognition on the approach of aspect graph. There are two important events, depicted by the aspect graph approach, edge-：edge-edge （EEE） events and edge-vertex （EV） events. This paper presents an algorithm to compute EEE events by characteristic analysis based on conicoid theory, in contrast to current algorithms that focus too much on EV events and often overlook the importance of EEE events. Also, the paper provides a standard flowchart for the viewpoint space partitioning based on aspect graph theory that makes it suitable for perspective models. The partitioning result best demonstrates the algorithm＇s efficiency with more valuable viewpoints found with the help of EEE events, which can definitely help to achieve high recognition rate for 3-D object recognition.

Recognition of 3-D objects based on Markov random field models

The recognition of 3-D objects is quite a difficult task for computer vision systems.This paper presents a new object framework,which utilizes densely sampled grids with different resolutions to represent the local information of the input image.A Markov random field model is then created to model the geometric distribution of the object key nodes.Flexible matching,which aims to find the accurate correspondence map between the key points of two images,is performed by combining the local similarities and the geometric relations together using the highest confidence first method.Afterwards,a global similarity is calculated for object recognition.Experimental results on Coil-100 object database,which consists of 7200 images of 100 objects,are presented.When the numbers of templates vary from 4,8,18 to 36 for each object,and the remaining images compose the test sets,the object recognition rates are 95.75%,99.30%,100.0%and 100.0%,respectively.The excellent recognition performance is much better than those of the other cited references,which indicates that our approach is well-suited for appearance-based object recognition.

双摄象头三维视觉系统

本文介绍了一种较实用的双摄象头三维视觉系统.主要讨论了视场内具有六个自由度物体的定位问题.系统以Frei和Chen方法抽取边缘,并运用了非均匀量化和预加重技术.采用了改进的Moravec兴趣算子法,实现了特征点的自动抽取.基于边缘的序贯分层配准法大大缩短了配准时间.在用三维数据获取物体姿态时.根据刚体运动总结的规则解决了工作特征点和模型点匹配时的组合爆炸问题和多义性问题.

A new navigation approach of terrain contour matching based on 3-D terrain reconstruction from onboard image sequence

This article presents a passive navigation method of terrain contour matching by reconstructing the 3-D terrain from the image sequence(acquired by the onboard camera).To achieve automation and simultaneity of the image sequence processing for navigation,a correspondence registration method based on control points tracking is proposed which tracks the sparse control points through the whole image sequence and uses them as correspondence in the relation geometry solution.Besides,a key frame selection method based on the images overlapping ratio and intersecting angles is explored,thereafter the requirement for the camera system configuration is provided.The proposed method also includes an optimal local homography estimating algorithm according to the control points,which helps correctly predict points to be matched and their speed corresponding.Consequently,the real-time 3-D terrain of the trajectory thus reconstructed is matched with the referenced terrain map,and the result of which provides navigating information.The digital simulation experiment and the real image based experiment have verified the proposed method.

A High Speed Detection Scheme for Point Targets in a Multitarget Environment

The purpose of this paper is to develop a high speed detection scheme for moving and / or stationary point targets in a multitarget environment as registered in an IR image sequence. An iterative approximate 3-D line searching algorithm based upon the geometric representation of lines (for non-maneuvering targets in space) in a 3-D space is derived. The convergency of the algorithm is proved. An analysis is performed of the theoretical detection performance of the algorithm. The statistical experiment results show high effectiveness and computational efficiency of the algorithm in the case of low SNR. The idea may be employed to satisfy the real-time processing requirement of an IR system.

Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features

A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead （OGWH）. Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%.