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.

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.

3D Object Recognition by Classification Using Neural Networks

In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads to recognition of the former. 3D objects of this database are transformations of other objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group.

3-81 A Plugin for 3D-confocal Object Recognition

The research of ionizing radiation induced foci is an important method of DNA damage repair. Although the visualization technology of foci has been mature, the traditional foci recognition analysis technology has a lot of defects due to the spatial overlap of foci.

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.

Real-Time Recognition and Location of Indoor Objects

Object recognition and location has always been one of the research hotspots in machine vision.It is of great value and significance to the development and application of current service robots,industrial automation,unmanned driving and other fields.In order to realize the real-time recognition and location of indoor scene objects,this article proposes an improved YOLOv3 neural network model,which combines densely connected networks and residual networks to construct a new YOLOv3 backbone network,which is applied to the detection and recognition of objects in indoor scenes.In this article,RealSense D415 RGB-D camera is used to obtain the RGB map and depth map,the actual distance value is calculated after each pixel in the scene image is mapped to the real scene.Experiment results proved that the detection and recognition accuracy and real-time performance by the new network are obviously improved compared with the previous YOLOV3 neural network model in the same scene.More objects can be detected after the improvement of network which cannot be detected with the YOLOv3 network before the improvement.The running time of objects detection and recognition is reduced to less than half of the original.This improved network has a certain reference value for practical engineering application.

TWO NEW RECOGNITION METHODS FOR SPATIAL PLANAR POLYGONS

Two new recognition methods for the spatial planar POlygon using perspective invariants are presented. The corss-ratio (R c) of a vetex and the co-base area rotio (RA) of a edge in a spatial planar polygon are proposed and used as the invariant primitive of the recognition eigenvector. The second distance error decision rule (SD EDR) estimating the relative error of RA is introduced also too. The mthods could recognize a spatial planar polygon with an arbitrary orientation through only a single perspective view. Experimental examples are gievn.

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.

Research of the ATR system based on the 3-D models and L-M BP neural network

Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recog- nize and classify the typical targets in the battle field. The invariant features of Hu invariant moments and roundness were selected to be the inputs of the neural network because they have the invari- ances of rotation, translation and scaling. The pictures of the targets are generated by the 3-D mod- els to improve the recognition rate because it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can be implement ed in the ATR system and it has a high recognition rate and can be applied in real time.

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.

微透镜阵列实现3维物体旋转不变实时识别

为了实现3维物体旋转不变实时识别,应用微透镜阵列的多视角成像特点,利用透射像阵列的高关联性,实现3维物体信息与2维透射像阵列信息之间的转换,从而可以利用光学2维图像识别技术实现3维物体的识别。对转换和识别过程进行了理论分析,用匹配滤波的方法进行了实验验证,实现了3维物体旋转不变实时识别。得到了良好的识别效果,并实现了旋转方向的准确定位和旋转角度大小的比较判别。结果表明,应用微透镜阵列可以实现旋转3维物体旋转不变实时识别。

Recurrent 3D attentional networks for end-to-end active object recognition

Active vision is inherently attention-driven:an agent actively selects views to attend in order to rapidly perform a vision task while improving its internal representation of the scene being observed.Inspired by the recent success of attention-based models in 2D vision tasks based on single RGB images, we address multi-view depth-based active object recognition using an attention mechanism, by use of an end-to-end recurrent 3D attentional network. The architecture takes advantage of a recurrent neural network to store and update an internal representation. Our model,trained with 3D shape datasets, is able to iteratively attend the best views targeting an object of interest for recognizing it. To realize 3D view selection, we derive a 3D spatial transformer network. It is dierentiable,allowing training with backpropagation, and so achieving much faster convergence than the reinforcement learning employed by most existing attention-based models. Experiments show that our method, with only depth input, achieves state-of-the-art next-best-view performance both in terms of time taken and recognition accuracy.

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.

基于微透镜阵列的实时三维物体识别

提出一种基于微透镜阵列多视角成像特点,将三维物体的深度信息转化为二维透射像阵列的角度信息,利用光学二维图像识别技术,实现对三维物体识别的方法.对识别过程进行了理论分析和计算,用匹配滤波的方法实现了对三维物体骰子的实时识别.实验结果表明,本方法的相关识别能力较高,并且具有很强的灵活性,对于有微小旋转、微小平移的三维物体也可进行识别.

基于组合不变矩和神经网络的三维物体识别

在三维物体识别系统中,提出将三维物体的Hu不变矩和仿射不变矩两者的低阶矩组合作为三维物体的特征,结合改进的BP神经网络应用于三维物体的分类识别。理论分析和仿真实验表明组合这两种矩特征进行物体识别,性能优于单独使用Hu不变矩,如果进一步对这两种组合的矩特征进行主成分分析处理,可显著提高系统识别性能,并减少网络的训练时间。

基于模型的三维物体识别

实现了一个完整的基于模型的三维物体识别系统，它可识别灰度图象中包含的物体，如对遮挡加以限制，还可识别被遮挡的物体．该系统能实现物体的自动建模，也可先定性识别某一物体的立体图对以获取高层知识，然后在高层知识的指导下准确地匹配立体图对中相对应的特征．此外，还提出了利用最能表示物体特征的表面（特征面）来识别物体的方法，以提高系统抗噪声的能力．大量实验证明，该系统具有相当的稳健性．

激光三维成像技术及其主要应用

阐述了目前三维成像在其常见应用领域中的研究,主要致力于研究高分辨率三维成像系统。三维激光成像是一项可以应用于探测隐藏目标、地形测绘、构建虚拟环境、城市建模、目标识别等领域中的技术。在区域成像技术中,除了如立体视觉和结构化灯光等更常规的技术,实时三维传感也具有现实可操作性。当前三维激光成像技术已经发展到有能力提供厘米级波长的高分辨率三维成像,这将给许多领域提供方便,包括法律的实施和法医调查。与CCD和红外技术等传统的被动成像系统相比,激光成像技术不仅能提供强度和范围信息,还能穿透植被和窗户等特定情景元素。这意味着激光三维成像系统在目标识别与辨认等方面具备新的潜力。结果表明,激光三维成像系统可以在许多情况下得到应用。

基于结构照明和BP神经网络的三维物体识别

提出一种具有旋转不变性的三维物体识别的新方法,该方法通过结构光照明的方法,使物体的高度分布以变形条纹的形式编码于二维强度图中,由于条纹图包含有物体的高度分布信息,因此对条纹的相关识别具有本征三维识别的特点。旋转不变性是通过BP神经网络实现的。计算机模拟结果表明,用二维强度像的基频分量做训练样本设计BP神经网络,选择训练样本和隐藏层神经元的数目,基于结构光编码的BP神经网络对三维物体具有良好的旋转不变识别效果。

广义的以物体为中心的行程编码——一种新的三维物体模型

三维物体的表达和识别是图象理解和场景分析的核心问题,三维模型在三维物体的识别和场景分析中具有十分重要的作用.三维模型应该是以物体为中心的,能够提供该场景的所有有用信息.物体的大小,形状及朝向应均可从该模型中提取得到.本文提出了一种新的三维物体模型——广义的以物体为中心的行程编码.它包括物体的GORC物理数据结构,详细的形状描述和抽象描述.物体的高层次的表达可以通过以GORC编码的物理数据直接提取得到.三维的GORC是二维的以物体为中心的行程编码在三维上的推广,它兼有物体的体积表达和表面表达的优点.三维物体的GORC模型可以很容易地由其深度信息构造得出,基于GORC的投影运算,图象代数运算以及特征提取均可非常有效地实现.