Linear object classes and image synthesis from a single exampleimage

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, simpler techniques are applicable under restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other “prototypical” objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by the authors (1992). For linear object classes, it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively “rotate” high-resolution face images from a single 2D view     

The Alignment of Objects with Smooth Surfaces

This paper examines the recognition of rigid objects bounded by smooth surfaces, using an alignment approach. The projected image of such an object changes during rotation in a manner that is generally difficult to predict. An approach to this problem is suggested, using the 3D surface curvature at the points along the silhouette. The curvature information requires a single number for each point along the object′s silhouette, the radial curvature at the point. We have implemented this method and tested it on images of complex 3D objects. Models of the viewed objects were acquired using three images of each object. The implemented scheme was found to give accurate predictions of the objects′ appearances for large transformations. Using this method, a small number of (viewer-centered) models can be used to predict the new appearance of an object from any given viewpoint.     

利用信息融合方法的三维特征提取

本文提出了一种基于信息融合的物体三维特征的提取方法，该方法利用两幅互相配准的三维测距图像和灰度图像，来提取多面体的三维特征。首先，通过分析灰度图像中的灰度变化及测距图像中的测距值变化，分别求取各自图像中物体的特征点及特征边；然后，利用两配准图像之间的对应关系，求得所有特征点、面与多边形在三维测距图像中的三维表示；接着，通过分析三维测距图像中所测得的各候选平面上特定点与边处的曲率及法向，验证候选平面     

Building Three-Dimensional Object Models from Image Sequences

We present the results from a working system designed to reconstruct a complete CAD/CAM surface model for objects that have both smooth and sharp surface boundaries. The input is a sequence of images of the object; the output is a 3-D surface mesh that can be manipulated and rendered with standard CAD/CAM tools. It is now well known that complete surface information (second-order differential surface properties) can be recovered at edges generated by the extremal boundary of a 3-D surface, In this paper we present new results in applying this theoretical framework to many views of a real object to build a complete 3-D model. Our experiments place these multiple frames in a common coordinate system using known motion, if available, or by otherwise automatically computing object motion based on our classification of edges in the reconstruction process. Experimental results are shown for bath real and synthetic data.     

Graph-based representation for similarity retrieval of symbolic images

Image retrieval from an image database by the image objects and their spatial relationships has emerged as an important research subject in these decades. To retrieve images similar to a given query image, retrieval methods must assess the similarity degree between a database image and the query image by the extracted features with acceptable efficiency and effectiveness. This paper proposes a graph-based model SRG (spatial relation graph) to represent the semantic information of the contained objects and their spatial relationships in an image with no file annotation. In an SRG graph, the image objects are symbolized by the predefined class names as vertices and the spatial relations between object pairs are represented as arcs. The proposed model assesses the similarity degree between two images by calculating the maximum common subgraph of two corresponding SRG’s through intersection, which has quadratic time complexity owing to the characteristics of SRG. Its efficiency remains quadratic regardless of the duplication rate of the object symbols. The extended model SRG_T is also proposed, with the same time complexity, for the applications that need to consider the topological relations among objects. A synthetic symbolic image database and an existing image dataset are used in the conducted experiments to verify the performance of the proposed models. The experimental results show that the proposed models have compatible retrieval quality with remarkable efficiency improvements compared with three well-known methods LCS_Clique, SIM_R, and 2D Be-string, where LCS_Clique utilizes the number of objects in the maximum common subimage as its similarity function, SIM_R uses accumulation-based similarity function of similar object pairs, and 2D Be-string calculates the similarity of 2D patterns by the linear combination of two 1D similarities.     

Object recognition by combining paraperspective images

This paper provides a study on object recognition under paraperspective projection. Discussed is the problem of determining whether or not a given image was obtained from a 3-D object to be recognized. First it is clarified that paraperspective projection is the first-order approximation of perspective projection. Then it is shown that, if we represent an object as a set of its feature points and the object undergoes a rigid transformation or an affine transformation, any paraperspective image can be expressed as a linear combination of several appropriate paraperspective images: we need at least three images for rigid transformations; whereas we need at least two images for affine transformations. Particularly in the case of a rigid transformation, the coefficients of the combination have to satisfy two conditions: orthogonality and norm equality. A simple algorithm to solve the above problem based on these properties is presented: a linear, single-shot algorithm. Some experimental results with synthetic images and real images are also given.This work was done while the author was with ATR Auditory and Visual Perception Research Laboratories.Advanced Research Laboratory Hitachi, Ltd.     

Object matching using deformable templates

We propose a general object localization and retrieval scheme based on object shape using deformable templates. Prior knowledge of an object shape is described by a prototype template which consists of the representative contour/edges, and a set of probabilistic deformation transformations on the template. A Bayesian scheme, which is based on this prior knowledge and the edge information in the input image, is employed to find a match between the deformed template and objects in the image. Computational efficiency is achieved via a coarse-to-fine implementation of the matching algorithm. Our method has been applied to retrieve objects with a variety of shapes from images with complex background. The proposed scheme is invariant to location, rotation, and moderate scale changes of the template     

3D object recognition based on curvature information of planar views

In this paper, a 3D object recognition algorithm is proposed. Objects are recognized by studying planar images corresponding to a sequence of views. Planar shape contours are represented by their adaptively calculated curvature functions, which are decomposed in the Fourier domain as a linear combination of a set of representative shapes. Finally, sequences of views are identified by means of Hidden Markov Models. The proposed system has been tested for artificial and real objects. Distorted and noisy versions of the objects were correctly clustered together.     

基于水动力学、分数阶微分及Steger算法的线状目标提取

目的 线状目标的检测具有非常广泛的应用领域,如车道线、道路及裂缝的检测等,而裂缝是其中最难检测的线状目标。为避免直接提取线状目标时图像分割难的问题,以裂缝和车道线为例,提出了一种新的跟踪线状目标中线的算法。方法 对图像进行高斯平滑,用一种新的分数阶微分模板增强图像中的模糊及微细线状目标;基于Steger算法提出一种提取线状目标中心线特征点的算法,避免了提取整体目标的困难;根据水动力学思想将裂隙看成溪流,通过最大熵阈值处理后,先进行特征点的连接,再基于线段之间的距离及夹角进行线段之间的连接（溪流之间的融合）。结果 对300幅裂缝图像及4种类别的其他线状目标图像进行试验,并与距离变换、最大熵阈值法+细线化Otsu阈值分割+细线化、谷底边界检测等类似算法进行比较分析,本文算法检测出的线状目标的连续性好、漏检（大间隙少）和误检（毛刺及多余线段少）率均较低。结论 本文算法能够在复杂的线状目标图像中准确快速地提取目标的中心线,一定程度上改善了复杂线状目标图像分割难的问题。     

图像中物体空间关系的表示及在检索中的应用

基于图像中物体之间的空间关系的图像检索往往受困于待处理的图像中物体种类和空间位置难以自动准确地获取。文中基于物体识别算法的输出,提出一种对物体空间关系的三元组表示法,给出基于这种表示方法对图像索引、相似度计算和检索排序的方法及允许用户使用查询词和空间关系表达查询需求的二维输入界面,并实现原型系统。这种表示法具有良好的鲁棒性,可容忍物体识别算法一定程度的误差,将物体识别得到的置信度加入三元组表示法置信度计算和排序算法中,减少物体识别结果误差对检索性能的影响。在原型系统上的实验表明,该系统在实验中对包含物体位置关系的检索给出更准确的结果,在NDCG@m、MAP、F@m上均优于现有系统。     

Contour Tracking in Clutter: A Subset Approach

A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data in the form of a subset of contour space. Greater complexity is added to the contour model by analyzing rigid and non-rigid transformations of contours separately. In the course of tracking, multiple contours may be observed due to the presence of extraneous edges in the form of clutter; the learned model guides the algorithm in picking out the correct one. The algorithm, which is posed as a solution to a minimization problem, is made efficient by the use of several iterative schemes. Results applying the proposed algorithm to the tracking of a flexing finger and to a conversing individual's lips are presented.     

Obtaining base edge correspondence in stereo images via quantitative measures along C-diagonals

We propose a novel approach to solving the object edge correspondence problem for stereo images. For an object placed on a calibration plate (C-plate), the proposed approach first obtains the correspondences of the object edges lying on the C-plate (the base edges) via quantitative measures of locations of the intersections of the extended line of the edges and the diagonals of the C-plate (C-diagonals) using cross ratios. The measures are viewpoint invariant for an object base edge, and are expressed in the number of image pixels. Special cases which need only coarse calculations, as well as those which require extra measures for additional point features, are also considered. The proposed approach requires 2-D image data only, and is robust in the presence of errors in the feature detection. Experimental results are presented for polyhedral and curved objects to demonstrate the effectiveness of the proposed approach.     

基于独立分量分析的人脸超分辨率重建

提出一种基于独立分量分析(ICA)的人脸超分辨率重建算法。该算法利用ICA从高分辨率训练图像中提取出独立分量,并对ICA系数进行先验估计。对于给定的低分辨率图像,结合最大后验概率估计求出ICA系数,进行ICA反变换得到高分辨率图像的近似估计,并利用局部结构张量对图像进行精化处理得到重建图像。仿真结果表明,该算法在实现人脸超分辨率重建的同时保持了人脸整体结构特征,且对光照、表情、姿态等具有一定的鲁棒性,将重建结果用于人脸辨识,有效提高了辨识效率。     

3D modeling of multiple-object scenes from sets of images

This paper proposes a new approach for multi-object 3D scene modeling. Scenes with multiple objects are characterized by object occlusions under several views, complex illumination conditions due to multiple reflections and shadows, as well as a variety of object shapes and surface properties. These factors raise huge challenges when attempting to model real 3D multi-object scene by using existing approaches which are designed mainly for single object modeling. The proposed method relies on the initialization provided by a rough 3D model of the scene estimated from the given set of multi-view images. The contributions described in this paper consists of two new methods for identifying and correcting errors in the reconstructed 3D scene. The first approach corrects the location of 3D patches from the scene after detecting the disparity between pairs of their projections into images. The second approach is called shape-from-contours and identifies discrepancies between projections of 3D objects and their corresponding contours, segmented from images. Both unsupervised and supervised segmentations are used to define the contours of objects.     

Illuminating the fourth dimension

A family of techniques for creating intuitively informative shaded images of 4-D mathematical objects is proposed. The rendering of an object in a 4-D world is described by considering step-by-step how objects might be rendered into images in simpler worlds. The mathematical principles needed to compute projected images of objects and their shadows in D dimensions are outlined. The issues involved in producing shaded images of objects in four dimensions, including extending rendering from 3-D to 4-D, smooth shading, and specularity, are discussed. Results of rendering a Steiner surface, torus, and knotted sphere in four dimensions are presented     

纹理边缘引导的深度图像超分辨率重建

目的 深度图像作为一种普遍的3维场景信息表达方式在立体视觉领域有着广泛的应用。Kinect深度相机能够实时获取场景的深度图像,但由于内部硬件的限制和外界因素的干扰,获取的深度图像存在分辨率低、边缘不准确的问题,无法满足实际应用的需要。为此提出了一种基于彩色图像边缘引导的Kinect深度图像超分辨率重建算法。方法 首先对深度图像进行初始化上采样,并提取初始化深度图像的边缘;进一步利用高分辨率彩色图像和深度图像的相似性,采用基于结构化学习的边缘检测方法提取深度图的正确边缘;最后找出初始化深度图的错误边缘和深度图正确边缘之间的不可靠区域,采用边缘对齐的策略对不可靠区域进行插值填充。结果 在NYU2数据集上进行实验,与8种最新的深度图像超分辨率重建算法作比较,用重建之后的深度图像和3维重建的点云效果进行验证。实验结果表明本文算法在提高深度图像的分辨率的同时,能有效修正上采样后深度图像的边缘,使深度边缘与纹理边缘对齐,也能抑制上采样算法带来的边缘模糊现象;3维点云效果显示,本文算法能准确区分场景中的前景和背景,应用于3维重建等应用能取得较其他算法更好的效果。结论 本文算法普遍适用于Kinect深度图像的超分辨率重建问题,该算法结合同场景彩色图像与深度图像的相似性,利用纹理边缘引导深度图像的超分辨率重建,可以得到较好的重建结果。     

实时立体视觉系统中的深度映射

目的 为减少立体图像中由于水平视差过大引起的视觉疲劳。针对实时渲染的立体视觉系统,给出了一种非均匀深度压缩方法。方法 该方法在单一相机空间内,通过不同的投影变换矩阵生成双眼图像,水平视差由投影变换来控制。为减少深度压缩造成的模型变形而带来的瑕疵,将不同深度区域内物体施以不同的压缩比例;将相机轴距表示为深度的连续函数,通过相机轴距推导出在单一相机空间内获取双眼图像的坐标变换,将深度压缩转换为模型的坐标变换,从而保证压缩比例的连续变化。结果 实验结果表明,该方法能有效提高立体图像的质量。结论 该方法简单、高效,可应用于游戏、虚拟现实等实时立体视觉系统。     

基于图像线特征和点云面特征的水下人造目标定位

目的 针对水下人造目标的位姿参数估计问题,提出一种基于图像线特征与点云面特征的目标定位算法。方法 基于对人造物体成像后的边缘特征及其本身曲面特征的认知,将目标描述成为一种线特征与面特征的组合。首先依据指定线型对目标图像边缘进行线特征检测,初步定位目标在图像中的位置;然后采用RANSAC(random sample consensus)算法对投影到目标区域内的点云进行曲面特征检测,得到目标参数的近似值并从视场点云中提取目标点云;最后以超二次曲面作为目标的部件化模型,以检测到的目标参数为初值,建立3维目标尺寸和位姿估计的非线性目标函数,将该目标函数的优化结果作为3维目标的定位结果。结果 通过水下实验对算法的有效性进行验证,定位后的目标旋转轴角度偏差不超过2°,相对位置偏差不超过1%,单目标定位耗时不超过5 s。结论 实验结果表明,该算法的定位精度和耗时均能满足应用需要,可有效定位未知尺寸的人造目标,且对水下复杂环境有较强的适应性。     

基于三幅图像的曲面造型及纹理提取

利用标准的参数曲面来表示真实的三维曲面物体，并将物体正面、侧面及背面拍摄图像的侧影轮廓线作为约束，通过最小化线性约束下的二次目标函数实现曲面造型。将曲面重新投影到物体的正面、背面及侧面图像上，利用基于重心坐标的加权纹理提取方法提取纹理图像。利用真实图像实现了逼真的曲面造型，验证了该方法的可行性和正确性。