A volumetric/iconic frequency domain representation for objectswith application for pose invariant face recognition

A novel method for representing 3D objects that unifies viewer and model centered object representations is presented. A unified 3D frequency-domain representation, called volumetric frequency representation (VFR), encapsulates both the spatial structure of the object and a continuum of its views in the same data structure. The frequency-domain image of an object viewed from any direction can be directly extracted employing an extension of the projection slice theorem, where each Fourier-transformed view is a planar slice of the volumetric frequency representation. The VFR is employed for pose-invariant recognition of complex objects, such as faces. The recognition and pose estimation is based on an efficient matching algorithm in a four-dimensional Fourier space. Experimental examples of pose estimation and recognition of faces in various poses are also presented     

Implicit Surface-Based Geometric Fusion

This paper introduces a general purpose algorithm for reliable integration of sets of surface measurements into a single 3D model. The new algorithm constructs a single continuous implicit surface representation which is the zero-set of a scalar field function. An explicit object model is obtained using any implicit surface polygonization algorithm. Object models are reconstructed from both multiple view conventional 2.5D range images and hand-held sensor range data. To our knowledge this is the first geometric fusion algorithm capable of reconstructing 3D object models from noisy hand-held sensor range data.This approach has several important advantages over existing techniques. The implicit surface representation allows reconstruction of unknown objects of arbitrary topology and geometry. A continuous implicit surface representation enables reliable reconstruction of complex geometry. Correct integration of overlapping surface measurements in the presence of noise is achieved using geometric constraints based on measurement uncertainty. The use of measurement uncertainty ensures that the algorithm is robust to significant levels of measurement noise. Previous implicit surface-based approaches use discrete representations resulting in unreliable reconstruction for regions of high curvature or thin surface sections. Direct representation of the implicit surface boundary ensures correct reconstruction of arbitrary topology object surfaces. Fusion of overlapping measurements is performed using operations in 3D space only. This avoids the local 2D projection required for many previous methods which results in limitations on the object surface geometry that is reliably reconstructed. All previous geometric fusion algorithms developed for conventional range sensor data are based on the 2.5D image structure preventing their use for hand-held sensor data. Performance evaluation of the new integration algorithm against existing techniques demonstrates improved reconstruction of complex geometry.     

3D reconstruction of complex curved objects from line drawings

An active research topic in computer vision and graphics is developing algorithms that can reconstruct the 3D surface of curved objects from line drawings. There are a number of algorithms have been dedicated to solve this problem, but they can't solve this problem when the geometric structure of a curved object becomes complex. This paper proposes a novel approach to reconstructing a complex curved 3D object from single 2D line drawings. Our approach has three steps:(1) decomposing a complex line drawing into several simpler line drawings and transforming them into polyhedron;(2) reconstructing the 3D wireframe of curved object from these simpler line drawings and generating the curved faces;(3) combining the 3D objects into the complete objects. A number of examples are given to demonstrate the ability of our approach to successfully perform reconstruction of curved objects which are more complex than previous methods.     

三维人脸识别研究综述

近二十多年来,虽然基于图像的人脸识别已取得很大进展,并可在约束环境下获得很好的识别性能,但仍受光照、姿态、表情等变化的影响很大,其本质原因在于图像是三维物体在二维空间的简约投影.因此,利用脸部曲面的显式三维表达进行人脸识别正成为近几年学术界的研究热点.文中分析了三维人脸识别的产生动机、概念与基本过程;根据特征形式,将三维人脸识别算法分为基于空域直接匹配、基于局部特征匹配、基于整体特征匹配三大类进行综述;对二维和三维的双模态融合方法进行分类阐述;列出了部分代表性的三维人脸数据库;对部分方法进行实验比较,并分析了方法有效性的原因;总结了目前三维人脸识别技术的优势与困难,并探讨了未来的研究趋势.     

一种车站危险品检测中的三维图像重建方法

图像重建是车站危险品检测过程中的重要环节,其结果直接关系到后续危险品的识别。为此提出了一种基于FDK算法的三维图像重建方法。该方法首先将X射线探测器得到的物体投影值,利用正弦函数对投影数据进行加权;然后对不同投影角度的投影数据进行水平方向的一维滤波,去除噪声和伪迹;最后沿X射线方向进行三维反投影。实验结果表明,该方法能更清晰地重建出物体。     

Extrusion and boundary evaluation for multidimensional polyhedra

Extrusion is a basic operation allowing the generation of higher intrinsic dimension polyhedra. The paper gives closed formulas both to generate a (d+1)-dimensional polyhedron obtained by affine extrusion of a (d)-dimensional polyhedron, and to generate by rotational extrusion. Algorithms for the boundary evaluation when a decompositive representation is given are also discussed.

The representation used in the paper, based on simplicial complexes, is general and simple, and allows us to represent nonconvex, unconnected, unoriented, nonmanifold and unbounded linear polyhedra. A simplicial complex triangulating the extruded polyhedron is generated by independently extruding the simplices of the input object. The approach is very efficient because no a posteriori triangulation of the extruded polyhedron is required; furthermore, both the underlying complex and the adjacencies between cells are calculated by using closed formulas.     

Visual learning and recognition of 3-d objects from appearance

The problem of automatically learning object models for recognition and pose estimation is addressed. In contrast to the traditional approach, the recognition problem is formulated as one of matching appearance rather than shape. The appearance of an object in a two-dimensional image depends on its shape, reflectance properties, pose in the scene, and the illumination conditions. While shape and reflectance are intrinsic properties and constant for a rigid object, pose and illumination vary from scene to scene. A compact representation of object appearance is proposed that is parametrized by pose and illumination. For each object of interest, a large set of images is obtained by automatically varying pose and illumination. This image set is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the object is represented as a manifold. Given an unknown input image, the recognition system projects the image to eigenspace. The object is recognized based on the manifold it lies on. The exact position of the projection on the manifold determines the object's pose in the image.A variety of experiments are conducted using objects with complex appearance characteristics. The performance of the recognition and pose estimation algorithms is studied using over a thousand input images of sample objects. Sensitivity of recognition to the number of eigenspace dimensions and the number of learning samples is analyzed. For the objects used, appearance representation in eigenspaces with less than 20 dimensions produces accurate recognition results with an average pose estimation error of about 1.0 degree. A near real-time recognition system with 20 complex objects in the database has been developed. The paper is concluded with a discussion on various issues related to the proposed learning and recognition methodology.     

Isolated 3D object recognition through next view planning

In many cases, a single view of an object may not contain sufficient features to recognize it unambiguously. This paper presents a new online recognition scheme based on next view planning for the identification of an isolated 3D object using simple features. The scheme uses a probabilistic reasoning framework for recognition and planning. Our knowledge representation scheme encodes feature based information about objects as well as the uncertainty in the recognition process. This is used both in the probability calculations as well as in planning the next view. Results clearly demonstrate the effectiveness of our strategy for a reasonably complex experimental set     

Plane-based optimization for 3D object reconstruction from single line drawings

In previous optimization-based methods of 3D planar-faced object reconstruction from single 2D line drawings, the missing depths of the vertices of a line drawing (and other parameters in some methods) are used as the variables of the objective functions. A 3D object with planar faces is derived by finding values for these variables that minimize the objective functions. These methods work well for simple objects with a small number N of variables. As N grows, however, it is very difficult for them to find expected objects. This is because with the nonlinear objective functions in a space of large dimension N, the search for optimal solutions can easily get trapped into local minima. In this paper, we use the parameters of the planes that pass through the planar faces of an object as the variables of the objective function. This leads to a set of linear constraints on the planes of the object, resulting in a much lower dimensional nullspace where optimization is easier to achieve. We prove that the dimension of this nullspace is exactly equal to the minimum number of vertex depths which define the 3D object. Since a practical line drawing is usually not an exact projection of a 3D object, we expand the nullspace to a larger space based on the singular value decomposition of the projection matrix of the line drawing. In this space, robust 3D reconstruction can be achieved. Compared with two most related methods, our method not only can reconstruct more complex 3D objects from 2D line drawings, but also is computationally more efficient.     

Recognition by prototypes

A scheme for recognizing 3D objects from single 2D images under orthographic projection is introduced. The scheme proceeds in two stages. In the first stage, the categorization stage, the image is compared to prototype objects. For each prototype, the view that most resembles the image is recovered, and, if the view is found to be similar to the image, the class identity of the object is determined. In the second stage, the identification stage, the observed object is compared to the individual models of its class, where classes are expected to contain objects with relatively similar shapes. For each model, a view that matches the image is sought. If such a view is found, the object's specific identity is determined. The advantage of categorizing the object before it is identified is twofold. First, the image is compared to a smaller number of models, since only models that belong to the object's class need to be considered. Second, the cost of comparing the image to each model in a class is very low, because correspondence is computed once for the whole class. More specifically, the correspondence and object pose computed in the categorization stage to align the prototype with the image are reused in the identification stage to align the individual models with the image. As a result, identification is reduced to a series of simple template comparisons. The paper concludes with an algorithm for constructing optimal prototypes for classes of objects.     

The complex EGI: a new representation for 3-D pose determination

The complex extended Gaussian image (CEGI), a 3D object representation that can be used to determine the pose of an object, is described. In this representation, the weight associated with each outward surface normal is a complex weight. The normal distance of the surface from the predefined origin is encoded as the phase of the weight, whereas the magnitude of the weight is the visible area of the surface. This approach decouples the orientation and translation determination into two distinct least-squares problems. The justification for using such a scheme is twofold: it not only allows the pose of the object to be extracted, but it also distinguishes a convex object from a nonconvex object having the same EGI representation. The CEGI scheme has the advantage of not requiring explicit spatial object-model surface correspondence in determining object orientation and translation. Experiments involving synthetic data of two polyhedral and two smooth objects are presented to illustrate the feasibility of this method     

Strategies of Multi-view and Multi-matching for 3D Object Recognition

A multi-view representation scheme and a multi-matching strategy for 3D object recognition are described; 3D objects are represented in terms of their 2D appearances so that 2D techniques can be applied to 3D recognition. Appearances of objects in the representation scheme are further organized in a hierarchical manner so that the matching process can reduce its search space by examining only the optimal view at every level of the representation scheme. In our multi-matching strategy, the matching module is composed of four components: point matcher, string matcher, vector matcher, and chamfer matcher. Each matcher is associated with a termination rule so that impossible views can be rejected at the early stages of the matching process. Experimental results reveal that the proposed strategies are feasible for 3D object recognition.     

Decomposition of complex line drawings with hidden lines for 3D planar-faced manifold object reconstruction

Three-dimensional object reconstruction from a single 2D line drawing is an important problem in computer vision. Many methods have been presented to solve this problem, but they usually fail when the geometric structure of a 3D object becomes complex. In this paper, a novel approach based on a divide-and-conquer strategy is proposed to handle the 3D reconstruction of a planar-faced complex manifold object from its 2D line drawing with hidden lines visible. The approach consists of four steps: 1) identifying the internal faces of the line drawing, 2) decomposing the line drawing into multiple simpler ones based on the internal faces, 3) reconstructing the 3D shapes from these simpler line drawings, and 4) merging the 3D shapes into one complete object represented by the original line drawing. A number of examples are provided to show that our approach can handle 3D reconstruction of more complex objects than previous methods.     

Shapeme histogram projection and matching for partial object recognition

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.     

Evolutionary search for faces from line drawings

Single 2D line drawing is a straightforward method to illustrate 3D objects. The faces of an object depicted by a line drawing give very useful information for the reconstruction of its 3D geometry. Two recently proposed methods for face identification from line drawings are based on two steps: finding a set of circuits that may be faces and searching for real faces from the set according to some criteria. The two steps, however, involve two combinatorial problems. The number of the circuits generated in the first step grows exponentially with the number of edges of a line drawing. These circuits are then used as the input to the second combinatorial search step. When dealing with objects having more faces, the combinatorial explosion prevents these methods from finding solutions within feasible time. This paper proposes a new method to tackle the face identification problem by a variable-length genetic algorithm with novel heuristic and geometric constraints incorporated for local search. The hybrid GA solves the two combinatorial problems simultaneously. Experimental results show that our algorithm can find the faces of a line drawing having more than 30 faces much more efficiently. In addition, simulated annealing for solving the face identification problem is also implemented for comparison.     

基于AdaBoost的公交客流量统计算法

为解决复杂场景目标识别中伪目标的干扰问题,采用基于AdaBoost分类的方法分析疑似目标的三维轨迹,结合真实目标的共有的特征信息,进一步分类真实目标与伪目标。首先,根据深度相机获取的深度图像提取疑似目标的人头区域,利用Kalman滤波跟踪得到的二维轨迹。其次,通过摄像机标定将目标的二维轨迹转换为空间中的三维轨迹。最后,利用AdaBoost训练正负样本得到强分类器,进一步分类真实目标与伪目标。实验结果表明,该方法能够有效的提高目标识别的精度,对复杂场景下的目标识别具有良好的适应性。     

Obtaining complete 2 D view representation of polyhedra using concept of seedling single-view area

This article deals with construction of complete 2 D exact view models of polyhedral objects for visual identification systems. In particular, a new method and an algorithm views generation using the view sphere with perspective concept are described. A set of views generated by this method forms a complete view representation of the object. The method of ensuring completeness of the view representation by controlling covering of the view space (by single-view areas) is used in the presented algorithm. The perspective projection used for calculating the views, the total, tight covering of the view sphere by the single-view areas and -dimensionality of the views ensure, in our opinion, unambiguous and proper identification of polyhedral objects. The method consists in calculating single (any) view, determining the corresponding single-view area (so-called seedling single-view area) and then spiral propagation of neighbouring single-view areas until the whole view sphere is covered by them (i.e., until the border register containing the border between the covered and uncovered parts of the view sphere becomes empty). Having a complete set of single-view areas, we get a complete set of views as well. A method of determining single-view areas for convex polyhedra is also presented.     

Voxel-based three-view hybrid parallel network for 3D object classification

Three-dimensional models are widely used in the fields of multimedia, computer graphics, virtual reality, entertainment, design, and manufacturing because of the rich information that preserves the surface, color and texture of real objects. Therefore, effective 3D object classification technology has become an urgent need. Previous methods usually directly convert classic 2D convolution into 3D form and apply it to objects with binary voxel representation, which may lose internal information that is essential for recognition. In this paper, we propose a novel voxel-based three-view hybrid parallel network for 3D shape classification. This method first obtains the depth projection views of the three-dimensional model from the front view, the top view and the side view, so as to preserve the spatial information of the three-dimensional model to the greatest extent, and output its predicted probability value for the category of the three-dimensional model, and then combining the three-view parallel network with voxel sub-network performs weight fusion, and then uses Softmax for classification. We conducted a series of experiments to verify the design of the network and achieved competitive performance in the 3D object classification tasks of ModelNet10 and ModelNet40.     

Particle-based sampling and meshing of surfaces in multimaterial volumes

Methods that faithfully and robustly capture the geometry of complex material interfaces in labeled volume data are important for generating realistic and accurate visualizations and simulations of real-world objects. The generation of such multimaterial models from measured data poses two unique challenges: first, the surfaces must be well-sampled with regular, efficient tessellations that are consistent across material boundaries; and second, the resulting meshes must respect the nonmanifold geometry of the multimaterial interfaces. This paper proposes a strategy for sampling and meshing multimaterial volumes using dynamic particle systems, including a novel, differentiable representation of the material junctions that allows the particle system to explicitly sample corners, edges, and surfaces of material intersections. The distributions of particles are controlled by fundamental sampling constraints, allowing Delaunay-based meshing algorithms to reliably extract watertight meshes of consistently high-quality.