Enhanced 3D shape recovery using the neural-based hybrid reflectance model.

It is known that most real surfaces usually are neither perfectly Lambertian model nor ideally specular model; rather, they are formed by the hybrid structure of these two models. This hybrid reflectance model still suffers from the noise, strong specular, and unknown reflectivity conditions. In this article, these limitations are addressed, and a new neural-based hybrid reflectance model is proposed. The goal of this method is to optimize a proper reflectance model by learning the weight and parameters of the hybrid structure of feedforward neural networks and radial basis function networks and to recover the 3D object shape by the shape from shading technique with this resulting model. Experimental results, including synthetic and real images, were performed to demonstrate the performance of the proposed reflectance model in the case of different specular effects and noise environments.     

Learning parametric specular reflectance model by radial basis function network

For the shape from shading problem, it is known that most real images usually contain specular components and are affected by unknown reflectivity. In the paper, these limitations are addressed and a neural-based specular reflectance model is proposed. The idea of this method is to optimize a proper specular model by learning the parameters of a radial basis function network and to recover the object shape by the variational approach with this resulting model. The obtained results are very encouraging and the performance is demonstrated by using the synthetic and real images in the case of different specular effects and noisy environments.     

Shape From Shading by Using Neural Based Colour Reflectance Model

In this Letter, a new methodology for Colour Shape From Shading problem is proposed. The problem of colour SFS refers to the well-known fact that most real objects usually contain mixtures of diffuse and specular colour reflections. In this paper, these limitations are addressed and a new colour neural based model is proposed. The proposed approach focuses on developing a generalized neural based colour reflectance model. Experimental results on synthetic coloured objects and a real coloured object were performed to demonstrate the performance of the proposed methodology.     

Shape recovery from shading by a new neural-based reflectance model.

We present a neural-based reflectance model of which the physical parameters of the reflectivity under different lighting conditions are interpreted by the network weights. The idea of our method is to optimize a proper reflectance model by an effective learning algorithm and to recover the object surface by a simple shape from shading recursive algorithm with this resulting model. Experiments, including synthetic and real images, were performed to demonstrate the performance of the proposed method for practical applications.     

基于混合反射模型的SFS有限元方法的研究

提出了基于混合反射模型的由明暗恢复物体三维形状的有限元算法。用正方形面元逼近光滑曲面,把曲面表示为所有节点基函数的线性组合;基于既含有漫反射成分又有镜面反射成分的混合模型,结合节点基函数,将反射图线性化。考虑数字图像的特点,直接使用离散形式的SFS问题的亮度约束形式,用最小化方法得到高度满足的线性方程;使用Kaczmarz算法计算出表面三维形状。使用合成图像和实际图像验证该文算法的有效性,探讨了该算法的性能。     

基于神经网络的三维图象恢复与重建新算法

简介了兰伯特Lambertian反射模型用于三维图象的恢复与重建的算法,由于其具有很大的局限性,为此,给出了一种基于神经网络与模糊处理相结合的新型反向模型,用于三维图象的恢复与重建,且新算法不需知道光源方向,经实验证明,具有计算快捷且图象识别精度高的特点。     

Robust face recognition using generalized neural reflectance model

A generalized neural reflectance (GNR) model for enhancing face recognition under variations in illumination and posture is presented in this paper. Our work is based on training a number of synthesis images of each face taken at single lighting direction with frontal/posture view. This way of synthesizing images can be used to build training cases for each face under different known illumination conditions from which face recognition can be significantly improved. However, reconstructing face shape may not easily be achieved and the human face images usually form by highly complex structure which suffers from strong specular and unknown reflective conditions. In this paper, these limitations are addressed by Cho and Chow (IEEE Trans Neural Netw 12(5):1204–1214, 2002). Face surfaces are recovered by this GNR model and face images in different poses are synthesized to create a database for training. Our training algorithm assigns to recognize the face identity by similarity measure on face features extracting first by the principle component analysis (PCA) method and then further processing by the Fisher’s discrimination analysis (FDA) to acquire lower dimensional patterns. Experimental results conducted on the Yale Face Database B show that lower error rates of classification and recognition are achieved under different variations in lighting and pose and the performance significantly outperforms the recognition without using the proposed GNR model.     

A Framework for Automatically Recovering Object Shape,Reflectance and Light Sources from Calibrated Images

In this paper, we present a complete framework for recovering an object shape, estimating its reflectance properties and light sources from a set of images. The whole process is performed automatically. We use the shape from silhouette approach proposed by R. Szeliski (1993) combined with image pixels for reconstructing a triangular mesh according to the marching cubes algorithm. A classification process identifies regions of the object having the same appearance. For each region, a single point or directional light source is detected. Therefore, we use specular lobes, lambertian regions of the surface or specular highlights seen on images. An identification method jointly (i) decides what light sources are actually significant and (ii) estimates diffuse and specular coefficients for a surface represented by the modified Phong model (Lewis, 1994). In order to validate our algorithm efficiency, we present a case study with various objects, light sources and surface properties. As shown in the results, our system proves accurate even for real objects images obtained with an inexpensive acquisition system.     

A lighting robust fitting approach of 3D morphable model for face reconstruction

Three-dimensional morphable model (3DMM) is a powerful tool for recovering 3D shape and texture from a single facial image. The success of 3DMM relies on two things: an effective optimization strategy and a realistic approach to synthesizing face images. However, most previous methods have focused on developing an optimization strategy under Phong’s synthesis approach. In this paper, we adopt a more realistic synthesis technique that fully considers illumination and reflectance in the 3DMM fitting process. Using the sphere harmonic illumination model (SHIM), our new synthesis approach can account for more lighting factors than Phong’s model. Spatially varying specular reflectance is also introduced into the synthesis process. Under SHIM, the cost function is nearly linear for all parameters, which simplifies the optimization. We apply our new optimization algorithm to determine the shape and texture parameters simultaneously. The accuracy of the recovered shape and texture can be improved significantly by considering the spatially varying specular reflectance. Hence, our algorithm produces an enhanced shape and texture compared with previous SHIM-based methods that recover shape from feature points. Although we use just a single input image in a profile pose, our approach gives plausible results. Experiments on a well-known image database show that, compared to state-of-the-art methods based on Phong’s model, the proposed approach enhances the robustness of the 3DMM fitting results under extreme lighting and profile pose.     

Light source position and reflectance estimation from a single view without the distant illumination assumption

Several techniques have been developed for recovering reflectance properties of real surfaces under unknown illumination. However, in most cases, those techniques assume that the light sources are located at infinity, which cannot be applied safely to, for example, reflectance modeling of indoor environments. In this paper, we propose two types of methods to estimate the surface reflectance property of an object, as well as the position of a light source from a single view without the distant illumination assumption, thus relaxing the conditions in the previous methods. Given a real image and a 3D geometric model of an object with specular reflection as inputs, the first method estimates the light source position by fitting to the Lambertian diffuse component, while separating the specular and diffuse components by using an iterative relaxation scheme. Our second method extends that first method by using as input a specular component image, which is acquired by analyzing multiple polarization images taken from a single view, thus removing its constraints on the diffuse reflectance property. This method simultaneously recovers the reflectance properties and the light source positions by optimizing the linearity of a log-transformed Torrance-Sparrow model. By estimating the object's reflectance property and the light source position, we can freely generate synthetic images of the target object under arbitrary lighting conditions with not only source direction modification but also source-surface distance modification. Experimental results show the accuracy of our estimation framework.     

Improving a Real-Time Neural-Based Stereo Vision System

Refinements of the energy expression of a real-time neural-based stereo vision system are presented. The neural network optimizes a scalar functional, that represents an area-based stereo matching algorithm. The neural system is reviewed and its performances presented. The proposed improvements are in terms of the exploitation of the image chromatic content and of local pixel information relative to the distance from an image feature. Experimental results showing the performance improvements are presented on synthetic and on real images. The hardware implementation currently in progress will straightforwardly benefit from these improvements.     

Using recurrent neural networks to detect changes in autocorrelated processes for quality monitoring

With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for quality control in the case of serially correlated data. In particular, the Elman’s recurrent neural network is proposed for manufacturing process quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for quality monitoring in the case of autocorrelated process data.     

Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability

We describe a method of learning generative models of objects from a set of images of the object under different, and unknown, illumination. Such a model allows us to approximate the objects' appearance under a range of lighting conditions. This work is closely related to photometric stereo with unknown light sources and, in particular, to the use of Singular Value Decomposition (SVD) to estimate shape and albedo from multiple images up to a linear transformation (Hayakawa, 1994). Firstly we analyze and extend the SVD approach to this problem. We demonstrate that it applies to objects for which the dominant imaging effects are Lambertian reflectance with a distant light source and a background ambient term. To determine that this is a reasonable approximation we calculate the eigenvectors of the SVD on a set of real objects, under varying lighting conditions, and demonstrate that the first few eigenvectors account for most of the data in agreement with our predictions. We then analyze the linear ambiguities in the SVD approach and demonstrate that previous methods proposed to resolve them (Hayakawa, 1994) are only valid under certain conditions. We discuss alternative possibilities and, in particular, demonstrate that knowledge of the object class is sufficient to resolve this problem. Secondly, we describe the use of surface consistency for putting constraints on the possible solutions. We prove that this constraint reduces the ambiguities to a subspace called the generalized bas relief ambiguity (GBR) which is inherent in the Lambertian reflectance function (and which can be shown to exist even if attached and cast shadows are present (Belhumeur et al., 1997)). We demonstrate the use of surface consistency to solve for the shape and albedo up to a GBR and describe, and implement, a variety of additional assumptions to resolve the GBR. Thirdly, we demonstrate an iterative algorithm that can detect and remove some attached shadows from the objects thereby increasing the accuracy of the reconstructed shape and albedo.     

反射图建模的表面重建算法

提出一种利用单幅图像的灰度估计表面形状的方法．对于一幅图像，使用单隐层小波神经网络建立非线性反射图函数模型，通过神经网络的训练最小化误差函数得到网络权值．利用变分法得到景物最终表面高度值，并引入分级实现降低计算量．以此模型为基础。不再需要预知光源参数．实验结果表明了该方法的有效性，且其在恢复精度上有所提高．     

Estimating Facial Reflectance Properties Using Shape-from-Shading

In this paper we show how to estimate facial surface reflectance properties (a slice of the BRDF and the albedo) in conjunction with the facial shape from a single image. The key idea underpinning our approach is to iteratively interleave the two processes of estimating reflectance properties based on the current shape estimate and updating the shape estimate based on the current estimate of the reflectance function. For frontally illuminated faces, the reflectance properties can be described by a function of one variable which we estimate by fitting a curve to the scattered and noisy reflectance samples provided by the input image and estimated shape. For non-frontal illumination, we fit a smooth surface to the scattered 2D reflectance samples. We make use of a novel statistical face shape constraint which we term ‘model-based integrability’ which we use to regularise the shape estimation. We show that the method is capable of recovering accurate shape and reflectance information from single grayscale or colour images using both synthetic and real world imagery. We use the estimated reflectance measurements to render synthetic images of the face in varying poses. To synthesise images under novel illumination, we show how to fit a parametric model of reflectance to the estimated reflectance function.     

Shape and View Independent Reflectance Map from Multiple Views

We consider the problem of estimating the 3D shape and reflectance properties of an object made of a single material from a set of calibrated views. To model the reflectance, we propose to use the View Independent Reflectance Map (VIRM), which is a representation of the joint effect of the diffuse+specular Bidirectional Reflectance Distribution Function (BRDF) and the environment illumination. The object shape is parameterized using a triangular mesh. We pose the estimation problem as minimizing the cost of matching input images, and the images synthesized using the shape and VIRM estimates. We show that by enforcing a constant value of VIRM as a global constraint, we can minimize the cost function by iterating between the VIRM and shape estimation. Experimental results on both synthetic and real objects show that our algorithm can recover both the 3D shape and the diffuse/specular reflectance information. Our algorithm does not require the light sources to be known or calibrated. The estimated VIRM can be used to predict the appearances of objects with the same material from novel viewpoints and under transformed illumination. The support of National Science Foundation under grant ECS 02-25523 is gratefully acknowledged. Tianli Yu was supported in part by a Beckman Institute Graduate Fellowship.     

SO dynamic deformation for building of 3-D models

Three-dimensional (3D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3D modeling is to construct a representation of a 3D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, where the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems.     

Retrieving shape information from multiple images of a specularsurface

In many remote sensing and machine vision applications, the shape of a specular surface such as water, glass, or polished metal must be determined instantaneously and under natural lighting conditions. Most image analysis techniques, however, assume surface reflectance properties or lighting conditions that are incompatible with these situations. To retrieve the shape of smooth specular surfaces, a technique known as specular surface stereo was developed. The method analyzes multiple images of a surface and finds a surface shape that results in a set of synthetic images that match the observed ones. An image synthesis model is used to predict image irradiance values as a function of the shape and reflectance properties of the surface, camera geometry, and radiance distribution of the illumination. The specular surface stereo technique was tested by processing four numerical simulations-a water surface illuminated by a low- and high-contrast extended light source, and a mirrored surface illuminated by a low- and high-contrast extended light source. Under these controlled circumstances, the recovered surface shape showed good agreement with the known input     

Moment-based alignment for shape prior with variational B-spline level set

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.     

Mixture of spherical distributions for single-view relighting

We present a method for simultaneously estimating the illumination of a scene and the reflectance property of an object from single view images - a single image or a small number of images taken from the same viewpoint. We assume that the illumination consists of multiple point light sources and the shape of the object is known. First, we represent the illumination on the surface of a unit sphere as a finite mixture of von Mises-Fisher distributions based on a novel spherical specular reflection model that well approximates the Torrance-Sparrow reflection model. Next, we estimate the parameters of this mixture model including the number of its component distributions and the standard deviation of them, which correspond to the number of light sources and the surface roughness, respectively. Finally, using these results as the initial estimates, we iteratively refine the estimates based on the original Torrance-Sparrow reflection model. The final estimates can be used to relight single-view images such as altering the intensities and directions of the individual light sources. The proposed method provides a unified framework based on directional statistics for simultaneously estimating the intensities and directions of an unknown number of light sources as well as the specular reflection parameter of the object in the scene.