Model-based invariants for 3-D vision

Invariance under a group of 3-D transformations seems a desirable component of an efficient 3-D shape representation. We propose representations which are invariant under weak perspective to either rigid or linear 3-D transformations, and we show how they can be computed efficiently from a sequence of images with a linear and incremental algorithm. We show simulated results with perspective projection and noise, and the results of model acquisition from a real sequence of images. The use of linear computation, together with the integration through time of invariant representations, offers improved robustness and stability. Using these invariant representations, we derive model-based projective invariant functions of general 3-D objects. We discuss the use of the model-based invariants with existing recognition strategies: alignment without transformation, and constant time indexing from 2-D images of general 3-D objects.     

Wavelet-based affine invariant representation: a tool forrecognizing planar objects in 3D space

A technique is developed to construct a representation of planar objects undergoing a general affine transformation. The representation can be used to describe planar or nearly planar objects in a three-dimensional space, observed by a camera under arbitrary orientations. The technique is based upon object contours, parameterized by an affine invariant parameter and the dyadic wavelet transform. The role of the wavelet transform is the extraction of multiresolution affine invariant features from the affine invariant contour representation. A dissimilarity function is also developed and used to distinguish among different object representations. This function makes use of the extrema on the representations, thus making its computation very efficient. A study of the effect of using different wavelet functions and their order or vanishing moments is also carried out. Experimental results show that the performance of the proposed representation is better than that of other existing methods, particularly when objects are heavily corrupted with noise     

Star pattern recognition based on features invariant under rotation

A star pattern recognition algorithm is proposed on the basis of features invariant under rotation. Guidance star identification via the algorithm is performed on star images captured by star sensor and simulated images. The results indicate that the proposed method presents better robustness against position and magnitude noise than conventional ones and eliminates rotation procedure and avoids the influence caused by grid size choice. The database feature storage for each pattern consists of simply four floating point numbers.     

Robust histogram construction from color invariants for object recognition

An effective object recognition scheme is to represent and match images on the basis of histograms derived from photometric color invariants. A drawback, however, is that certain color invariant values become very unstable in the presence of sensor noise. To suppress the effect of noise for unstable color invariant values, in this paper, histograms are computed by variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariant variables. As a result, the associated uncertainty is obtained for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel for the purpose of robust histogram construction. It is empirically verified that the proposed density estimator compares favorably to traditional histogram schemes for the purpose of object recognition.     

基于分数阶矩的仿射不变特征提取

仿射不变的特征提取在目标识别和配准中起关键作用, 图像矩是提取仿射不变特征的重要方法, 高阶矩对噪声较敏感, 实际中仅有几个由整数阶矩构造的仿射不变量可用. 本文引入分数阶矩, 它由变形累次积分定义, 不仅充分利用仿射变换映直线为直线这一特性,而且能方便地消除仿射变换前后极角因子的影响. 利用分数阶矩给出了仿射不变量的构造, 传统矩构造的不变量仅是这种构造的特例. 实验结果表明低次矩构造的不变量一般有较好的抗噪性能.     

Illuminant and device invariant colour using histogram equalisation

Colour can potentially provide useful information for a variety of computer vision tasks such as image segmentation, image retrieval, object recognition and tracking. However, for it to be helpful in practice, colour must relate directly to the intrinsic properties of the imaged objects and be independent of imaging conditions such as scene illumination and the imaging device. To this end many invariant colour representations have been proposed in the literature. Unfortunately, recent work (Second Workshop on Content-based Multimedia Indexing) has shown that none of them provides good enough practical performance.In this paper we propose a new colour invariant image representation based on an existing grey-scale image enhancement technique: histogram equalisation. We show that provided the rank ordering of sensor responses are preserved across a change in imaging conditions (lighting or device) a histogram equalisation of each channel of a colour image renders it invariant to these conditions. We set out theoretical conditions under which rank ordering of sensor responses is preserved and we present empirical evidence which demonstrates that rank ordering is maintained in practice for a wide range of illuminants and imaging devices. Finally, we apply the method to an image indexing application and show that the method out performs all previous invariant representations, giving close to perfect illumination invariance and very good performance across a change in device.     

Invariant algebra and the fusion of multi-spectral information

The astonishingly large varieties of multi-sensor scene signatures that need to be considered for building a robust machine-based scene analysis system require that one explore the possibility of deriving multi-sensor representations that are at the least invariant to scale, translation, and rotation with respect to the observer.The work presented in this paper is a probability density function (PDF)-based technique that uses the theory of invariant algebra to derive algebraic expressions that remain constant under an object's joint geometrical and surface material changes. This generalization of the geometrical invariants to cover both geometrical as well as physical changes in the images of a scene is a significant contribution to the state-of-the-art.Two cases of similar and dissimilar sensor types are considered. The fused invariants for the case of similar sensor types are unchanged under material as well as under affine transformations. For the case of dissimilar sensors, the approach leads to the derivation of the multi-sensor algebraic expressions that remain unchanged under scale, translation and two-dimensional rotation with respect to the observer. An analysis of the computational complexities of the presented techniques and its comparison with a typical non-invariant approach illustrate the noticeable advantages of the invariant method.     

Invariant image classification using triple-correlation-basedneural networks

Triple-correlation-based neural networks are introduced and used in this paper for invariant classification of 2D gray scale images. Third-order correlations of an image are appropriately clustered, in spatial or spectral domain, to generate an equivalent image representation that is invariant with respect to translation, rotation, and dilation. An efficient implementation scheme is also proposed, which is robust to distortions, insensitive to additive noise, and classifies the original image using adequate neural network architectures applied directly to 2D image representations. Third-order neural networks are shown to be a specific category of triple-correlation-based networks, applied either to binary or gray-scale images. A simulation study is given, which illustrates the theoretical developments, using synthetic and real image data.     

利用角度签名进行3维表面配准

3维表面的配准在3维物体重建、场景检测和物体识别过程中起着重要的作用。为此提出了一种新的3维表面表示方法——角度签名(angle signature),并将其用于3维表面配准。该表示方法将表面的局部几何信息表示成为1维的向量,具有对刚体变换的不变性。由于其简洁的表示方式,可以实现表面的快速配准。此外,该方法较其他3维表面的表示方法具有更强的鲁棒性。在实际应用中,为了提高表面配准的速度,首先筛选出特征点,然后利用特征点寻找表面之间的对应关系,从而将刚体变换的参数求出,实现表面的配准。实验结果表明,采用角度签名实现物体表面配准具有较快的速度和较高的精度。     

基于鲁棒正不变集的传感器故障区间估计

针对具有传感器故障和未知扰动与测量噪声的线性离散系统, 提出了一种传感器故障区间估计方法. 将传感器故障视为增广状态, 原始系统转化为一个等效的广义系统. 为了得到故障的点估计同时抑制扰动和噪声的影响, 基于有界实引理设计了一个针对广义系统的鲁棒状态观测器. 然后, 通过中心对称多胞体技术实现对故障的区间估计并基于鲁棒正不变集给出了一种降低区间估计计算量的方法. 最后, 通过一个垂直起降(Vertical take-off and landing, VTOL)飞行器线性化模型的仿真算例验证了所提出方法的有效性与优越性.     

Retrieval of gamma corrected images

This paper presents a histogram based image retrieval system for environments where the images may be subject to gamma correction. The images are transformed into representations that are invariant under gamma correction. The differential invariant employed is a local feature implemented with derivatives of the Gaussian. The image retrieval is based on feature histograms computed from the invariant representations. The invariant provides a generic method to trade off derivatives for an unknown power law parameter.     

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.     

基于单声学矢量传感器的最大能量动态声源波达方向跟踪算法

针对在空域非均匀噪声环境下目标定位跟踪问题,提出一种基于单声学矢量传感器的最大能量动态声源波达方向(DOA)跟踪算法.首先结合噪声协方差矩阵估计结果实现对传感器接收信号的预白化处理,进而确定加权参数值,提出一种加权参数固化的最大能量算法,从而在声压与振速域噪声功率比未知的条件下提高了DOA估计精度.在此基础上,利用最大能量定向估计子输出信息来构建运动目标的量测方程,并在容积Kalman滤波框架下实现对于动态声源的状态跟踪.理论分析与仿真结果验证了算法的可行性和有效性.     

Stability investigation of the sign representation of images

The stability notion of the sign representation of images is introduced. Group properties of the invariant transformations on sign representations of images are investigated. Analytical estimates are obtained of the stability measure of the sign representation of images noise-affected by the normal additive noise. The relation is set up between the stability of the complete sign representation and the stability of the window sign representation. The results of the statistical modeling that confirm the validity of theoretical estimates are presented.     

Robust 3D face recognition based on resolution invariant features

A novel resolution invariant local feature based method is proposed for 3D face recognition. Scale space extrema on shape index images and texture images are detected and matched, through which resolution and noise insensitive face matching is achieved without complex preprocessing and normalization. An outlier removal strategy is designed to eliminate incorrect matching points while keeping relevant ones. Six different scale invariant similarity measures are proposed and fused at the score level, which increases the robustness against expression variations. Systematical experiments are conducted on the FRGC v2.0 database, achieving in the neutral vs. all experiment a verification rate of 90.7% with un-normalized similarity scores, and 96.3% with normalized similarity scores at False Acceptance Rate (FAR) of 0.1%, and 96.2% rank-1 identification rate, which are comparable to the state of the art, and promising considering the significantly reduced preprocessing requirement.     

Subspace-based fault detection robust to changes in the noise covariances

The detection of changes in the eigenstructure of a linear time invariant system by means of a subspace-based residual function has been proposed previously. While enjoying some success in its applicability in particular in the context of vibration monitoring, the robustness of this framework against changes in the noise properties has not been properly addressed yet. In this paper, a new robust residual is proposed and the robustness of its statistics against changes in the noise covariances is shown. The complete theory for hypothesis testing for fault detection is derived and a numerical illustration is provided.     

一种基于多尺度空间的图像水印算法

当前数字水印对常见的图像处理攻击如高斯噪声、中值滤波、有损压缩等具有较强的抵抗能力,但其抵抗剪切、缩放等几何攻击的能力较差。因此,为提高数字水印的鲁棒性,结合尺度不变特征变换算子和视觉感知模型,提出一种基于不变特征的水印嵌入方法。基于广义高斯分布模型对小波系数进行刻画,并根据二值假设检验理论确定水印的检测阈值,由此进一步给出水印虚警和漏警之间的工作特性曲线关系。实验仿真结果表明提出的水印方法在抗压缩、叠加噪声、缩放、剪切、旋转等攻击时具有较好的鲁棒性能。     

基于相位相关的指纹拼接方法研究

基于指纹图像的旋转、平移、距离等不变特性,从增加指纹图像的有效特征点出发,提出了指纹拼接方法。该方法分为三步：第一步,提出基于中值滤波及局部属性的归一化预处理算法,去除图像噪声和影响相位相关度准确性的频率信息;第二步,图像进行伪极坐标快速傅立叶变换,根据旋转和平移的不变性,利用相位相关对图像进行快速配准;第三步,对配准的指纹图像完成平滑拼接。大量的实验结果证明本方法可以增加有效的指纹图像特征点数量,改善比对效果,计算复杂度较低,可以实现指纹图像的快速拼接。     

图像归一化与伪Zernike矩的鲁棒水印算法研究*

以基于矩的图像归一化技术及伪Zernike矩相关知识为基础,提出一种可有效抵抗几何攻击的数字水印新算法。算法首先利用归一化技术将原始图像映射到几何不变空间内;然后结合不变质心理论提取出归一化图像的重要区域;最后通过量化调制伪Zernike矩幅值将水印嵌入到重要区域中。仿真实验表明,该算法不仅具有较好的透明性,而且对常规信号处理(滤波、锐化、加噪和JPEG压缩等)和几何攻击(全局仿射变换、局部失真等)均具有较好的鲁棒性。     

Area and length preserving geometric invariant scale-spaces

In this paper, area preserving multi-scale representations of planar curves are described. This allows smoothing without shrinkage at the same time preserving all the scale-space properties. The representations are obtained deforming the curve via geometric heat flows while simultaneously magnifying the plane by a homethety which keeps the enclosed area constant. When the Euclidean geometric heat flow is used, the resulting representation is Euclidean invariant, and similarly it is affine invariant when the affine one is used. The flows are geometrically intrinsic to the curve, and exactly satisfy all the basic requirements of scale-space representations. In the case of the Euclidean heat flow, it is completely local as well. The same approach is used to define length preserving geometric flows. A similarity (scale) invariant geometric heat flow is studied as well in this work