Fast linear discriminant analysis using binary bases

Linear Discriminant Analysis (LDA) is a widely used technique for pattern classification. It seeks the linear projection of the data to a low dimensional subspace where the data features can be modelled with maximal discriminative power. The main computation in LDA is the dot product between LDA base vector and the data point which involves costly element-wise floating point multiplications. In this paper, we present a fast linear discriminant analysis method called binary LDA (B-LDA), which possesses the desirable property that the subspace projection operation can be computed very efficiently. We investigate the LDA guided non-orthogonal binary subspace method to find the binary LDA bases, each of which is a linear combination of a small number of Haar-like box functions. We also show that B-LDA base vectors are nearly orthogonal to each other. As a result, in the non-orthogonal vector decomposition process, the computationally intensive pseudo-inverse projection operator can be approximated by the direct dot product without causing significant distance distortion. This direct dot product projection can be computed as a linear combination of the dot products with a small number of Haar-like box functions which can be efficiently evaluated using the integral image. The proposed approach is applied to face recognition on ORL and FERET dataset. Experiments show that the discriminative power of binary LDA is preserved and the projection computation is significantly reduced.     

基于网格IC图象的多模板快速匹配算法

为了加快 IC图象中多个相似单元模板的匹配与定位 ,提出了一种基于网格 IC图象的多模板快速匹配算法 .该算法首先抽取网格图象和模板的二值拓扑结构 ,以构成图象和模板的粗分辨率表示 ;然后 ,在拓扑结构表示上通过综合来构造多模板的二叉树模型 ;接着 ,在二值拓扑结构表示上运用树模型进行搜索 ,在搜索过程中应用二叉决策树识别多个模板 ;最后 ,将粗匹配得到的目标 ,在原图象对应位置的小邻域内进行二次匹配 ,以确定模板和对应实例的位置 .应用此算法对 IC图象库进行测试 ,结果表明 ,所提出的多模板二叉决策树搜索算法与逐个模板匹配的方法相比 ,速度和效率均有较大幅度的提高     

Robust palmprint identification based on directional representations and compressed sensing

In this paper, we propose a novel approach for palmprint recognition, which contains two interesting components: directional representation and compressed sensing. Gabor wavelets can be well represented for biometric image for their similar characteristics to human visual system. However, these Gabor-based algorithms are not robust for image recognition under non-uniform illumination and suffer from the heavy computational burden. To improve the recognition performance under the low quality conditions with a fast operation speed, we propose novel palmprint recognition approach using directional representations. Firstly, the directional representation for palmprint appearance is obtained by the anisotropy filter, which is robust to drastic illumination changes and preserves important discriminative information. Then, the principal component analysis (PCA) is used for feature extraction to reduce the dimensions of the palmprint images. At last, based on a sparse representation on PCA feature, the compressed sensing is used to distinguish palms from different hands. Experimental results on the PolyU palmprint database show the proposed algorithm have better performance than that of the Gabor based methods.     

Acquiring linear subspaces for face recognition under variable lighting

Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.     

视觉导航特征点匹配及误匹配剔除算法

飞行器视觉导航中图像的复杂性对特征点匹配提出了很高的要求。根据图像HSV色彩空间各分量固有的稳定性提出了一种新的特征点不变向量生成方法,以基于欧氏距离的最近邻准则作为特征点的相似度量将该算法提取的特征应用于图像特征点匹配;为了降低特征点描述向量的维数,提高匹配的实时性,采用了主成分分析(PCA)方法。针对误匹配问题,提出了一种利用惯性导航系统的输出信息进行误匹配特征点检测的方法。最后,通过实验证明,所提出的色彩匹配方法可以提高匹配的准确率,并且通过将PCA方法与上述方法结合不仅可以保持匹配的准确性还能降低计算的复杂度;所提出的误匹配检测方法可以较好的剔除误匹配点并能满足实时性的要求,这为视觉导航提供了一种可靠性更高的特征点匹配方法。     

Surface signatures: an orientation independent free-form surface representation scheme for the purpose of objects registration and matching

This paper introduces anew free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information (seen from certain points) and produces images, called "surface signatures," at these points. Matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces. We propose using template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio is used. This representation scheme can be used as a global representation of the surface as well as a local one and performs near real-time registration. We show that the signature representation can be used to recover scaling transformation as well as matching objects in 3D scenes in the presence of clutter and occlusion. Applications presented include: free-form object matching, multimodal medical volumes registration, and dental teeth reconstruction from intraoral images.     

Combining multi-visual features for efficient indexing in a large image database

The optimized distance-based access methods currently available for multidimensional indexing in multimedia databases have been developed based on two major assumptions: a suitable distance function is known a priori and the dimensionality of the image features is low. It is not trivial to define a distance function that best mimics human visual perception regarding image similarity measurements. Reducing high-dimensional features in images using the popular principle component analysis (PCA) might not always be possible due to the non-linear correlations that may be present in the feature vectors. We propose in this paper a fast and robust hybrid method for non-linear dimensions reduction of composite image features for indexing in large image database. This method incorporates both the PCA and non-linear neural network techniques to reduce the dimensions of feature vectors so that an optimized access method can be applied. To incorporate human visual perception into our system, we also conducted experiments that involved a number of subjects classifying images into different classes for neural network training. We demonstrate that not only can our neural network system reduce the dimensions of the feature vectors, but that the reduced dimensional feature vectors can also be mapped to an optimized access method for fast and accurate indexing. Received 11 June 1998 / Accepted 25 July 2000 Published online: 13 February 2001     

Context-based object-class recognition and retrieval by generalized correlograms

We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs where each one encodes information about some local part and the spatial relations from this part to others (i.e., the part's context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that by integrating our representation with Boosting the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object's parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as Inverted Files, we show that thousands of images can be evaluated efficiently. The framework has been applied to different standard databases and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy.     

Accurate and speedy computation of image Legendre moments for computer vision applications

A novel algorithm that permits the fast and accurate computation of the Legendre image moments is introduced in this paper. The proposed algorithm is based on the block representation of an image and on a new image representation scheme, the Image Slice Representation (ISR) method. The ISR method decomposes a gray-scale image as an expansion of several two-level images of different intensities (slices) and thus enables the partial application of the well-known Image Block Representation (IBR) algorithm to each image component. Moreover, using the resulted set of image blocks, the Legendre moments’ computation can be accelerated through appropriate computation schemes. Extensive experiments prove that the proposed methodology exhibits high efficiency in calculating Legendre moments on gray-scale, but furthermore on binary images. The newly introduced algorithm is suitable for the computation of the Legendre moments for pattern recognition and computer vision applications, where the images consist of objects presented in a scene.     

Edge detection in the feature space

To build a consistent image representation model which can process the non-Gaussian distribution data, a novel edge detection method (KPCA-SCF) based on the kernel method is proposed. KPCA-SCF combines kernel principal component analysis and kernel subspace classification proposed in this paper to extract edge features. KPCA-SCF was tested and compared with linear PCA, nonlinear PCA and conventional methods such as Sobel, LOG, Canny, etc. Experiments on synthetic and real-world images show that KPCA-SCF is more robust under noisy conditions. KPCA-SCF's score of F-measure (0.44) ranks 11th in the Berkeley segmentation dataset and benchmark, it (0.54) ranks 10th tested on a noised image.     

实数型曲线描述子二值化研究

曲线匹配技术在模式识别、计算机视觉和图像理解中具有重要作用。随着移动设备的广泛使用,有必要研究存储空间小、匹配速度快的二值型曲线描述子。针对常见实数型曲线描述子(MSCD、IOMSD、IOCD和TCHP),利用阈值化方法,获得由0、1表示的二值曲线描述子。实验结果表明,在旋转、视角变化和光照变化条件下,提出的曲线二值描述子能够保持实数型描述子的匹配准确性,而占用的内存空间仅为原描述子的1/32或1/16。     

A novel binary image representation algorithm by using NAM and coordinate encoding procedure and its application to area calculation

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.     

Clustered blockwise PCA for representing visual data

Principal component analysis (PCA) is extensively used in computer vision and image processing. Since it provides the optimal linear subspace in a least-square sense, it has been used for dimensionality reduction and subspace analysis in various domains. However, its scalability is very limited because of its inherent computational complexity. We introduce a new framework for applying PCA to visual data which takes advantage of the spatio-temporal correlation and localized frequency variations that are typically found in such data. Instead of applying PCA to the whole volume of data (complete set of images), we partition the volume into a set of blocks and apply PCA to each block. Then, we group the subspaces corresponding to the blocks and merge them together. As a result, we not only achieve greater efficiency in the resulting representation of the visual data, but also successfully scale PCA to handle large data sets. We present a thorough analysis of the computational complexity and storage benefits of our approach. We apply our algorithm to several types of videos. We show that, in addition to its storage and speed benefits, the algorithm results in a useful representation of the visual data.     

基于特征运动的表情人脸识别

人脸像的面部表情识别一直是人脸识别的一个难点，为了提高表情人脸识别的鲁棒性，提出了一种基于特征运动的人脸识别方法，该方法首先利用块匹配的方法来确定表情人脸和无表情人脸之间的运动向量，然后利用主成分分析方法（PCA）从这些运动向量中，产生低维子空间，称之为特征运动空间，测试时，先将测试人脸与无表情人脸之间的运动向量投影到特征运动空间，再根据这个运动向量在特征运动空间里的残差进行人脸识别，同时还介绍了基于特征运动的个人模型方法和公共模型方法，实验结果证明，该新算法在表情人脸的识别上，优于特征脸方法，有非常高的识别率。     

二值图象的快速标记方法及其应用

连通区标记(Connected Components Labeling，CCL)是图象处理中的基础算法，是机器视觉和模式识别中提取目标、分析目标几何特征的常用方法。本文采用一种基于程的二值图象表示及基于树的标号合并的快速标记及图象几何特征分析方法，并给出其电级图象在线检测中的应用及与其它方法的比较，实验结果表明该方法是快速、有效的。     

An efficient similarity metric for omnidirectional vision sensors

This paper presents an efficient metric for the computation of the similarity among omnidirectional images (image matching). The representation of image appearance is based on feature vectors that include both the chromatic attributes of color sets and their mutual spatial relationships. The proposed metric fits well to robotic navigation using omnidirectional vision sensors, because it has very important properties: it is reflexive, compositional and invariant with respect to image scaling and rotation. The robustness of the metric was repeatedly tested using omnidirectional images for a robot localization task in a real indoor environment.     

Gabor texture in active appearance models

In computer vision applications, Active Appearance Models (AAMs) is usually used to model the shape and the gray-level appearance of an object of interest using statistical methods, such as PCA. However, intensity values used in standard AAMs cannot provide enough information for image alignment. In this paper, we firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor, three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments, we show that the proposed Gabor representations lead to more accurate and robust matching between model and images.     

Improved support vector classification using PCA and ICA feature space modification

An approach that unifies subspace feature selection and optimal classification is presented. Independent component analysis (ICA) and principal component analysis (PCA) provide a maximally variant or statistically independent basis for pattern recognition. A support vector classifier (SVC) provides information about the significance of each feature vector. The feature vectors and the principal and independent component bases are modified to obtain classification results which provide lower classification error and better generalization than can be obtained by the SVC on the raw data and its PCA or ICA subspace representation. The performance of the approach is demonstrated with artificial data sets and an example of face recognition from an image database.     

Use of Fourier and Karhunen-Loeve decomposition for fast patternmatching with a large set of templates

We present a fast pattern matching algorithm with a large set of templates. The algorithm is based on the typical template matching speeded up by the dual decomposition; the Fourier transform and the Karhunen-Loeve transform. The proposed algorithm is appropriate for the search of an object with unknown distortion within a short period. Patterns with different distortion differ slightly from each other and are highly correlated. The image vector subspace required for effective representation can be defined by a small number of eigenvectors derived by the Karhunen-Loeve transform. A vector subspace spanned by the eigenvectors is generated, and any image vector in the subspace is considered as a pattern to be recognized. The pattern matching of objects with unknown distortion is formulated as the process to extract the portion of the input image, find the pattern most similar to the extracted portion in the subspace, compute normalized correlation between them at each location in the input image, and find the location with the best score. Searching for objects with unknown distortion requires vast computation. The formulation above makes it possible to decompose highly correlated reference images into eigenvectors, as well as to decompose images in frequency domain, and to speed up the process significantly