When is it Possible to Identify 3D Objects From Single Images Using Class Constraints?

One approach to recognizing objects seen from arbitrary viewpoint is by extracting invariant properties of the objects from single images. Such properties are found in images of 3D objects only when the objects are constrained to belong to certain classes (e.g., bilaterally symmetric objects). Existing studies that follow this approach propose how to compute invariant representations for a handful of classes of objects. A fundamental question regarding the invariance approach is whether it can be applied to a wide range of classes. To answer this question it is essential to study the set of classes for which invariance exists. This paper introduces a new method for determining the existence of invariant functions for classes of objects together with the set of images from which these invariants can be computed. We develop algebraic tests that determine whether the objects in a given class can be identified from single images. These tests apply to classes of objects undergoing affine projection. In addition, these tests allow us to determine the set of views of the objects which are degenerate. We apply these tests to several classes of objects and determine which of them is identifiable and which of their views are degenerate.     

基于姿态对齐的行人重识别方法

行人重识别是指根据输入的某个行人图片, 在视频监控网络中对该行人目标进行检索. 行人的姿态变化和监控场景的亮度变化是该任务的两个主要挑战. 针对行人的姿态变化问题, 本文首先对训练集中行人图片进行稠密 图像块采样获得图像块集合, 然后对每一个图像块提取其局部表观空间特征, 最后在此特征集上聚类得到通用的行人部件字典. 由于该部件字典编码了行人的部件信息, 因此通过该字典内的每一个码元可以建立两幅行人图像中特定图像块之间的对应关系. 将两幅行人图片的图像块集合分别向部件字典投影, 可以获得2幅行人图片姿态对齐后的图像块序列. 针对监控场景的亮度变化问题, 本文在姿态对齐后的图像块上分别提取4种颜色描述子, 并将不同颜色描述子下的图像块相似性进行分数级组合以获得更好的亮度不变性. 其中不同颜色描述子之间的组合系数通过结构化输出支持向量机学习得到. 在常用的视点不变行人重识别(viewpoint invariant pedestrian recognition,VIPeR)数据集上的实验结果表明, 该方法在存在行人姿态变化和场景亮度变化干扰时获得了较好的行人重识别效果.     

Thermophysical algebraic invariants from infrared imagery forobject recognition

An important issue in developing a model-based vision system is the specification of features that are invariant to viewing and scene conditions and also specific, i.e., the feature must have different values for different classes of objects. We formulate a new approach for establishing invariant features. Our approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and state which affect images sensed in the nonvisible spectrum. A new type of invariance called thermophysical invariance is defined. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. The approach is based on a physics-based model that is derived from the principle of the conservation of energy applied at the surface of the imaged object     

Colour and rotation invariant textural features based on Markov random fields

A visual appearance of natural materials significantly depends on acquisition circumstances, particularly illumination conditions and viewpoint position, whose variations cause difficulties in the analysis of real scenes. We address this issue with novel texture features, based on fast estimates of Markovian statistics, that are simultaneously rotation and illumination invariant. The proposed features are invariant to in-plane material rotation and illumination spectrum (colour invariance), they are robust to local intensity changes (cast shadows) and illumination direction. No knowledge of illumination conditions is required and recognition is possible from a single training image per material. The material recognition is tested on the currently most realistic visual representation - Bidirectional Texture Function (BTF), using CUReT and ALOT texture datasets with more than 250 natural materials. Our proposed features significantly outperform leading alternatives including Local Binary Patterns (LBP, LBP-HF) and texton MR8 methods.     

The quotient image: class-based re-rendering and recognition withvarying illuminations

The paper addresses the problem of “class-based” image-based recognition and rendering with varying illumination. The rendering problem is defined as follows: Given a single input image of an object and a sample of images with varying illumination conditions of other objects of the same general class, re-render the input image to simulate new illumination conditions. The class-based recognition problem is similarly defined: Given a single image of an object in a database of images of other objects, some of them multiply sampled under varying illumination, identify (match) any novel image of that object under varying illumination with the single image of that object in the database. We focus on Lambertian surface classes and, in particular, the class of human faces. The key result in our approach is based on a definition of an illumination invariant signature image which enables an analytic generation of the image space with varying illumination. We show that a small database of objects-in our experiments as few as two objects-is sufficient for generating the image space with varying illumination of any new object of the class from a single input image of that object. In many cases, the recognition results outperform by far conventional methods and the re-rendering is of remarkable quality considering the size of the database of example images and the mild preprocess required for making the algorithm work     

Slow feature analysis: unsupervised learning of invariances

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.     

Distinctive Image Features from Scale-Invariant Keypoints

This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.     

A Bayesian approach for object classification based on clusters of SIFT local features

Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, scale, illumination and partial occlusion. However, they have poor performance for classification tasks. In this work, SIFT features are used to solve object class recognition problems in images using a two-step process. In its first step, the proposed method performs clustering on the extracted features in order to characterize the appearance of the different classes. Then, in the classification step, it uses a three layer Bayesian network for object class recognition. Experiments show quantitatively that clusters of SIFT features are suitable to represent classes of objects. The main contributions of this paper are the introduction of a Bayesian network approach in the classification step to improve performance in an object class recognition task, and a detailed experimentation that shows robustness to changes in illumination, scale, rotation and partial occlusion.     

Moment invariants for recognition under changing viewpoint and illumination

Generalised color moments combine shape and color information and put them on an equal footing. Rational expressions of such moments can be designed, that are invariant under both geometric deformations and photometric changes. These generalised color moment invariants are effective features for recognition under changing viewpoint and illumination. The paper gives a systematic overview of such moment invariants for several combinations of deformations and photometric changes. Their validity and potential is corroborated through a series of experiments. Both the cases of indoor and outdoor images are considered, as illumination changes tend to differ between these circumstances. Although the generalised color moment invariants are extracted from planar surface patches, it is argued that invariant neighbourhoods offer a concept through which they can also be used to deal with 3D objects and scenes.     

Face recognition: the problem of compensating for changes inillumination direction

A face recognition system must recognize a face from a novel image despite the variations between images of the same face. A common approach to overcoming image variations because of changes in the illumination conditions is to use image representations that are relatively insensitive to these variations. Examples of such representations are edge maps, image intensity derivatives, and images convolved with 2D Gabor-like filters. Here we present an empirical study that evaluates the sensitivity of these representations to changes in illumination, as well as viewpoint and facial expression. Our findings indicated that none of the representations considered is sufficient by itself to overcome image variations because of a change in the direction of illumination. Similar results were obtained for changes due to viewpoint and expression. Image representations that emphasized the horizontal features were found to be less sensitive to changes in the direction of illumination. However, systems based only on such representations failed to recognize up to 20 percent of the faces in our database. Humans performed considerably better under the same conditions. We discuss possible reasons for this superiority and alternative methods for overcoming illumination effects in recognition     

The illumination-invariant matching of deterministic localstructure in color images

The availability of multiple spectral measurements at each pixel in an image provides important additional information for recognition. Spectral information is of particular importance for applications where spatial information is limited. Such applications include the recognition of small objects or the recognition of small features on partially occluded objects. We introduce a feature matrix representation for deterministic local structure in color images. Although feature matrices are useful for recognition, this representation depends on the spectral properties of the scene illumination. Using a linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that changes in the spectral content of the illumination correspond to linear transformations of the feature matrices, and that image plane rotations correspond to circular shifts of the matrices. From these relationships, we derive an algorithm for the recognition of local surface structure which is invariant to these scene transformations. We demonstrate the algorithm with a series of experiments on images of real objects     

Illumination invariant extraction for face recognition using neighboring wavelet coefficients

The features of a face can change drastically as the illumination changes. In contrast to pose position and expression, illumination changes present a much greater challenge to face recognition. In this paper, we propose a novel wavelet based approach that considers the correlation of neighboring wavelet coefficients to extract an illumination invariant. This invariant represents the key facial structure needed for face recognition. Our method has better edge preserving ability in low frequency illumination fields and better useful information saving ability in high frequency fields using wavelet based NeighShrink denoise techniques. This method proposes different process approaches for training images and testing images since these images always have different illuminations. More importantly, by having different processes, a simple processing algorithm with low time complexity can be applied to the testing image. This leads to an easy application to real face recognition systems. Experimental results on Yale face database B and CMU PIE Face Database show that excellent recognition rates can be achieved by the proposed method.     

基于总变分模型的光照不变人脸识别算法

提出了一种基于L1总变分模型的对数商图像光照不变人脸识别算法。用L1总变分模型作为低通滤波算子对图像平滑滤波,得到图像光照分量的估计,然后在对数域中定义原图像与其光照分量的商为光照归一化图像,并用该图像作为光照不变量进行人脸识别。基于L1总变分模型的平滑滤波具有较好的边缘保持作用,能有效地消除光晕现象,并且参数设置简单。在YaleB和CMU PIE 人脸图像库上的试验结果表明,该算法能有效地提高人脸识别系统在不同光照条件下的识别率。     

Learning viewpoint invariant perceptual representations from cluttered images

In order to perform object recognition, it is necessary to form perceptual representations that are sufficiently specific to distinguish between objects, but that are also sufficiently flexible to generalize across changes in location, rotation, and scale. A standard method for learning perceptual representations that are invariant to viewpoint is to form temporal associations across image sequences showing object transformations. However, this method requires that individual stimuli be presented in isolation and is therefore unlikely to succeed in real-world applications where multiple objects can co-occur in the visual input. This paper proposes a simple modification to the learning method that can overcome this limitation and results in more robust learning of invariant representations.     

基于扩展的SURF描述符的彩色图像配准技术*

目前大多数图像配准算法都需要先将彩色图像转换为灰度图像再进行图像配准,色彩信息的丢失可能引起图像的误匹配。本文在SURF算法的基础上,提出了构建颜色描述向量扩展SURF描述符,形成ESURF描述符,再进行图像配准的方法。该算法能够充分利用彩色图像的色彩信息,相比大多数算法基于灰度图像的配准方法有更高的鲁棒性,同时继承了SURF算法良好的性能。描述符性能测试和图像配准测试证明：ESURF算法比灰度图像SURF算法在图像尺度变化、旋转、模糊、视角变化、特别是光照变化方面有更高的鲁棒性。     

Face recognition using a fuzzy fisherface classifier

In this study, we are concerned with face recognition using fuzzy fisherface approach and its fuzzy set based augmentation. The well-known fisherface method is relatively insensitive to substantial variations in light direction, face pose, and facial expression. This is accomplished by using both principal component analysis and Fisher's linear discriminant analysis. What makes most of the methods of face recognition (including the fisherface approach) similar is an assumption about the same level of typicality (relevance) of each face to the corresponding class (category). We propose to incorporate a gradual level of assignment to class being regarded as a membership grade with anticipation that such discrimination helps improve classification results. More specifically, when operating on feature vectors resulting from the PCA transformation we complete a Fuzzy K-nearest neighbor class assignment that produces the corresponding degrees of class membership. The comprehensive experiments completed on ORL, Yale, and CNU (Chungbuk National University) face databases show improved classification rates and reduced sensitivity to variations between face images caused by changes in illumination and viewing directions. The performance is compared vis-à-vis other commonly used methods, such as eigenface and fisherface.