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.     

轮廓矩不变量及其在物体形状识别中的应用

为了有效地刻画物体的形状特征，在基于区域的Hu矩不变量的基础上，构造了一种基于物体轮廓曲线的新的矩不变量，即轮廓矩不变量。该不变量不仅独立于物体本身的颜色和灰度级，而且具有平移、旋转和尺度不变性，因此可将轮廓矩不变量应用于物体形状的识别，为了能快速地进行物体形状识别，还讨论了小波边缘检测和轮廓的获取问题及其算法。实验表明，基于这种轮廓矩的识别算法具有很好的识别率。     

An algebraic theory of object-oriented systems

The paper presents an algebraic specification of net objects. By net objects we mean those that are defined in object-oriented paradigms and those defined with nested relations in complex database models. An algebraic specification is set up that involves structures of net objects, accesses of net objects, and some features of object-oriented programming, such as multiple inheritance and polymorphism. Objects and their relationships are then characterized formally in the modeling, which utilizes the hierarchical approach in the algebraic theory of abstract data types, and is further developed by adding mechanisms from existing object systems. Categories of net objects are presented with the properties of accesses among them     

室外场景CSIFT的关键点检测和特征匹配

在目标的识别和匹配中,SIFT已经被证明是鲁棒性最好的局部不变特征描述符.虽然它能够对各种几何变化保持不变性,但却忽略了目标的颜色信息,限制了它的性能.提出一种改进的彩色的尺度不变特征变换(CSIFT)方法,结合目标的颜色特征与几何特征,针对室外场景的特点,简化了颜色不变量形式,实现了室外场景中目标的关键点检测和特征匹配.实验结果表明,CSIFT方法比经典的SIFT能更好地适用于室外场景中的关键点检测和特征匹配.     

Generalization to Novel Views: Universal,Class-based,and Model-based Processing

A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewing conditions such as viewing position and illumination. In this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place: universal, class, and model-based. The first is applicable equally to all objects, the second to a class of objects, and the third uses known properties of individual objects. We derive theoretical limitations on each of the three generalization levels. For the universal level, previous results have shown that no invariance can be obtained. Here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed. We also extend the results to changes of illumination direction. For the class level, previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist. Here, we distinguish between classes that admit such invariance and classes that do not. We demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects. Furthermore, we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level, when the objects are restricted to belong to a given class, an invariant function to illumination direction can be defined. A general conclusion of this study is that class-based processing, that has not been used extensively in the past, is often advantageous for dealing with variations due to viewpoint and illuminant changes.     

Learning optimized features for hierarchical models of invariant object recognition

There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network. We show that principles of sparse coding, which were previously mostly applied to the initial feature detection stages, can also be employed to obtain optimized intermediate complex features. We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy. The approach explicitly enforces symmetry constraints like translation invariance on the feature set. This leads to a dimension reduction in the search space of optimal features and allows determining more efficiently the basis representatives, which achieve a sparse decomposition of the input. We analyze the quality of the learned feature representation by investigating the recognition performance of the resulting hierarchical network on object and face databases. We show that a hierarchy with features learned on a single object data set can also be applied to face recognition without parameter changes and is competitive with other recent machine learning recognition approaches. To investigate the effect of the interplay between sparse coding and processing nonlinearities, we also consider alternative feedforward pooling nonlinearities such as presynaptic maximum selection and sum-of-squares integration. The comparison shows that a combination of strong competitive nonlinearities with sparse coding offers the best recognition performance in the difficult scenario of segmentation-free recognition in cluttered surround. We demonstrate that for both learning and recognition, a precise segmentation of the objects is not necessary.     

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.     

On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes

3D object recognition from local features is robust to occlusions and clutter. However, local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features. We present an algorithm for the detection of such keypoints on 3D models and partial views of objects. The keypoints are highly repeatable between partial views of an object and its complete 3D model. We also propose a quality measure to rank the keypoints and select the best ones for extracting local features. Keypoints are identified at locations where a unique local 3D coordinate basis can be derived from the underlying surface in order to extract invariant features. We also propose an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales. Features are projected to a PCA subspace and matched to find correspondences between a database and query object. Each pair of matching features gives a transformation that aligns the query and database object. These transformations are clustered and the biggest cluster is used to identify the query object. Experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi-scale features have a recognition rate of over 95% for up to 80% occluded objects.     

Object-oriented specification and formal verification of real-time systems

An object-oriented approach for specification and verification of real-time systems is described in this paper. It is motivated by taking advantage of object-oriented techniques to produce real-time software that is easy to understand, maintain, and reuse. The approach specifies the structural, behavioral, and control aspects of objects in one model with a textual representation as well as a graphical representation. For ease to comprehend and use, the model encapsulates object states and allows an analyst to focus on specifying object operations one at a time. System behavior from individual objects can be deduced and analyzed. For safety considerations, the approach supports specification of failures to object behavior and their resultant faults. The approach also supports modeling of timed temporal constraints for specifying and verifying desirable real-time properties. An object timed temporal logic OTTL is defined for expressing the syntax and semantics of these constraints. Decision procedures for their verification are also presented.     

用不变矩和边界方向进行形状检索

基于形状的图像检索一直以来是图像内容检索的一个难点问题，而目前采用周长、面积、边角率等描述形状的方法不能使形状检索达到理想的效果．本文提出了一种新的针对图像形状的检索方法．首先，用Canny算子对图像进行平滑处理，提取图像边界方向直方图特征、其次，用不变矩来描述图像形状的区域特征，不变矩特征不受图像的缩放、平移和旋转的影响．最后，为了克服不变矩只关心对象区域，而对图像边界忽视的缺点，提出了不变矩与边界方向特征相结合的方法，使得检索取得更好的效果．本文通过对医学图像的形状检索实验，给出了实验结果和结论．     

A Neural Network Approach to Planar-Object Recognition in 3D Space

Most existing 2D object recognition algorithms are not perspective (or projective) invariant, and hence are not suitable for many real-world applications. By contrast, one of the primary goals of this research is to develop a flat object matching system that can identify and localise an object, even when seen from different viewpoints in 3D space. In addition, we also strive to achieve good scale invariance and robustness against partial occlusion as in any practical 2D object recognition system. The proposed system uses multi-view model representations and objects are recognised by self-organised dynamic link matching. The merit of this approach is that it offers a compact framework for concurrent assessments of multiple match hypotheses by promoting competitions and/or co-operations among several local mappings of model and test image feature correspondences. Our experiments show that the system is very successful in recognising object to perspective distortion, even in rather cluttered scenes. Receiveed: 29 May 1998?,Received in revised form: 12 October 1998?Accepted: 26 October 1998     

Modeling the material grading and structures of heterogeneous objects for layered manufacturing

The emphasis of this work is to discuss a scheme for modeling the material grading and structures of heterogeneous objects. To model the material grading of a heterogeneous object, a termed ‘grading source’ is defined. The grading sources are reference features, which specify the type and position of grading, and a material grading function for controlling the material variation within the geometric boundary of the objects. Within the proposed modeling scheme, a concept on representing objects, which are both heterogeneous in material and structure (e.g. composite laminates), will also be elaborated. For downstream layered manufacturing purposes, a contour sub-division algorithm on each layer arising from slicing a heterogeneous object is proposed. Within each slice, the material grading is decomposed into sub-contours according to the different grading variation. A parameter called ‘grading step-width’ is defined to control the number of sub-contours and resolution of the grading. With such discretization, it is, therefore, possible to build a heterogeneous object on layered manufacturing machines of different fabricating precision specification.     

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.     

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.     

Pictorial Query Specification for Browsing Through Spatially Referenced Image Databases

A pictorial query specification technique that enables the formulation of complex pictorial queries for browsing through a collection of spatially referenced images is presented. It is distinguished from most other methods by the fact that in these methods the query image specifies a target database image in its entirety whereas in our approach the query image specifies the combination of objects that the target database image should contain rather than being treated as a whole image. The query objects are represented by shape features although other features such as color, texture or wavelets could also be used. Using our technique, it is possible to specify which particular objects should appear in the target images as well as how many occurrences of each object are required. Moreover, it is possible to specify the minimum required certainty of matching between query-image objects and database-image objects, as well as to impose spatial constraints that specify bounds on the distance between objects and the relative direction between them. These spatial constraints can also be used to specify other topological relations such as enclosure, intersection, overlap, etc. Each pictorial query is composed of one or more query images. Each query image is constructed by selecting the required query objects and positioning them according to the desired spatial configuration. Boolean combinations of two or more query images are also possible by use of AND and OR operators. A query image may be negated in order to specify conditions that should not be satisfied by the database images that are retrieved successfully. In addition, a capability is provided to specify whether the same instance of an object is to be used when it appears in more than one of the query images that make up the pictorial query, or whether two different instances are allowed. Several example queries are given that demonstrate the expressive power of this query specification method. An algorithm for retrieving all database images that conform to a given pictorial query specification is presented. The user interface for using this pictorial query specification method to browse the results in a map image database application is described and illustrated via screen shots.     

柔性体力触觉形变模型研究

针对虚拟现实力触觉交互中柔性体受力与形变的复杂关系,提出一种基于球面调和函数(SphericalHarmonic,SPHARM))的柔性体力触觉仿真模型。利用SPHARM的正交归一、旋转不变、多尺度等特性完成柔性体几何模型的准确表达;将柔性体在不同作用力下的形变模型映射到共同的参照系统中,在共同的参照系统中,利用主成分分析方法(Principal Components Analysis,PCA)完成柔性体在不同作用力下的形变比较,根据简化后的波动方程计算物体形变后的反馈力。实验结果表明,该方法可以准确表达柔性体的形变过程,适用于柔性体力触觉实时交互研究。