An automated tactile sensing strategy for planar object recognitionand localization

The planning problem associated with tactile exploration for object recognition and localization is addressed. Given that an object has been sensed and is one of a number of modeled objects, and given that the data obtained so far are insufficient for recognition and/or localization, the methods developed determin the paths along which a point contact sensor must be directed in order to obtain further highly diagnostic measurements. Three families of sensor paths are found. The first is the family of paths for which recognition and localization are guaranteed. The second guarantees only that something will be learned. The third represents paths to avoid because nothing new will be learned. The methods are based on a small but powerful set of geometric ideas and are developed for two-dimensional, planar-faced objects. They are conceptually easily generalized to handle three-dimensional objects, including objects with through holes     

Practical reliable Bayesian recognition of 2D and 3D objects usingimplicit polynomials and algebraic invariants

We treat the use of more complex higher degree polynomial curves and surfaces of degree higher than 2, which have many desirable properties for object recognition and position estimation, and attack the instability problem arising in their use with partial and noisy data. The scenario discussed in this paper is one where we have a set of objects that are modeled as implicit polynomial functions, or a set of representations of classes of objects with each object in a class modeled as an implicit polynomial function, stored in the database. Then, given partial data from one of the objects, we want to recognize the object (or the object class) or collect more data in order to get better parameter estimates for more reliable recognition. Two problems arising in this scenario are discussed: 1) the problem of recognizing these polynomials by comparing them in terms of their coefficients; and 2) the problem of where to collect data so as to improve the parameter estimates as quickly as possible. We use an asymptotic Bayesian approximation for solving the two problems. The intrinsic dimensionality of polynomials and the use of the Mahalanobis distance are discussed     

Integration of Multiple Feature Groups and Multiple Views into a 3D Object Recognition System

This paper proposes two approaches for utilizing the information in multiple entity groups and multiple views to reduce the number of hypotheses passed to the verification stage in a model-based object recognition system employing invariant feature indexing (P. J. Flynn and A. K. Jain, CVGIP: Image Understand. 55(2), 1992, 119-129). The first approach is based on a majority voting scheme that keeps track of the number of consistent votes cast by prototype hypotheses for particular object models. The second approach examines the consistency of estimated object pose from multiple groups of entities (surfaces) in one or more views. A salient feature of our system and experiment design compared to most existing 3D object recognition systems is our use of a large object database and a large number of test images. Monte Carlo experiments employing 585 single-view synthetic range images and 117 pairs of synthetic range images with a large CAD-based 3D object database (P. J. Flynn and A. K. Jain, IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 1991, 114-132) show that a large number of hypotheses (about 60% for single views and 90% for multiple views on average) can be eliminated through use of these approaches. The techniques have also been tested on several real 3D objects sensed by a Technical Arts 100X range scanner to demonstrate a substantial improvement in recognition time.     

Point Signatures: A New Representation for 3D Object Recognition

Few systems capable of recognizing complex objects with free-form (sculptured) surfaces have been developed. The apparent lack of success is mainly due to the lack of a competent modelling scheme for representing such complex objects. In this paper, a new form of point representation for describing 3D free-form surfaces is proposed. This representation, which we call the point signature, serves to describe the structural neighbourhood of a point in a more complete manner than just using the 3D coordinates of the point. Being invariant to rotation and translation, the point signature can be used directly to hypothesize the correspondence to model points with similar signatures. Recognition is achieved by matching the signatures of data points representing the sensed surface to the signatures of data points representing the model surface.The use of point signatures is not restricted to the recognition of a single-object scene to a small library of models. Instead, it can be extended naturally to the recognition of scenes containing multiple partially-overlapping objects (which may also be juxtaposed with each other) against a large model library. No preliminary phase of segmenting the scene into the component objects is required. In searching for the appropriate candidate model, recognition need not proceed in a linear order which can become prohibitive for a large model library. For a given scene, signatures are extracted at arbitrarily spaced seed points. Each of these signatures is used to vote for models that contain points having similar signatures. Inappropriate models with low votes can be rejected while the remaining candidate models are ordered according to the votes they received. In this way, efficient verification of the hypothesized candidates can proceed by testing the most likely model first. Experiments using real data obtained from a range finder have shown fast recognition from a library of fifteen models whose complexities vary from that of simple piecewise quadric shapes to complicated face masks. Results from the recognition of both single-object and multiple-object scenes are presented.     

Three-dimensional model-based object recognition and segmentation in cluttered scenes

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.     

3-D object recognition using bipartite matching embedded indiscrete relaxation

The authors show how large efficiencies can be achieved in model-based 3-D vision by combining the notions of discrete relaxation and bipartite matching. The computational approach presented is capable of pruning large segments of search space-an indispensable step when the number of objects in the model library is large and when recognition of complex objects with a large number of surfaces is called for. Bipartite matching is used for quick wholesale rejection of inapplicable models and for the determination of compatibility of a scene surface with a potential model surface taking into account relational considerations. The time complexity function associated with those aspects of the procedure that are implemented via bipartite matching is provided. The algorithms do not take more than a couple of iterations, even for objects with more than 30 surfaces     

Feature space trajectory methods for active computer vision

We advance new active object recognition algorithms that classify rigid objects and estimate their pose from intensity images. Our algorithms automatically detect if the class or pose of an object is ambiguous in a given image, reposition the sensor as needed, and incorporate data from multiple object views in determining the final object class and pose estimate. A probabilistic feature space trajectory (FST) in a global eigenspace is used to represent 3D distorted views of an object and to estimate the class and pose of an input object. Confidence measures for the class and pose estimates, derived using the probabilistic FST object representation, determine when additional observations are required as well as where the sensor should be positioned to provide the most useful information. We demonstrate the ability to use FSTs constructed from images rendered from computer-aided design models to recognize real objects in real images and present test results for a set of metal machined parts.     

IVIS: An Integrated Volumetric Inspection System

A novel approach to automated visual inspection based on comparing a volumetric model of a reference object to a volumetric model of an actual object which is iteratively created from sensor data is presented. The use of volumetric models gives this approach a number of distinct advantages over more traditional surface-based methods. First, there is no need to identify "features," which is important when inspecting objects whose features are difficult to identify. Second, volumetric inspection lends itself naturally to multisensor applications. Finally, true 3-D comparisons of the reference and sensed objects can be easily carried out using volumetric models.     

A CAD Model Based System for Object Recognition

3D object recognition is a difficult and yet an important problem in computer vision. A 3D object recognition system has two major components, namely: an object modeller and a system that performs the matching of stored representations to those derived from the sensed image. The performance of systems wherein the construction of object models is done by training from one or more images of the objects, has not been very satisfactory. Although objects used in a robotic workcell or in assembly processes have been designed using a CAD system, the vision systems used for recognition of these objects are independent of the CAD database. This paper proposes a scheme for interfacing the CAD database of objects and the computer vision processes used for recognising these objects. CAD models of objects are processed to generate vision oriented features that appear in the different views of the object and the same features are extracted from images of the object to identify the object and its pose.     

用于遥感图像人造目标识别的三维建模方法研究

该文研究了用于遥感图像人造地物目标识别的三维建模方法,文中分析了识别任务的特点,比较了一般的建模方法,介绍了一种基于广义锥思想的几何表示方法,并利用面向对象的技术来表示模型内部数据及其操作。     

3D Free-Form Object Recognition Using Indexing by Contour Features

We address the problem of recognizing free-form 3D objects from a single 2D intensity image. A model-based solution within the alignment paradigm is presented which involves three major schemes—modeling, matching, and indexing. The modeling scheme constructs a set of model aspects which can predict the object contour as seen from any viewpoint. The matching scheme aligns the edgemap of a candidate model to the observed edgemap using an initial approximate pose. The major contribution of this paper involves the indexing scheme and its integration with modeling and matching to perform recognition. Indexing generates hypotheses specifying both candidate model aspects and approximate pose and scale. Hypotheses are ordered by likelihood based on prior knowledge of pre-stored models and the visual evidence from the observed objects. A prototype implementation has been tested in recognition and localization experiments with a database containing 658 model aspects from twenty 3D objects and eighty 2D objects. Bench tests and simulations show that many kinds of objects can be handled accurately and efficiently even in cluttered scenes. We conclude that the proposed recognition-by-alignment paradigm is a viable approach to many 3D object recognition problems.     

The cost of choosing the wrong model in object recognition by constrained search

Many current recognition systems use variations on constrained tree search to locate objects in cluttered environments. If the system is simply finding instances of an object known to be in the scene, then previous formal analysis has shown that the expected amount of search is quadratic in the number of model and data features when all the data is known to come from a single object, but is exponential when spurious data is included. If one can group the data into subsets likely to have come from a single object, then terminating the search once a “good enough” interpretation is found reduces the expected search to cubic. Without successful grouping, terminated search is still exponential. These results apply to finding instances of a known object in the data. What happens when the object is not present? In this article, we turn to the problem of selecting models from a library, and examine the combinatorial cost of determining that an incorrectly chosen candidate object is not present in the data. We show that the expected search is again exponential, implying that naive approaches to library indexing are likely to carry an expensive overhead, since an exponential amount of work is needed to weed out each incorrect model. The analytic results are shown to be in agreement with empirical data for cluttered object recognition.     

Constrained pose refinement of parametric objects

Pose refinement is an essential task for computer vision systems that require the calibration and verification of model and camera parameters. Typical domains include the real-time tracking of objects and verification in model-based recognition systems. A technique is presented for recovering model and camera parameters of 3D objects from a single two-dimensional image. This basic problem is further complicated by the incorporation of simple bounds on the model and camera parameters and linear constraints restricting some subset of object parameters to a specific relationship. It is demonstrated in this paper that this constrained pose refinement formulation is no more difficult than the original problem based on numerical analysis techniques, including active set methods and lagrange multiplier analysis. A number of bounded and linearly constrained parametric models are tested and convergence to proper values occurs from a wide range of initial error, utilizing minimal matching information (relative to the number of parameters and components). The ability to recover model parameters in a constrained search space will thus simplify associated object recognition problems.     

Recognizing objects with multiple configurations

Computer vision has been extensively adopted in many domains during the last three decades. One of the main goals of computer vision applications is to recognize objects. Generally, computers can successfully achieve object recognition by relying on a large quantity of data. In real world, some objects may own diverse configurations or/and be observed at various angles and positions, and the process of object recognition is denoted as recognizing objects in dynamic state. It is difficult to collect enough data to achieve the sorts of objects recognition. In order to resolve the problem, we propose a technique to achieve object recognition which is not only in static state where the objects do not own multiple configurations, but also in dynamic state. First, we apply an effective robust algorithm to obtain landmarks from objects in two dimensional images. With the algorithm, the number of landmarks from different objects can be appointed in advance. A set of landmarks as a point is projected into a pre-shape space and a shape space. Next, a method is proposed to create a surface among three basic data models in a pre-shape space. If basic data are too few to create a surface or a curve, a new basic data can be built from the basic data. Then, a series of new data models can be obtained from these basic data in a pre-shape space. Finally, object recognition can be achieved by using the new data models in shape space. We give some examples to show the algorithms are efficient not only for the objects with noises, but also for the ones with various configurations.     

Localizing overlapping parts by searching the interpretation tree

This paper discusses how local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degrees of positional freedom relative to the sensors. The approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of nonoverlapping parts previously described in [18] and [15].     

VC-Dimension Analysis of Object Recognition Tasks

We analyze the amount of data needed to carry out various model-based recognition tasks in the context of a probabilistic data collection model. We focus on objects that may be described as semi-algebraic subsets of a Euclidean space. This is a very rich class that includes polynomially described bodies, as well as polygonal objects, as special cases. The class of object transformations considered is wide, and includes perspective and affine transformations of 2D objects, and perspective projections of 3D objects.We derive upper bounds on the number of data features (associated with non-zero spatial error) which provably suffice for drawing reliable conclusions. Our bounds are based on a quantitative analysis of the complexity of the hypotheses class that one has to choose from. Our central tool is the VC-dimension, which is a well-studied parameter measuring the combinatorial complexity of families of sets. It turns out that these bounds grow linearly with the task complexity, measured via the VC-dimension of the class of objects one deals with. We show that this VC-dimension is at most logarithmic in the algebraic complexity of the objects and in the cardinality of the model library.Our approach borrows from computational learning theory. Both learning and recognition use evidence to infer hypotheses but as far as we know, their similarity was not exploited previously. We draw close relations between recognition tasks and a certain learnability framework and then apply basic techniques of learnability theory to derive our sample size upper bounds. We believe that other relations between learning procedures and visual tasks exist and hope that this work will trigger further fruitful study along these lines.