Structural indexing: efficient 3-D object recognition

The authors present an approach for the recognition of multiple 3-D object models from three 3-D scene data. The approach uses two different types of primitives for matching: small surface patches, where differential properties can be reliably computed, and lines corresponding to depth or orientation discontinuities. These are represented by splashes and 3-D curves, respectively. It is shown how both of these primitives can be encoded by a set of super segments, consisting of connected linear segments. These super segments are entered into a table and provide the essential mechanism for fast retrieval and matching. The issues of robustness and stability of the features are addressed in detail. The acquisition of the 3-D models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3-D curves. The authors present results with the current system (3-D object recognition based on super segments) and discuss further extensions     

一种基于形状的三维医学图像插值算法

三维医学图像的层间插值是实现医学体视化的一个重要步骤;传统的直接灰度插值方法只适用于层间距小或形状差别不大的断层扫描图像,否则会产生严重的轮廓模糊现象;文中的三维插值算法通过对轮廓进行多边形近似,将形状和灰度信息较好地结合起来,充分利用了图像的形状信息,从而较好地避免了大层间距和形状差别较大时的轮廓重影问题,实验结果证明了其有效性.     

Index set: A practical indexing scheme for object database systems

Efficient indexing in a class hierarchy is essential for the achievement of high performance in query evaluation for object database management systems. In this paper, we present a practical indexing scheme, index set, which provides good index configuration for any real database environment. The proposed scheme considers the distribution of key values, as well as query patterns such as query weight on each class. The index set can easily be applied to any database system, since it uses the well-known B⁺-tree structure. We develop a cost model and, through experiments, demonstrate the performance of the proposed scheme over various class hierarchies.     

Point fingerprint: a new 3-D object representation scheme

This paper proposes a new, efficient surface representation method for surface matching. A feature carrier for a surface point, which is a set of two-dimensional (2-D) contours that are the projections of geodesic circles on the tangent plane, is generated. The carrier is named point fingerprint because its pattern is similar to human fingerprints and plays a role in discriminating surface points. Corresponding points on surfaces from different views are found by comparing their fingerprints. The point fingerprint is able to carry curvature, color, and other information which can improve matching accuracy, and the matching process is faster than 2-D image comparison. A novel candidate point selection method based on the fingerprint irregularity is introduced. Point fingerprint is successfully applied to pose estimation of real range data.     

2-D invariant object recognition using distributed associativememory

Complex-log conformal mapping is combined with a distributed associative memory to create a system that recognizes objects regardless of changes in rotation or scale. Information recalled from the memorized database is used to classify an object, reconstruct the memorized version of the object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments using real gray-scale images are presented to show the feasibility of the approach     

Probabilistic indexing for object recognition

Recent papers have indicated that indexing is a promising approach to fast model-based object recognition because it allows most of the possible matches between sets of image features and sets of model features to be quickly eliminated from consideration. This correspondence describes a system that is capable of indexing using sets of three points undergoing 3D transformations and projection by taking advantage of the probabilistic peaking effect. To be able to index using sets of three points, we must allow false negatives. These are overcome by ensuring that we examine several correct hypotheses. The use of these techniques to speed up the alignment method is described. Results are given on real and synthetic data     

Interpolation techniques for 3-D object generation

The technique of interpolation (or blending) and its generalization is considered in this paper. A unifying model for generating 3-D objects using this technique is presented. Various factors which affect the shape of generated objects are identified. Several illustrative examples of 3-D objects generated by this technique, using linear as well as non-linear interpolation, are presented. Although many of the objects have complex shapes, they are represented by simple closed form mathematical equations.     

Convolutional deep learning for 3D object retrieval

In recent years, with the development of 3D technologies, 3D model retrieval has become a hot topic. The key point of 3D model retrieval is to extract robust feature for 3D model representation. In order to improve the effectiveness of method on 3D model retrieval, this paper proposes a feature extraction model based on convolutional neural networks (CNN). First, we extract a set of 2D images from 3D model to represent each 3D object. SIFT detector is utilized to detect interesting points from each 2D image and extract interesting patches to represent local information of each 3D model. X-means is leveraged to generate the CNN filters. Second, a single CNN layer learns low-level features which are then given as inputs to multiple recursive neural networks (RNN) in order to compose higher order features. RNNs can generate the final feature for 2D image representation. Finally, nearest neighbor is used to compute the similarity between different 3D models in order to handle the retrieval problem. Extensive comparison experiments were on the popular ETH and MV-RED 3D model datasets. The results demonstrate the superiority of the proposed method.     

View-wised discriminative ranking for 3D object retrieval

In this paper, we propose a new framework which can capture the latent relative information within the multiple views of 3D model, named View-wised Discriminative Ranking(VDR). Different to existing view-based methods which treat the multiple views as the independent information, we want to model the relative information within multiple views. By placing the views of model in certain order, we learn the parameters of ranking function as a new robust model representation. We evaluate our proposal on several challenging datasets for 3D retrieval and the comparison experiments demonstrate the superiority of the proposed method in both retrieval accuracy and efficiency.     

Off-the-shelf CNN features for 3D object retrieval

Multimedia Tools and Applications - Effective feature representation is crucial to view-based 3D object retrieval (V3OR). Most previous works employed hand-crafted features to represent the views...     

Translation invariant data-structure for 3-D binary images

In this paper we described a data structure suitable for three dimensional images. The basic property of this data structure is that it is translation invariant and can be tailored in such a way that it can have most of the desired properties required for binary image representation. The optimality of performance of this data structure over the conventional oct-trees for 3-D images has been investigated.     

Experiments with discrimination-tree indexing and path indexing for term retrieval

This article addresses the problem of indexing and retrieving first-order predicate calculus terms in the context of automated deduction programs. The four retrieval operations of concern are to find variants, generalizations, instances, and terms that unify with a given term. Discrimination-tree indexing is reviewed, and several variations are presented. The path-indexing method is also reviewed. Experiments were conducted on large sets of terms to determine how the properties of the terms affect the performance of the two indexing methods. Results of the experiments are presented.This was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

Color indexing for efficient image retrieval

Content based image retrieval is an active area of research. Many approaches have been proposed to retrieve images based on matching of some features derived from the image content. Color is an important feature of image content. The problem with many traditional matching-based retrieval methods is that the search time for retrieving similar images for a given query image increases linearly with the size of the image database. We present an efficient color indexing scheme for similarity-based retrieval which has a search time that increases logarithmically with the database size.In our approach, the color features are extracted automatically using a color clustering algorithm. Then the cluster centroids are used as representatives of the images in 3-dimensional color space and are indexed using a spatial indexing method that usesR-tree. The worst case search time complexity of this approach isOn _q log(N^* n_avg)), whereN is the number of images in the database, andn _q andn _avg are the number of colors in the query image and the average number of colors per image in the database respectively. We present the experimental results for the proposed approach on two databases consisting of 337 Trademark images and 200 Flag images.     

Part I: Modeling image curves using invariant 3-D object curvemodels-a path to 3-D recognition and shape estimation from imagecontours

This paper and its companion are concerned with the problems of 3-D object recognition and shape estimation from image curves using a 3-D object curve model that is invariant to affine transformation onto the image space, and a binocular stereo imaging system. The objects of interest here are the ones that have markings (e.g., characters, letters, special drawings and symbols, etc.) on their surfaces. The 3-D curves on the object are modeled as B-splines, which are characterized by a set of parameters (the control points) from which the 3-D curve can be totally generated. The B-splines are invariant under affine transformations. That means that the affine projected object curve onto the image space is a B-spline whose control points are related to the object control points through the affine transformation. Part I deals with issues relating to the curve modeling process. In particular, the authors address the problems of estimating the control points from the data curve, and of deciding on the “best” order B-spline and the “best” number of control points to be used to model the image or object curve(s). A minimum mean-square error (mmse) estimation technique which is invariant to affine transformations is presented as a noniterative, simple, and fast approach for control point estimation. The “best” B-spline is decided upon using a Bayesian selection rule. Finally, we present a matching algorithm that allocates a sample curve to one of p prototype curves when the sample curve is an a priori unknown affine transformation of one of the prototype curves stored in the data base. The approach is tried on a variety of images of real objects     

An indexing scheme for energy-efficient processing of content-based retrieval queries on a wireless data stream

Wireless data broadcasting is a popular data delivery approach in mobile computing environments, where the broadcasting servers usually adopt indexing schemes for mobile clients to energy-efficiently access data on a wireless broadcast stream. However, conventional indexing schemes use primary key attribute values to construct tree structures. Therefore, these schemes do not support content-based retrieval queries such as partial-match queries and range-queries. This paper proposes an indexing method that supports content-based retrieval queries on a wireless data stream. The method uses a tree-structured index, called B2V-Tree, which is composed of bit-vectors that are generated from data records through multi-attribute hashing. Through analysis and experiments, the effectiveness of the proposed method is shown.     

Recognizing large isolated 3-D objects through next view planning using inner camera invariants.

Most model-based three-dimensional (3-D) object recognition systems use information from a single view of an object. However, a single view may not contain sufficient features to recognize it unambiguously. Further, two objects may have all views in common with respect to a given feature set, and may be distinguished only through a sequence of views. A further complication arises when in an image, we do not have a complete view of an object. This paper presents a new online scheme for the recognition and pose estimation of a large isolated 3-D object, which may not entirely fit in a camera's field of view. We consider an uncalibrated projective camera, and consider the case when the internal parameters of the camera may be varied either unintentionally, or on purpose. The scheme uses a probabilistic reasoning framework for recognition and next-view planning. We show results of successful recognition and pose estimation even in cases of a high degree of interpretation ambiguity associated with the initial view.