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RELATIONAL MOdEL CONSTRUCTION ANd 3d object recognition FROM SINGLE 2d MONOCHROMATIC IMAGE

IMAGE ANd VISION COMPUTING 1992年第5期10卷 313-318页

作者： ZHANG, S SULLIVAN, Gd BAKER, Kd Intelligent Systems Group University of Reading Whiteknights Reading RG6 2AY UK

This paper reports a method for automatically constructing a relational model of a rigid 3d object to represent view-independent relations among its component parts, and of such using a model to recognize the object from single monochromatic images. The relational model is a graph associated with procedural constraints. It is constructed by a statistical analysis of images generated by projecting a CAd model of the object from a set of viewpoints on the Gaussian viewsphere. object recognition is achieved by a hypothesis-and-verification process. Extended hypotheses are generated by aggregating image features which satisfy the view-independent constraints. These hypotheses are then verified by projective inversion and 3d grouping to achieve object recognition. The approach is illustrated by means of the recognition of a hatchback model car. The method can readily be adapted to the recognition of any object defined geometrically.

关键词： 3d object recognition MOdEL CONSTRUCTION VIEW INdEPENdENT RELATIONAL MOdEL

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Efficient 3d object recognition via geometric information preservation

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PATTERN recognition 2019年 92卷 135-145页

作者： Liu, Hongsen Cong, Yang Yang, Chenguang Tang, Yandong Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110016 Liaoning Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England

Accurate 3d object recognition and 6-dOF pose estimation have been pervasively applied to a variety of applications, such as unmanned warehouse, cooperative robots, and manufacturing industry. How to extract a robust and representative feature from the point clouds is an inevitable and important issue. In this paper, an unsupervised feature learning network is introduced to extract 3d keypoint features from point clouds directly, rather than transforming point clouds to voxel grids or projected RGB images, which saves computational time while preserving the object geometric information as well. Specifically, the proposed network features in a stacked point feature encoder, which can stack the local discriminative features within its neighborhoods to the original point-wise feature counterparts. The main framework consists of both offline training phase and online testing phase. In the offline training phase, the stacked point feature encoder is trained first and then generate feature database of all keypoints, which are sampled from synthetic point clouds of multiple model views. In the online testing phase, each feature extracted from the unknown testing scene is matched among the database by using the K-d tree voting strategy. Afterwards, the matching results are achieved by using the hypothesis & verification strategy. The proposed method is extensively evaluated on four public datasets and the results show that ours deliver comparable or even superior performances than the state-of-the-arts in terms of F1-score, Average of the 3d distance (Add) and recognition rate. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Stacked 3d feature encoder 3d object recognition 6-dOF pose estimation Geometric information preservation

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GOOd: A global orthographic object descriptor for 3d object recognition and manipulation

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PATTERN recognition LETTERS 2016年第Part3期83卷 312-320页

作者： Kasaei, S. Hamidreza Tome, Ana Maria Lopes, Luis Seabra Oliveira, Miguel Univ Aveiro IEETA P-3810193 Aveiro Portugal Univ Aveiro Dept Elect Telecomunicacoes & Informat Aveiro Portugal Inst Engn Sistemas & Comp Tecnol & Ciencia R Dr Roberto Frias 465 P-4200 Oporto Portugal

object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure robustness, a global object descriptor must be computed based on a unique and repeatable object reference frame. Moreover, the descriptor should contain enough information enabling to recognize the same or similar objects seen from different perspectives. This paper presents a new object descriptor named Global Orthographic object descriptor (GOOd) designed to be robust, descriptive and efficient to compute and use. We propose a novel sign disambiguation method, for computing a unique reference frame from the eigenvectors obtained through Principal Component Analysis of the point cloud of the target object view captured by a 3d sensor. Three principal orthographic projections and their distribution matrices are computed by exploiting the object reference frame. The descriptor is finally obtained by concatenating the distribution matrices in a sequence determined by entropy and variance features of the projections. Experimental results show that the overall classification performance obtained with GOOd is comparable to the best performances obtained with the state-of-the-art descriptors. Concerning memory and computation time, GOOd clearly outperforms the other descriptors. Therefore, GOOd is especially suited for real-time applications. The estimated object's pose is precise enough for real-time object manipulation tasks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： 3d object recognition object Perception Orthographic projection

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A Hidden Markov Model approach for appearance-based 3d object recognition

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PATTERN recognition LETTERS 2005年第16期26卷 2588-2599页

作者： Bicego, M Castellani, U Murino, V Univ Verona Dipartimento Informat I-37134 Verona Italy

In this paper, a new appearance-based 3d object classification method is proposed based on the Hidden Markov Model (HMM) approach. Hidden Markov Models are a widely used methodology for sequential data modelling, of growing importance in the last years. In the proposed approach, each view is subdivided in regular, partially overlapped sub-images, and wavelet coefficients are computed for each window. These coefficients are then arranged in a sequential fashion to compose a sequence vector, which is used to train a HMM, paying particular attention to the model selection issue and to the training procedure initialization. A thorough experimental evaluation on a standard database has shown promising results, also in presence of image distortions and occlusions, the latter representing one of the most severe problems of the recognition methods. This analysis suggests that the proposed approach represents an interesting alternative to classic appearance-based methods to 3d object classification. (c) 2005 Elsevier B.V. All rights reserved.

关键词： 3d object recognition Hidden Markov Models model selection shape occlusion appearance-based recognition

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Point signatures: A new representation for 3d object recognition

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INTERNATIONAL JOURNAL OF COMPUTER VISION 1997年第1期25卷 63-85页

作者： Chua, CS Jarvis, R MONASH UNIV DEPT ELECT & COMP SYST ENGN INTELLIGENT ROBOT RES CTR CLAYTON VIC 3168 AUSTRALIA

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

关键词： 3d object recognition model indexing feature extraction free-form surface registration pose estimation

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Interactive Open-Ended Learning for 3d object recognition: An Approach and Experiments

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2015年第3-4期80卷 537-553页

作者： Kasaei, S. Hamidreza Oliveira, Miguel Lim, Gi Hyun Lopes, Luis Seabra Tome, Ana Maria Univ Aveiro Inst Engn Elect & Telemat Aveiro Aveiro Portugal Univ Aveiro Dept Elect Telecomunicacoes & Informat Aveiro Portugal

3d object detection and recognition is increasingly used for manipulation and navigation tasks in service robots. It involves segmenting the objects present in a scene, estimating a feature descriptor for the object view and, finally, recognizing the object view by comparing it to the known object categories. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, "open-ended" implies that the set of object categories to be learned is not known in advance. The training instances are extracted from on-line experiences of a robot, and thus become gradually available over time, rather than at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3d scenes in an open-ended manner? (2) How to acquire and use high-level knowledge obtained from the interaction with human users, namely when they provide category labels, in order to improve the system performance? This approach starts with a pre-processing step to remove irrelevant data and prepare a suitable point cloud for the subsequent processing. Clustering is then applied to detect object candidates, and object views are described based on a 3d shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to predict the categories of the detected objects. A leave-one-out cross validation algorithm is used to compute precision and recall, in a classical off-line evaluation setting, for different system parameters. Also, an on-line evaluation protocol is used to assess the performance of the system in an open-ended setting. Results show that the proposed system is able to interact with human users, learning new object categories continuously over time.

关键词： Open-ended learning 3d object recognition Spin-image descriptor Autonomous robots

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Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3d object recognition

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IEEE TRANSACTIONS ON PATTERN ANALYSIS ANd MACHINE INTELLIGENCE 2012年第10期34卷 1902-1914页

作者： Ulrich, Markus Wiedemann, Christian Steger, Carsten MVTec Software GmbH D-81675 Munich Germany

This paper describes an approach for recognizing instances of a 3d object in a single camera image and for determining their 3d poses. A hierarchical model is generated solely based on the geometry information of a 3d CAd model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3d pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.

关键词： 3d object recognition machine vision aspect graphs similarity measures hierarchical models robotics industrial automation image generation projections shape feature measurement least squares methods perspective

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3d object recognition Using Multiple Features and Neural Network

3D Object Recognition Using Multiple Features and Neural Net...

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IEEE International Conference on Cybernetic Intelligent Systems (CIS 2008)

作者： Sheng, Xu Qi-Cong, Peng Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 610054 Peoples R China

ISBN: (纸本)9781424416738

To improve the performance of view-based three-dimensional object recognition system, we propose to extract multiple features from the 2d images of 3d objects, including texture characteristics, color moments, Hu's moment invariants, and affine moment invariants. Texture characteristics and color moments are used to distinguish objects of similar shape and different appearance. Hu's moment invariants have the invariance properties under rotation, scale and translation, and affine moment invariants have the invariance properties under affine transformation for the 3d objects in images. All these characteristics compose a 1-dimensional feature vector of 23 components for each 2d image of 3d objects, and then they are presented to a BP neural network for training. The trained BP network can be used to recognize 3d objects when provided the feature vectors of unseen views. We assessed our method based on both the original and noise corrupted COIL-100 3d objects dataset and achieved 100% correct rate of recognition when training views were presented every 10 degrees.

关键词： 3d object recognition texture analysis color moments Hu's moment invariants Affine moment invariants BP Neural Network

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HOUGH TECHNIQUE FOR 3d object recognition

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IEE PROCEEdINGS-E COMPUTERS ANd dIGITAL TECHNIQUES 1989年第6期136卷 565-568页

作者： TSUI, HT CHAN, CK Department of Electronics Chinese University of Hong Kong Shatin Hong Kong School of Electrical and Electronic System Engineering Queensland University of Technology Brisbane Australia

Advances in imaging techniques make it efficient to extract information such as the length and orientation of a straight edge in 3d space. An algorithm using the Hough technique is proposed for the recognition of objects which can be represented or characterised by a set of vertices and straight lines. Assuming the scale is known a priori, the orientation and translation of an object are determined sequentially in two stages. The method is robust and can handle objects with curved or irregularly shaped surfaces, and surface reconstruction is not required.

关键词： translation length straight edge curved surfaces irregularly shaped surfaces pattern recognition 3d space Hough technique orientation Optical information, image and video signal processing Pattern recognition 3d object recognition Computer vision and image processing techniques picture processing

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Robust and Fast Hypothesis Verification in 3d object recognition

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PATTERN recognition ANd IMAGE ANALYSIS 2020年第4期30卷 702-710页

作者： Zheng, Yi Lu Wang, Xiao Fei Song, Peng Xu, Yin Long Univ Sci & Technol China Hefei 230026 Peoples R China Southwest Univ Sci & Technol Mianyang 621010 Sichuan Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore

3d object recognition requires generating and verifying hypothesis, where a hypothesis means a hypothesized object together with a hypothesized pose of the object in the scene. Hypothesis verification is typically achieved by transforming the hypothesized object model into the scene based on its pose, estimating the overlap between the transformed model and the scene, and accepting the hypothesis if the overlap area is sufficiently large. This paper develops a robust and fast hypothesis verification approach to improve the recognition accuracy without slowing down its speed. Our key idea is to identify corresponding points in the overlap between a hypothesized model and the scene using both point position and normal, and to speed up the identification of corresponding points by utilizing a signed distance transform map defined on the model's voxelization. Moreover, we estimate overlap area from the identified corresponding points more accurately based on an idea of area-weighted vertices. Experiments on publicly available datasets demonstrate the robustness and efficiency of our verification approach.

关键词： hypothesis verification 3d object recognition distance transform map area-weighted vertices

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