Object Model Database Research Articles

AN APPROACH OF DOM RECORD MANAGEMENT COMPONENT UI CREATION

This paper presents an approach of Document Object Model database record management component user interface (UI) creation. Mainstream of UI development approaches concentrates in few directions, omitting holistic view on the fundamental database operations. The proposed solution uses method lay on of standard clauses adopted by the ECMA Script edition sixth and above. The program logic for the UI component was built with the intention - The existing functionality of such UI component to be extended and complemented.

Object-Level Semantic Map Construction for Dynamic Scenes

Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes.

Model-Driven Feedforward Prediction for Manipulation of Deformable Objects

Robotic manipulation of deformable objects is a difficult problem especially because of the complexity of the many different ways an object can deform. Searching such a high-dimensional state space makes it difficult to recognize, track, and manipulate deformable objects. In this paper, we introduce a predictive, model-driven approach to address this challenge, using a precomputed, simulated database of deformable object models. Mesh models of common deformable garments are simulated with the garments picked up in multiple different poses under gravity, and stored in a database for fast and efficient retrieval. To validate this approach, we developed a comprehensive pipeline for manipulating clothing as in a typical laundry task. First, the database is used for category and the pose estimation is used for a garment in an arbitrary position. A fully featured 3-D model of the garment is constructed in real time, and volumetric features are then used to obtain the most similar model in the database to predict the object category and pose. Second, the database can significantly benefit the manipulation of deformable objects via nonrigid registration, providing accurate correspondences between the reconstructed object model and the database models. Third, the accurate model simulation can also be used to optimize the trajectories for the manipulation of deformable objects, such as the folding of garments. Extensive experimental results are shown for the above tasks using a variety of different clothings. Note to Practitioners —This paper provides an open source, extensible, 3-D database for dissemination to the robotics and graphics communities. Model-driven methods are proliferating, and they need to be applied, tested, and validated in real environments. A key idea we have exploited is to have an innovative and novel use of simulation. This database will serve as infrastructure for developing advanced robotic machine learning algorithms. We want to address this machine learning idea ourselves, but we expect the dissemination of the database to other researchers with different agendas and task applications, which will bring wide progress in this area. Our proposed methods, as mentioned earlier, can be easily applied to interrelated areas. One example is that the 3-D shape-based matching algorithm can be used for other objects, such as bottles, papers, and food. After integrating with other robotic systems, the use of the robot can be easily extended to other tasks, such as making food, cleaning room, and fetching objects, to assist our daily life.

Planning High-Quality Grasps Using Mean Curvature Object Skeletons

In this letter, we present a grasp planner that integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identify object regions which are suitable for grasping. We show how this information can be used to derive different grasping strategies, which also allows to distinguish between precision and power grasps. Second, the local surface structure is investigated to construct feasible and robust grasping poses by aligning the hand according to the local object shape. We apply the approach to a wide variety of object models of the KIT and the YCB real-world object model databases and evaluate it with several robotic hands. The results show that the skeleton-based grasp planner is capable to generate high-quality grasps in an efficient manner. In addition, we evaluate how robust the planned grasps are against hand positioning errors as they occur in real-world applications due to perception and actuation inaccuracies. The evaluation shows that the majority of the generated grasps are of high quality since they can be successfully applied even when the hand is not exactly positioned.

Construction of a 3D object recognition and manipulation database from grasp demonstrations

Object recognition and manipulation are critical for enabling robots to operate in household environments. Many grasp planners can estimate grasps based on object shape, but they ignore key information about non-visual object characteristics. Object model databases can account for this information, but existing methods for database construction are time and resource intensive. We present an easy-to-use system for constructing object models for 3D object recognition and manipulation made possible by advances in web robotics. The database consists of point clouds generated using a novel iterative point cloud registration algorithm. The system requires no additional equipment beyond the robot, and non-expert users can demonstrate grasps through an intuitive web interface. We validate the system with data collected from both a crowdsourcing user study and expert demonstration. We show that the demonstration approach outperforms purely vision-based grasp planning approaches for a wide variety of object classes.

The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics

For the execution of object recognition, localization and manipulation tasks, most algorithms use object models. Most models are derived from, or consist of two-dimensional (2D) images and/or three-dimensional (3D) geometric data. The system described in this article was constructed specifically for the generation of such model data. It allows 2D image and 3D geometric data of everyday objects be obtained semi-automatically. The calibration provided allows 2D data to be related to 3D data. Through the use of high-quality sensors, high-accuracy data is achieved. So far over 100 objects have been digitized using this system and the data has been successfully used in several international research projects. All of the models are freely available on the web via a front-end that allows preview and filtering of the data.

Combined 2D–3D categorization and classification for multimodal perception systems

In this article we describe an object perception system for autonomous robots performing everyday manipulation tasks in kitchen environments. The perception system gains its strengths by exploiting that the robots are to perform the same kinds of tasks with the same objects over and over again. It does so by learning the object representations necessary for the recognition and reconstruction in the context of pick-and-place tasks. The system employs a library of specialized perception routines that solve different, well-defined perceptual sub-tasks and can be combined into composite perceptual activities including the construction of an object model database, multimodal object classification, and object model reconstruction for grasping. We evaluate the effectiveness of our methods, and give examples of application scenarios using our personal robotic assistants acting in a human living environment.

An experimental study of four variants of pose clustering from dense range data

Parameter clustering is a robust estimation technique based on location statistics in a parameter space where parameter samples are computed from data samples. This article investigates parameter clustering as a global estimator of object pose or rigid motion from dense range data without knowing correspondences between data points. Four variants of the algorithm are quantitatively compared regarding estimation accuracy and robustness: sampling poses from data points or from points with surface normals derived from them, each combined with clustering poses in the canonical or consistent parameter space, as defined in Hillenbrand (2007) [1]. An extensive test data set is employed: synthetic data generated from a public database of three-dimensional object models through various levels of corruption of their geometric representation; real range data from a public database of models and cluttered scenes. It turns out that sampling raw data points and clustering in the consistent parameter space yields the estimator most robust to data corruption. For data of sufficient quality, however, sampling points with normals is more efficient; this is most evident when detecting objects in cluttered scenes. Moreover, the consistent parameter space is always preferable to the canonical parameter space for clustering.

Semantic video database system with semi-automatic secondary-content generation capability

A semantic video database system, based on an interactive approach that maps low-level features to high-level concepts, is proposed. A database of ontological semantic object models allows the user to get information about specific semantic objects, such as certain actors or other features of a TV drama. The system searches the database for key frames within the video to detect similarities in detailed or "low-level" features, such as the color, area, and position of a specific part of the frame. Since image recognition techniques are limited in their ability to fully identify and compare images, we propose an additional function in which a coarse model is used to recover a greater number of similar key frames, thus providing more relevant results. From these results, the content provider can select relevant key frames interactively; the matched objects in them are then automatically annotated according to descriptions that are added into the model by content provider. Therefore, more complex content can be generated with greater accuracy by using a combination of application-oriented operations. The system has high potential for use in object-based interactive multimedia applications. We also present an object-based video content generation application called the Drama Characters' Popularity Voting System.

FREE-FORM 3D OBJECT RECOGNITION IN RANGE DATA USING WEAK CORRESPONDENCE BETWEEN LOCAL FEATURES

Model-Based 3D object recognition systems have a variety of potential applications, but widespread use of such systems has not occurred, due to a number of factors including the representational limitations of models. One historical limitation is the discriminatory representation of free-form objects. The system described in this paper recognizes free-form objects in dense range data acquired by a structured light rangefinder. Images and object models are represented as a network of salient segments which are then brought into correspondence until a reliable pose estimate is available. Experiments with a database of images and object models highlight the contributions of this system.

A prototype indexing approach to 2-D object description and recognition

This paper introduces an indexing approach to 2-D object description and recognition in the presence of rotation, translation, scale, and partial occlusion of objects. The scheme is based on the polygonal approximations of object boundaries. To obtain stable features to represent 2-D objects, three polygonal approximations are computed for the objects using different line fitting tolerances. Local structural features of objects are extracted, which consist of line and circular arc elements. The indexing entries computed based on the initial features are employed for fast access to the object-model database and generating hypotheses. A dynamic feature-matching method is designed to implement final-shape matching by evaluating and verifying the hypotheses. The system is tested with both manufactured workpieces and prototype-test-objects, and the experimental results are presented.

A system for model-based object recognition in perspective aerial images

Recognition of objects in complex, perspective aerial imagery is difficult because of occlusion, shadow, clutter and various forms of image degradation. This paper presents a system for aircraft recognition under real-world conditions. The particular approach is based on the use of a hierarchical database of object models and involves three key processes: (a) The qualitative object recognition process performs heterogeneous model-based symbolic feature extraction and generic object recognition; (b) The refocused matching and evaluation process refines the extracted features for more specific classification with input from (a); and (c) The primitive feature extraction process regulates the extracted features based on their saliency and interacts with (a) and (b). Experimental results showing the qualitative recognition of aircraft in perspective, aerial images are presented.

ORACLE: An Integrated Learning Approach for Object Recognition

Model-based object recognition has become a popular paradigm in computer vision research. In most of the current model-based vision systems, the object models used for recognition are generally a priori given (e.g. obtained using a CAD model). For many object recognition applications, it is not realistic to utilize a fixed object model database with static model features. Rather, it is desirable to have a recognition system capable of performing automated object model acquisition and refinement. In order to achieve these capabilities, we have developed a system called ORACLE: Object Recognition Accomplished through Consolidated Learning Expertise. It uses two machine learning techniques known as Explanation-Based Learning (EBL) and Structured Conceptual Clustering (SCC) combined in a synergistic manner. As compared to systems which learn from numerous positive and negative examples, EBL allows the generalization of object model descriptions from a single example. Using these generalized descriptions, SCC constructs an efficient classification tree which is incremently built and modified over time. Learning from experience is used to dynamically update the specific feature values of each object. These capabilities provide a dynamic object model database which allows the system to exhibit improved performance over time. We provide an overview of the ORACLE system and present experimental results using a database of thirty aircraft models.

Data- and Model-Driven Multiresolution Processing

We introduce a technique formultiresolution processingwhich elegantly fits in our framework for visual recognition, described in earlier papers. The input is processedsimultaneouslyat a coarse resolution throughout the image and at finer resolution within a small window (fovea). We introduce an approach for controlling the movement of the high-resolution window which allows for both data- and model-driven selection of fixation points. Three fixation modes have been implemented, one based on large unexplained areas in the data, one on conflicts in the object-model database, and one on a 2D “space filling” algorithm. We argue that this kind of multiresolution processing is not only useful in limiting the computational time, as has been widely recognized, but also can be a deciding factor in making the entire vision problem a tractable and stable one. To demonstrate the approach, we introduce a class of3D surface texturesas a feature for recognition in our system. Surface texture recognition typically requires higher-resolution processing than that required for the extraction of the underlying surface. As examples, surface texture is used to discriminate between a ping-pong ball and a golf ball, and “curve texture” is used to recognize different types of gears. Other experimental results also are included to show the advantages and the implications of our approach.

A SURFACE FEATURE ATTRIBUTED HYPERGRAPH REPRESENTATION FOR 3-D OBJECT RECOGNITION

A surface feature hypergraph (SFAHG) representation is proposed for the recognition and localization of three-dimensional objects. The hypergraph representation is shown to be viewpoint independent thus resulting in substantial savings in terms of memory for the object model database. The resulting hypergraph matching algorithm integrates both, relational and the rigid pose constraint in a consistent unified manner. The matching algorithm is also shown to have a polynomial order of complexity even in multiple-object scenes with instances of objects partially occluding each other. An algorithm for incrementally constructing the hypergraph representation of an object model from range images of the object taken from different viewpoints is also presented. The hypergraph matching and the hypergraph construction algorithms are shown to be capable of correcting errors in the initial segmentation of the range image. The hypergraph construction algorithm and the matching algorithm are tested on range images of scenes containing multiple three-dimensional objects with partial occlusion.

Verification of Boundary Representations of Solid Models

Verification of polyhedral boundary representations (B-reps) of solid models through the use of an algorithm is addressed here. The validity conditions for B-rep models are presented in a format which leads directly to a set of verification algorithms. The validity verification algorithms are intended for design automation through execution after each solid modeling operation, after localized geometry modification, on imported object model databases, prior to storage of object models, or prior to execution of an application program on the solid model.

On geometric hashing and the generalized Hough transform

The generalized Hough transform and geometric hashing are two contemporary paradigms for model-based object recognition. Both schemes simultaneously find instances of objects in a scene and determine the location and orientation of these instances. The methods encode the models for the objects in a similar fashion and object recognition is achieved by image features "voting" for object models. For both schemes, the object recognition time is largely independent of the number of objects that are encoded in the object-model database. This paper puts the two schemes in perspective and examines differences and similarities. The authors also study object representation techniques and discuss how object representations are used for object recognition and position estimation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Model-Based Classification of Quadric Surfaces

Model-based 3D object recognition systems using range imagery typically employ entirely data-driven procedures for segmentation and surface classification. However, some recognition environments may contain only objects whose surface types and parameters are known a priori and can therefore be exploited by the early-processing steps used in the recognition system. We propose a new suite of model-driven techniques for identification of quadric surfaces (cones, cylinders, and spheres) in segmented range imagery. The methods employ surface positions and surface normal estimates in combination with the known parameters of surfaces in a database of object models. Second-derivative quantities (i.e., surface curvatures) are not used. The free parameters of cylinders and spheres are accumulated using a Hough transform, and free parameters of cones are estimated using a regression procedure. Experiments are presented for numerous scenes of both real and synthetic objects including part jumbles, objects in many poses, objects containing concave and convex surfaces, and noiseless and noisy synthetic range images of objects. Our experimental results show that the proposed surface classification methods can accurately recover surface parameters from both synthetic and real images, making them viable for environments with partial knowledge of surface type and parameters.