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In this work, we address the problem of simultaneous clasp and motion planning on unknown objects with holes. Clasping an object enables a rich set of activities such as dragging, toting, pulling and hauling which can be applied to both soft and rigid objects. To this end, we define a virtual linking measure which characterizes the spacial relation between the robot hand and object. The measure utilizes a set of closed curves arising from an approximately shortest basis of the object's first homology group. We define task spaces to perform collision-free motion planing with respect to multiple prioritized objectives using a sampling-based planing method. The approach is tested in simulation using different robot hands and various real-world objects. QC 20140318

The application of optical flow algorithms to guidance and navigation problems has gained considerable interest in recent years. This paper summarizes the results of a comparative study on the accuracy of nine different optical flow (OF) algorithms using videos that are captured from an on-board camera during the flight of an autonomous aircraft model. The comparison among the algorithms relies on two formulas that are used both to calculate the ideal OF generated by the motion of a rigid body in the camera field of view and to estimate the linear and angular velocity from the OF.

State estimation and control are intimately related processes in robot handling of flexible and articulated objects. While for rigid objects, we can generate a CAD model beforehand and a state estimation boils down to estimation of pose or velocity of the object, in case of flexible and articulated objects, such as a cloth, the representation of the object's state is heavily dependent on the task and execution. For example, when folding a cloth, the representation will mainly depend on the way the folding is executed. In this paper, we address the problem of learning a temporal object model from observations generated during task execution. We use the case of dynamic cloth folding as a proof-of-concept for our methodology. In cloth folding, the most important information is contained in the temporal structure of the data requiring appropriate representation of the observations, fast state estimation and a suitable prediction mechanism. Our approach is realized through efficient implementation of feature extraction and a generative process model, exploiting recent hardware advances in conjunction with principled probabilistic models. The model is capable of representing the temporal structure of the data and it is robust to noise in the observations. We present results exploiting our model to classify the success of a folding action.

Date of Conference: 26-29 Sept. 2010 Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object. © 2010 IEEE.

We present a novel GPU-based method for accelerating the visibility function computation of the lighting equation in dynamic scenes composed of rigid objects. The method pre-computes, for each object in the scene, the visibility and normal information, as seen from the environment, onto the bounding sphere surrounding the object and encodes it into maps. The visibility function is encoded by a four-dimensional visibility field that describes the distance of the object in each direction for all positional samples on a sphere around the object. In addition, the normal vectors of each object are computed and stored in corresponding fields for the same positional samples for use in the computation of reflection in ray-tracing. Thus we are able to speed up the calculation of most algorithms that trace rays to real-time frame rates. The pre-computation time of our method is relatively small. The space requirements amount to 1 byte per ray direction for the computation of ambient occlusion and soft shadows and 4 bytes per ray direction for the computation of reflection in ray-tracing. We present the acceleration results of our method and show its application to two different intersection intensive domains, ambient occlusion computation and stochastic ray tracing on the GPU. 131 138 Conference code: 89891 Cited By :1

Conference name: 13th International Conference, CAIP 2009 Date of Conference: September 2-4, 2009 Rigidity and reflectance are key object properties, important in their own rights, and they are key properties that stratify motion reconstruction algorithms. However, the inference of rigidity and reflectance are both difficult without additional information about the object's shape, the environment, or lighting. For humans, relative motions of object and observer provides rich information about object shape, rigidity, and reflectivity. We show that it is possible to detect rigid object motion for both specular and diffuse reflective surfaces using only optic flow, and that flow can distinguish specular and diffuse motion for rigid objects. Unlike nonrigid objects, optic flow fields for rigid moving surfaces are constrained by a global transformation, which can be detected using an optic flow matching procedure across time. In addition, using a Procrustes analysis of structure from motion reconstructed 3D points, we show how to classify specular from diffuse surfaces. © 2009 Springer Berlin Heidelberg.

Date of Conference: 26–30 April 2009 Conference Name: Digital Holography and Three-Dimensional Imaging, OSA 2009 A real-time color holographic video display system computes holograms from point cloud of a rigid object by using multi-GPU system and uses three different colored LEDs for reconstruction. Experimental results are satisfactory.

Date of Conference: 17-19 April 2006 Simulating every day phenomena such as fluid, rigid objects, or cloth and their interaction has been a challenge for the computer graphics community for decades. In this article techniques to model such interactions are explained briefly and some of the result of applying these tecniques are presented. © 2006 IEEE.

This paper describes a system for the animation of fracturing brittle objects. The system combines rigid body simulation methods with a constraint-based model to animate fracturing of arbitrary polyhedral shaped objects under impact. The objects are represented as sets of masses, where pairs of adjacent masses are connected via a distance-preserving linear constraint. Lagrange multipliers are used to compute the forces exerted by those constraints, where these forces determine how and where the object will break. However, a problem with existing systems is that the initial body models exhibit well-defined uniformity, which makes the generated animations unrealistic. This work introduces a method for generating more realistic cracks without any performance loss. This method is easy to implement and applicable on different models. Computer and Information Sciences - ISCIS 2005, 20th International Symposium, Istanbul, Turkey, October 26-28, 2005, Proceedings

Computer vision, or the application of computer-based image analysis and interpretation, is an emerging technology. Computer vision systems have been used in automated processing, analysis and understanding of images in many industrial applications. 3D object reconstruction and motion estimation present many interesting challenges in such applications. Over the last few decades, many good schemes have been developed to deal with these problems. In this thesis, a new scheme for estimating 3D profile and motion parameters of objects in an unstructured environment is presented. The scheme takes advantage of the morphology formulation at both the low-level image processing (image preprocessing) as well as high-level image processing (e.g., 3D object reconstruction and motion estimation). A new morphology-based transform is introduced to estimate motion parameters for both rigid and flexible objects. Furthermore, the thesis will discuss how the proposed scheme is extended so that it can deal with the 3D object surface reconstruction problem with limited surface measurements. A number of experiments are conducted to examine the proposed scheme and compare its performance to a commonly used technique for motion estimation. Based on the experimental results, the advantages and disadvantages of this scheme are discussed.

We give a complete classification of all algebras appearing as endomorphism algebras of maximal rigid objects in standard 2-Calabi-Yau categories of finite type. Such categories are equivalent to certain orbit categories of derived categories of Dynkin algebras. It turns out that with one exception, all the algebras that occur are $2$-Calabi-Yau-tilted, and therefore appear in an earlier classification by Bertani-{\O}kland and Oppermann. We explain this phenomenon by investigating the subcategories generated by rigid objects in standard 2-Calabi-Yau categories of finite type., Comment: 23 pages