The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The paper presents a model and an algorithm for recognizing and handling articulated objects using industrial robots. The model is based on the skeleton of the object and it is used for recognition and to associate multiple grasping positions for robot handling. The object skeleton is a shape descriptor which preserves the topology of the object, even if the shape is changing. The model can associate...
Used to describe the propagation of the electrical potential in heterogeneous cardiac tissue, the FitzHugh-Nagumo monodomain model has attracted numerous attentions to be analyzed. Due to the irregular profile of the cardiac tissue, there has been limitations for the numerical solution on the considered regular domains or approximate irregular domains, where the analytic solution could not be obtained...
The motion performance of piezoelectric actuator is restricted by its hysteresis nonlinearity in the application of precision positioning. A novel hysteresis modeling method based on Bouc-Wen model is proposed to describe the asymmetric hysteresis nonlinearity of piezoelectric actuator. Considering the fact that the classical Bouc-Wen model is only a symmetric model, the proposed model is developed...
Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far...
Shape models provide a compact parameterization of a class of shapes, and have been shown to be important to a variety of vision problems, including object detection, tracking, and image segmentation. Learning generative shape models from grid-structured representations, aka silhouettes, is usually hindered by (1) data likelihoods with intractable marginals and posteriors, (2) high-dimensional shape...
There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we...
The safety and reliability of roller bearing always have significant importance in rotating machinery. It is needful to build an efficient and excellent accuracy method to monitoring and diagnosis the baring failure. A novel method is presented in this paper to classify the fault feature by wavelet function and extreme learning machine(ELM) that take into account the high accuracy and efficient. The...
Pre-learnt subspace methods, e.g., 3DMMs, are significant exploration for the synthesis of 3D faces by assuming that faces are in a linear class. However, the human face is in a nonlinear manifold, and a new test are always not in the pre-learnt subspace accurately because of the disparity brought by ethnicity, age, gender, etc. In the paper, we propose a parametric T-spline morphable model (T-splineMM)...
We investigate the problem of estimating the dense 3D shape of an object, given a set of 2D landmarks and silhouette in a single image. An obvious prior to employ in such a problem is a dictionary of dense CAD models. Employing a sufficiently large enough dictionary of CAD models, however, is in general computationally infeasible. A common strategy in dictionary learning to encourage generalization...
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground...
We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matching to the image. By penalising the distance and the relative rotation between neighbouring triangles our matching compromises between the image and the...
We present a method for the fast 3D face reconstruction of people wearing glasses. Our method explicitly and robustly models the case in which a face to be reconstructed is partially occluded by glasses. We propose a simple and generic model for glasses that copes with a wide variety of different shapes, colors and styles, without the need for any database or learning. Our algorithm is simple, fast...
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits in-the-wild. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently...
Interest point detection is one of the key technologies in image processing and target recognition. This paper presents a new method for detecting interest points in digital images and computer vision problems based on complex network theory. We associate a directed and weighted complex network model to each image and then we propose three different algorithms to locate these key points based on three...
Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories...
In this paper, a novel image moment-based model for extended object shape estimation and tracking is presented. A method to represent and estimate an elliptical shape using its image moments is first developed. The model of representing the shape of an object falls under the category of random hypersurface model (RHM) for extended object tracking. The moments are estimated using an unscented Kalman...
We present a novel global registration method for deformable objects captured using a single RGB-D camera. Our algorithm allows objects to undergo large non-rigid deformations, and achieves high quality results without constraining the actor's pose or camera motion. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure...
In this paper, we present a procedural 2.5D water flow simulation technique to animate pictures. For a given picture, we convert it into a 2.5D structure and then perform water simulation for the 2.5D structure (like a shallow-relief sculpture). The proposed approach is divided into three phases: image processing, water simulation and rendering. First, we extract the objects in the picture and compute...
We report on a novel low-computation object grasping method that can classify complex objects into primitive shapes and then select the object grasping posture based on predefined grasping postures associated with the approximated primitive shapes. In this approach, the object is not precisely modeled, and the grasping posture is selected from a small number of candidates without massive search; thus,...
Tangible physical maps couple physical landscape model with digital information and can become an invaluable asset for learning geography in an embodied way. The objective of this work is to create and evaluate an easily constructible 3D tangible map for elementary students. The main differentiations of our approach are two: a) we suggest a new interaction style on the map for learning geography purposes,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.