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Our goal is to grasp 3D objects given a single image, by using prior 3D shape models of object classes. The shape models, defined as a collection of oriented primitive shapes centered at fixed 3D positions, can be learned from a few labeled images for each class. The 3D class model can then be used to estimate the 3D shape of a detected object, including occluded parts, from a single image. The estimated...
The ability to recognize objects and to localize them precisely is essential in all service robotic applications. One of the main challenges for service robots during operation lies in the handling of unavoidable uncertainties which originate from model and sensor inaccuracies and are characteristic for realistic application scenarios. Robustness under real world conditions can only be achieved when...
We propose a combined approach for 3D real-time object recognition and tracking, which is directly applicable to robotic manipulation. We use keypoints features for the initial pose estimation. This pose estimate serves as an initial estimate for edge-based tracking. The combination of these two complementary methods provides an efficient and robust tracking solution. The main contributions of this...
We present an algorithm to model 3D workspace and to understand test scene for navigation or human computer interaction in network-based mobile robot. This was done by line-based modelling and recognition algorithm. Line-based recognition using 3D lines has been tried by many researchers however its reliability still needs improvement due to ambiguity of 3D line feature information from original images...
This paper addresses the issue of learning and representing object grasp affordances, i.e. object-gripper relative configurations that lead to successful grasps. The purpose of grasp affordances is to organize and store the whole knowledge that an agent has about the grasping of an object, in order to facilitate reasoning on grasping solutions and their achievability. The affordance representation...
There are many algorithms to simplify meshes. But, most of them are to realize real-time rendering circumstance or models in virtual reality. This paper introduces a novel scheme to simplify meshes from a different view point--visual resolution. This paper uses visual recognizing object principle to simplify meshes. The model or scheme gives a reasonable standard which decides what extent the 3D model...
The categorization problem in object recognition is the assignment of semantic categories to objects or parts of objects. Here, the best categorization performance provide mammalian brain functions that motivate to partly mimic such a formation for the application to machine learning and automation. Curiosity and experimental willingness support human recognizing and detecting in relation to still...
Our goal is that the robot learns specific objects (not object category) from images. The major problem here is how to separate the target object from the background. We create a scene model from an image sequence. The scene model contains both the target object and background. We separate the target object from the background by matching the scene model and training images having different backgrounds...
In real world scenes a large amount of objects can occur, that have to be recognized and localized by humanoids. In this paper we propose a new approach to initialize three-dimensional pose and shape parameters for a large variety of objects, which is applicable to single images. In order to feed the recognition system with a priori knowledge, three-dimensional models are used and in order to cope...
In the past years, recognition algorithms based on local features have received much attention; and their advantages over traditional recognition methods in dealing with similarity transformation, partial occlusion and extraneous noise, have been verified. But due to the difficulty of most current local-feature-based methods in real-time implementation, their application in mobile robot vision has...
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