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In this paper, we propose a new shape decomposition method based on visual perception for shape matching and retrieval which uses the protrusive corners under multiple scales. Our approach composes of a multi-scale shape decomposition procedure and a contour segments matching procedure. Firstly, morphological operations are used for more stable and visual perceptual corners to get the contour segmented...
It is a very challenging problem to make robot vision autonomously identify and recognize objects in the real world, and the recent research in this field has been moving forward mostly through designing smart shape descriptors for providing better similarity measure. In this paper, we propose a novel shape descriptor called hierarchical representation to describe the object shape efficiently and...
We present a system for real-time general object recognition (gor) for indoor robot in complex scenes. A point cloud image containing the object to be recognized from a Kinect sensor, for general object at will, must be extracted a point cloud model of the object with the Cluster Extraction method, and then we can compute the global features of the object model, making up the model database after...
This paper proposes a vision-based behavior selection system using biologically-inspired visual attention selection mechanisms, i.e., Bottom-Up attention and Top-Down attention. We adopt purely Bottom-Up attention selection to identify conspicuous regions for obtaining the salient landmarks, while propose an object-based Top-Down attention method using low dimensional task-relevant feature for searching...
Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical...
We consider the task of long-term visual SLAM, i.e., simultaneous localization and mapping, in a partially changing environment (SLAM-PCE). The main problem we face is how to obtain discriminative and compact visual landmarks, which are necessary to cope with changes in appearance in an environment and with a large amount of visual information. We address this issue by proposing the use of common...
Map matching is a fundamental task in many robot vision applications, including viewpoint localization, change detection, alignment, merging, segmentation of maps, and multi-robot mapping. Existing frameworks so far have concentrated on local feature-based approach, where discriminative local features are extracted from the maps and visual indexing and map database searched are performed to find correspondence...
Place or scene recognition is an important competence of mobile robots for operating in a real dynamic environment. Latent Dirichlet Allocation (LDA), a popular probabilistic model, can achieve outstanding performance in image recognition, and it has been attracted the attention of a large number of researchers. Parameter estimation is a key step for the learning procedure of LDA. In this paper, we...
Accurate localization and mapping play a pivotal role in mobile robot navigation. In this paper, we present a novel algorithm for mobile robot SLAM (Simultaneous Localization and Mapping) based on stereo vision. First a novel method is proposed to extract distinctive invariant image features, which is coined PLOT (Polynomial Local Orientation Tensor). The stability of these features to image translation,...
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