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In many scenarios, domestic robot will regularly encounter unknown objects. In such cases, top-down knowledge about the object for detection, recognition, and classification cannot be used. To learn about the object, or to be able to grasp it, bottom-up object segmentation is an important competence for the robot. Also when there is top-down knowledge, prior segmentation of the object can improve...
In this paper a visual self-localization method for a humanoid robot is presented. This one is based on monocular information. The goal of this method is to obtain the position (x; y) and orientation θ of the humanoid robot inside the field of play. The methods proposed include some digital image processing algorithms and geometric interpretation to perform a 3D monocular reconstruction, that allows...
This paper presents a feature extraction method for hand gesture based on multi-layer perceptron. The feature of hand skin color in the YCbCr color space is used to detect hand gesture. The hand silhouette and features can be accurately extracted in means of binarizing the hand image and enhancing the contrast. Median and smoothing filters are integrated to remove the noise. Combinational parameters...
In this paper, we present a method for cup boundary detection from monocular colour fundus image to help quantify cup changes. The method is based on anatomical evidence such as vessel bends at cup boundary, considered relevant by glaucoma experts. Vessels are modeled and detected in a curvature space to better handle inter-image variations. Bends in a vessel are robustly detected using a region of...
A system for fast multi-view 3D model reconstruction of object sequences is composed of a number of hardware and software components: the multiple simultaneous image acquisition subsystems, the computation platform, the object/background segmentation algorithm, and in this case, a volumetric carving procedure based on the silhouettes of the objects from each view that generates a volumetric representation...
3D object reconstruction from images involves two important parts: object identification and object modeling. Human beings are very adept at automatically identifying different objects in a scene due to the extensive training they receive over their lifetimes. Similarly, machines need to be trained to perform this task. At present, automated 3D object identification process from aerial video imagery...
Multi-view approach has been proposed to solve occlusion and lack of visibility in crowded scenes. However, the problem is that too much redundancy information might bring about false alarm. Although researchers have done many efforts on how to use the multi-view information to track people accurately, it is particularly hard to wipe off the false alarm. Our approach is to use multiple views cooperatively...
We present a min-cut based method of segmenting objects in point clouds. Given an object location, our method builds a k-nearest neighbors graph, assumes a background prior, adds hard foreground (and optionally background) constraints, and finds the min-cut to compute a foreground-background segmentation. Our method can be run fully automatically, or interactively with a user interface. We test our...
Here, we address the problem of tracking of polyhedral objects in images from traffic scene. The matching of the model is achieved by minimizing the distance between the projected segments of the 3D model and the segments extracted in the 2D image. The success depends on the global optimization of the cost function that quantifies the quality of alignment. The existence of numerous local minima makes...
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