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This paper proposes a new target matching method for multiple cameras based on texture energy. The target region is represented by texture energy instead of traditional color features. In our method, the frame-difference is used in multi-target detecting, and the kalman filter is utilized in multi-target tracking. None camera calibrations are required in the new method, neither the constraint that...
Multiple-point geostatistics (MPS) simulation originates from geostatistical fields and allows extracting multiple-point structures from training images, after that MPS can copy these structures to the regions to be simulated. Porosity is quite significant to the characterization and prediction of transport properties. To simulate the porosity value, a porosity simulation method using continuous MPS...
Local binary patterns (LBP) method has been successfully applied into many texture classification areas. However, the most widely used uniform local binary patterns are defined according to the general texture micro-structures, which are not optimal for some specific application. In this paper we propose a novel adaptive local binary patterns (ALBP) method, which can adaptively choose the most suitable...
This paper describes a LIDAR-based perception system for ground robot mobility, consisting of 3D object detection, classification and tracking. The presented system was demonstrated on-board our autonomous ground vehicle MuCAR-3, enabling it to safely navigate in urban traffic-like scenarios as well as in off-road convoy scenarios. The efficiency of our approach stems from the unique combination of...
Bag-of-Visual-Features (BoVF) representations have achieved a great success when used for object recognition, mainly because of their robustness to several kinds of variations and occlusion. Recently, a number of BoVF approaches has been proposed also for recognition of human actions from videos. One important issue that arises when using BoVF for videos is how to take dynamic information into account,...
The Scale Invariant Feature Transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed. To reduce the number of features generated by SIFT as well as their extraction and matching time, a modified approach based sampling...
In this paper we propose a unified action recognition framework fusing local descriptors and holistic features. The motivation is that the local descriptors and holistic features emphasize different aspects of actions and are suitable for the different types of action databases. The proposed unified framework is based on frame differencing, bag-of-words and feature fusion. We extract two kinds of...
This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applications including robot navigation and automation, in particular with respect to path planning. To achieve robust object classification, we propose the idea of an object feature which represents a distribution of neighboring...
In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we...
In this paper we discuss the application of 3D scene reconstruction techniques in the area of automatic semantic annotation, search and retrieval of unedited video footage. Rather than working with static key-frames we exploit the time-depended dynamic properties of a moving camera. Based on state of the art camera self calibration techniques we develop a powerful analysis chain. We demonstrate, that...
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