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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...
As medical robotics gathers increasing attention, the ergonomics of the surgical-console design becomes an important issue. Motivated by the need of augmenting the surgeon mastery, we explore the capabilities of a near infrared brain-computer interface as a complementary input modality to enhance the human-robot interaction at the robotic console. A multistage analysis framework is proposed and evaluated...
Nowadays, the carpet quality analysis is determined in industry by human experts, because the automated assessment is not capable of matching the human expertise. Therefore, the carpet company demands a reliable and economic standardization of carpet wear level. This paper presents a new strategy for analyzing and classifying the texture of the wear carpet surface of 3D image, where 3D image is produced...
Confocal reflectance microscopy is an emerging modality, for dermatology applications, especially for in-situ and bedside detection of skin cancers. As this technology gains acceptance, automated processing methods become increasingly important to develop. Since the dominant internal feature of the skin is the epidermis/dermis boundary, it has been chosen as the initial target for this development...
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...
In this article, we present an approach for the automated extraction of quantitative information about trichome patterning on leaves of Arabidopsis thaliana. Time series of growing rosette leaves (4D confocal datasets, 3D + time) are used for this work. At first, significant anatomical structures, i.e. leaf surface and midplane are extracted robustly. Using the extracted anatomical structures, a biological...
This paper presents a new method for segmentation and interpretation of 3D point clouds from mobile LIDAR data. The main contribution of this work is the automatic detection and classification of artifacts located at the ground level. The detection is based on Top-Hat of hole filling algorithm of range images. Then, several features are extracted from the detected connected components (CCs). Afterward,...
This paper proposes a new person identification method using physiological and behavioral biometrics. Various person recognition systems have been proposed so far, and one of the recently introduced human characteristics for the person identification is gait. Although the shape of one's body has not been considered much as a characteristic, it is closely related to gait and it is difficult to disassociate...
In this paper we introduce Smario, a MATLAB open source toolbox for the analysis of BCI signals and implementation of translation algorithms for BCI systems. The Smario functions have been created based on the design of EEGLAB, they are accessible through the graphic user interface but they can also be run and edited using MATLAB syntax. Smario reads BCI2000 files in DAT and MAT formats, and documentation...
In this paper, we focus on the reliable detection of facial fiducial points, such as eye, eyebrow and mouth corners. The proposed algorithm aims to improve automatic land-marking performance in challenging realistic face scenarios subject to pose variations, high-valence facial expressions and occlusions. We explore the potential of several feature modalities, namely, gabor wavelets, independent component...
This paper presents a novel people detection and tracking method based on a combined multimodal sensor approach that utilizes 2D and 3D laser range and camera data. Laser data points are clustered and classified with a set of geometrical features using an SVM AdaBoost method. The clusters define a region of interest in the image that is adjusted using the ground plane information extracted from the...
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