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This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is self-dependent and avoids the registration between an input face against every face in the gallery. Then,...
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...
The segmentation of brain tissue from non-brain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. In this paper, we propose a fast automatic skull-stripping method. The proposed method is based on an adaptive gauss mixture model and a 3D Mathematical Morphology method. The adaptive gauss mixture model...
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...
Recent advancements of 3D computer graphics hardware systems have made possible the handling of 3D volumetric data, and the amount of the available data has increased for various scientific fields. This paper proposes a pattern feature extraction method for 3D volumetric data. Pattern features are important for systems which require segmentation and classification. In this paper, the laws texture...
In this paper, we present a fully automatic system for face recognition based on a silhouette of the face profile. Previous research has demonstrated the high discriminative potential of this biometric. However, for the successful employment of this characteristic one is confronted with many challenges, such as the sensitivity of a profile's geometry to face rotation and the difficulty of accurate...
Driving assistance systems provide either safety or comfort functions. Such systems must evaluate the state of the world and take necessary actions. A preliminary step for evaluating the state of the world is to detect, track and classify scene objects. The classification step becomes especially important in complex urban traffic scenarios. In such scenarios the sensors of choice are vision based,...
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...
Given a set of labeled 3D meshes acquired from stereo imaging of heads, the goal of this research is to develop a successful methodology for discriminating between individuals with 22q11.2 deletion syndrome and the general population. Although many approaches for such discrimination exist in the medical and computer vision literature, the goal is to develop methods that focus on shape-based morphological...
A method to extract views of different orientations of a vehicle captured using multi cameras on roadside is proposed. We expect the use of multi views would increase classification performance in tasks such as identifying vehicle types/makes. This paper does not discuss classification work in details; it accepts the concept that with more data obtained through multi camera views, the use of distinctive...
We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints. Objects are represented as a coherent ensemble of parts that are consistent under 3D viewpoint transformations. Each part is a collection of salient image features. A generative framework is used for learning a model that captures the relative...
In this work, we have looked into the problem of urban analysis using airborne LiDAR data based on the strategy of classification by segmentation. Segmentation is a key and hard step in the processing of 3D point clouds, which is not perfectly solved in view of different applications. A new 3d segmentation method incorporating the advantages of nonparametric and spectral graph clustering is presented...
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...
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...
We present work on vision based robotic grasping. The proposed method relies on extracting and representing the global contour of an object in a monocular image. A suitable grasp is then generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for...
It has been demonstrated recently by the authors that texture analysis of 3D images of vascular trees can be used to describe the trees quantitatively (see References section). Computer-simulated trees and their raster images were used in that study. This paper presents experimental confirmation of the findings using supervised and unsupervised classification methods.
A new approach to modelling and classification of human gait is proposed. Body movements are obtained using a sensor suit that records inertial signals that are subsequently modelled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. Using a range of concurrent features extracted from...
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