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This paper focuses on the spectral unmixing technique for analyzing hyperspectral image (HSI). In this paper, we first prove that the reconstruction errors and the abundance anomalies (AAs, abundances that are negative or greater than one) are effective in measuring the purity of pixels. Then, due to the continuity of the objects in the space, the endmembers are assumed to be located at some noticeable...
Traditional kernelized correlation filter tracking methods use the target position in the current frame to estimate the moving target initial position in the next frame. For fast moving target, these methods lose the target easily. To cope with this problem, a novel scale-adaptive regression position prediction tracking approach is proposed. This algorithm employs regression prediction method to predict...
This paper proposes a patch-based keypoints clustering method for long term robust visual tracking. We plan to employ a parallel framework with keypoints matching and estimation for tracking purpose. Patch-based method is implemented in our algorithm to improve the flexibility of system. The template is divided into patches to ensure the spatial constraint of local keypoints. The motion cue of patches...
Most Traditional algorithms using only unilateral estimation or bidirectional estimation usually produce poor visual quality because of the fact that the unilateral motion estimation suffers from holes and overlaps and the bidirectional motion estimation suffers from inaccurate motion vector. This paper presents a new improved Frame rate up conversion(FRUC) scheme which combines the unilateral estimation...
In order to cope with the complex variation of target appearance during visual tracking, a robust tracking algorithm based on multi-scale kernelized least squares (KLS) is proposed. First, by showing that the dense sampling set of translated patches is circulant, using the well-established theory of circulant matrices, kernelized least squares is efficient computed with fast Fourier transform (FFT)...
Object tracking is one of the important tasks for mobile robot, and developing a robust and real-time visual tracking algorithm which can adaptively capture the varying appearance of target under challenging conditions for mobile robot is still an open problem. The main challenges of visual tracking for mobile robot come from variation of target's appearance and disturbance of environment. To cope...
Drift is the most difficult issue in object visual tracking based on framework of “tracking-by-detection”. Due to the self-taught learning, the mis-aligned samples are potentially to be incorporated in learning and degrade the discrimination of the tracker. This paper proposes a new tracking approach that resolves this problem by three multi-level collaborative components: a high-level global appearance...
This paper presents a new robustification procedure for nonlinear least-squares optimisation problems. In particular, we focus on the robustness of view-graph SLAM against outlier correspondences in the images and outlier geometries in the graph. Our method utilises revised measurements model linearisation and decision making to detect and remove outliers during data fusion. We utilise innovations...
Nowadays, the technological and scientific research related to underwater perception is focused in developing more cost-effective tools to support activities related with the inspection, search and rescue of wreckages and site exploration: devices with higher autonomy, endurance and capabilities. Currently, specific tasks are already carried out by remotely-operated vehicles (ROV) and autonomous underwater...
We present an algorithm for autonomous network calibration of visual sensor networks, which become more and more pervasive since they can be found in various everyday life environments. The proposed algorithm works in a fully decentralized way and minimizes usage of cost-intensive vision algorithms. To achieve network calibration, our approach relies on jointly detected objects and geometric relations...
Correlation filters for long-term visual object tracking have recently seen great interest. Although they present competitive performance results, there is still a need for improving their tracking capabilities. In this paper, we present a fast scalable solution based on the Kernalized Correlation Filter (KCF) framework. We introduce an adjustable Gaussian window function and a keypoint-based model...
Local image features show a high degree of repeatability, while their local appearance usually does not bring enough discriminative pattern to obtain a reliable matching. In this paper, we present a new object matching algorithm based on a novel robust estimation of residual consensus and flexible spatial consistency filter. We evaluate the similarity between different homography model via two-parameter...
Real-time and reliable localization is a prerequisite for autonomously performing high-level tasks with micro aerial vehicles(MAVs). Nowadays, most existing methods use vision system for 6DoF pose estimation, which can not work in degraded visual environments. This paper presents an onboard 6DoF pose estimation method for an indoor MAV in challenging GPS-denied degraded visual environments by using...
Visual markers are useful tools assisting visual recognition of object pose in robotic applications. But they have two fundamental problems in orientation estimation. One is degradation of orientation accuracy in frontal observation. The other is “pose ambiguity” that the orientation cannot be determined uniquely. We previously developed a novel visual marker “LentiMark” which solves the former problem...
Lightweight RGB-D cameras that can provide rich 2D visual and 3D point cloud information are well suited to the motion estimation of indoor micro aerial vehicles (MAVs). In recent years, several RGB-D visual odometry methods which process data from the sensor in different ways have been proposed. However, it is unclear which methods are preferable for online odometry estimation on a computation-limited,...
The capability to instantiate a cooperation among heterogeneous agents is a fundamental feature in mobile robotics. In this paper we focus on the interaction between Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicle (UAV) to extend the endurance of UAV, thanks to a novel landing/recharging platform. The UGV acts as a docking station and hosts the UAV during the indoor/outdoor transition and...
This paper proposes a new method for rigid body pose estimation based on spectrahedral representations of the tautological orbitopes of SE(2) and SE(3). The approach can use dense point cloud data from stereo vision or an RGB-D sensor (such as the Microsoft Kinect), as well as visual appearance data as input. The method is a convex relaxation of the classical pose estimation problem, and is based...
Real-time, robust and precise estimation of a robot's ego-motion is a crucial requirement for higher level tasks like autonomous navigation. In this paper, a real-time and robust odometry estimation system for indoor micro aerial vehicle (MAV) is developed by only using the point cloud generated from the depth camera. First, local surface normal features are used to select points with most constraints...
This paper describes the physics-based modeling of the second visual system, the ocelli, common to many species of flying insects. We perform high fidelity visual simulation to study its role in estimating angular velocities during flight. Development of a fully analog, bio-mimetic ocellar sensor is detailed.
Perception of the surrounding environment is one of the many tasks an automated vehicle has to achieve in complex and ever-changing surroundings. This typically includes several distinct sub-tasks, such as map-building, localisation, static obstacles detection, pedestrian detection,… Some of these tasks are nowadays very well known, such as map-building, whereas the perception, localisation and classification...
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