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We propose the use of a light-weight setup consisting of a collocated camera and light source – commonly found on mobile devices – to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D “univariate” sampling of a 3-D isotropic BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical...
Visual servoing (VS) is an automatic control technique which uses the feedback of visual information to guide the robot motion to converge to the desired location. This paper proposes new moment-based features for robust VS with spheres, especially in cameras obeying the unified model. Specifically, we introduce a virtual plane to reload contours of image projections of spheres, in the process of...
We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras. It jointly optimizes for all the model parameters within the active window, including the intrinsic/extrinsic camera parameters of all keyframes and the depth values of all selected pixels. In particular, we propose a novel...
Current fiducial marker detection algorithms rely on marker IDs for false positive rejection. Time is wasted on potential detections that will eventually be rejected as false positives. We introduce ChromaTag, a fiducial marker and detection algorithm designed to use opponent colors to limit and quickly reject initial false detections and grayscale for precise localization. Through experiments, we...
Triangulation is a fundamental task in 3D computer vision. Unsurprisingly, it is a well-investigated problem with many mature algorithms. However, algorithms for robust triangulation, which are necessary to produce correct results in the presence of egregiously incorrect measurements (i.e., outliers), have received much less attention. The default approach to deal with outliers in triangulation is...
Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn...
This paper addresses the problem of estimating the alignment pose between two models by using structure-specific local descriptors which are generated by combining 2D image data and 3D contextual shape data. The 2D texture information is represented by a robust SIFT descriptor, and the geometric information is represented by a histogram supported by the orders of curvature and angles between normal...
Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While...
Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive...
People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multipeople tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly model those...
We present a robust algorithm for personalizing a spheremesh tracking model to a user from a collection of depth measurements. Our core contribution is to demonstrate how simple geometric reasoning can be exploited to build a shape-space, and how its performance is comparable to shape-spaces constructed from datasets of carefully calibrated models. We achieve this goal by first reparameterizing the...
Augmented reality is popular and rapidly growing direction. It is successfully used in medicine, education, engineering and entertainment. In the paper, basic principles of typical augmented reality system are described. An efficient hybrid visual tracking algorithm is proposed. The approach is based on combining of the optical flow technique with direct tracking methods. It is demonstrated that developed...
In this paper we present an end to end object modeling pipeline for an unmanned aerial vehicle (UAV). We contribute a UAV system which is able to autonomously plan a path, navigate, acquire views of an object in the environment from which a model is built. The UAV does collision checking of the path and navigates only to those areas deemed safe. The data acquired is sent to a registration system which...
In this paper, we present a robust system of self- directed autonomous robots evolving in a complex and public spaces and interacting with people. This system integrates highlevel skills of environment modeling using knowledge-based modeling and reasoning and scene understanding with robust image and video analysis, distributed autonomous decision- making using Markov decision process and Petri-Net...
Although ear shape is used in forensic investigations, ear identification system assisting forensic experts is not well developed. One of the reasons for this is ear's 3D concave shape that changes their 2D appearances when there are changes in camera angles. To compensate these changes in 2D appearance, 3D statistical modeling is necessary. For this purpose, only one model is used in our previous...
This work presents KMVO, a ground plane based visual odometry that utilizes the vehicle's kinematic model to improve accuracy and robustness. Instead of solving a generic image alignment problem, the motion parameters of a differential drive vehicle can be directly estimated from RGB- D image data. In addition, a method for outlier rejection is presented that can deal with large percentages of outliers...
This paper presents an implementation of a system that is autonomously able to find, follow and land on a car moving at 15 km/h. Our solution consists of two parts, the image processing for fast onboard detection of landing platform and the Model Predictive Control tracker for trajectory planning and control. This approach is fully autonomous using only the onboard computer and onboard sensors with...
This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian...
Shape from focus technique can be used in the computer monocular vision, which is widely applied in the smart transportation. In this study, we proposed a novel directional statistics based focus measure for shape from focus computation. We first compute the standard deviation σ and the mean value μ in the directional neighborhood. Then use the σ/μ as the focus measure to estimate the shape. The proposed...
We explore a new sensor suite to provide a precise and robust navigation information, primarily intended for pedestrian localisation. We use an IMU sensor augmented with an array of magnetometers, called MIMU (for Magneto-Inertial measurement Unit) hereafter, and a single central camera as the vision sensor. The MIMU sensor has been shown in previous work to significantly improve the inertial dead-reckoning...
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