基于全景视觉的机器人回航方法

提出一种使用全景视觉系统引导机器人回航的方法.利用全景视觉装置采集出发位置(Home位置)的全景图像,使用SURF(Speeded-Up Robust Feature)算法提取全景图像中的特征点作为自然路标点.机器人回航过程中,将当前位置获得的全景图像与Home位置的全景图像进行特征匹配,确定白然路标点之间的对应关系....     

A Visual Global Positioning System for Unmanned Aerial Vehicles Used in Photogrammetric Applications

The combination of photogrammetric aerial and terrestrial recording methods can provide new opportunities for photogrammetric applications. A UAV (Unmanned Aerial Vehicle), in our case a helicopter system, can cover both the aerial and quasi-terrestrial image acquisition methods. A UAV can be equipped with an on-board high resolution camera and a priori knowledge of the operating area where to perform photogrammetric tasks. In this general scenario our paper proposes vision-based techniques for localizing a UAV. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. The novel idea is to perform global localization, position tracking and localization failure recovery (kidnapping) based only on visual matching between current view and available georeferenced satellite images. The matching is based on SIFT features and the system estimates the position of the UAV and its altitude on the base of the reference image. The vision system replaces the GPS signal combining position information from visual odometry and georeferenced imagery. Georeferenced satellite or aerial images must be available on-board beforehand or downloaded during the flight. The growing availability of high resolution satellite images (e.g., provided by Google Earth or other local information sources) makes this topic very interesting and timely. Experiments with both synthetic (i.e., taken from satellites or datasets and pre elaborated) and real world images have been performed to test the accuracy and the robustness of our method. Results show sufficient performance if compared with common GPS systems and give a good performance also in the altitude estimation, even if in this last case there are only preliminary results.     

Vision-Based Navigation for an Electric Wheelchair Using Ceiling Light Landmark

This paper presents an autonomous wheelchair system with the capability of self-location and obstacle avoidance. The wheelchair is equipped with two TV cameras that are used for self-location and obstacle avoidance, respectively. In this system, the fluorescent ceiling lights are chosen as landmarks since they can be easily detected and do not require an additional installation. A recognition procedure of landmarks is described by which the desired landmark images in the navigational environment can be retrieved. A self-location technique using ceiling light landmarks is proposed. Using this self-location function, the wheelchair can locate itself in a world coordinate system. The path planning based on landmark and the method of generating a control scheme are presented so that the wheelchair is capable of navigating along any path from start position to goal position. A low cost and high-speed vision system for the detection and avoidance of obstacle is developed and the principal of obstacle avoidance is introduced. A number of navigation experiments are conducted for the wheelchair in an indoor environment. The experimental results indicate the effectiveness of the wheelchair system.     

Vision-aided Estimation of Attitude,Velocity, and Inertial Measurement Bias for UAV Stabilization

This paper studies vision-aided inertial navigation of small-scale unmanned aerial vehicles (UAVs) in GPS-denied environments. The objectives of the navigation system are to firstly online estimate and compensate the unknown inertial measurement biases, secondly provide drift-free velocity and attitude estimates which are crucial for UAV stabilization control, and thirdly give relatively accurate position estimation such that the UAV is able to perform at least a short-term navigation when the GPS signal is not available. For the vision system, we do not presume maps or landmarks of the environment. The vision system should be able to work robustly even given low-resolution images (e.g., 160 ×120 pixels) of near homogeneous visual features. To achieve these objectives, we propose a novel homography-based vision-aided navigation system that adopts four common sensors: a low-cost inertial measurement unit, a downward-looking monocular camera, a barometer, and a compass. The measurements of the sensors are fused by an extended Kalman filter. Based on both analytical and numerical observability analyses of the navigation system, we theoretically verify that the proposed navigation system is able to achieve the navigation objectives. We also show comprehensive simulation and real flight experimental results to verify the effectiveness and robustness of the proposed navigation system.     

印染错花在线智能监测系统的研究

针对目前圆网印花机对花技术的现状,提出了一种基于机器视觉技术的新型圆网印花机自动对花系统。该系统采用图像传感器实时采集印花过程中的花位标记图像,利用数字图像处理算法对采集到的图像进行处理分析,实时获取对花误差,并根据误差信号转化输出为控制信号,通过控制器控制、调节各印花网筒和整纬器的运行状态或参数,实现了印花精度的智能式检测与控制,提高了对花精度,进而提高了产品质量和工作效率。     

基于肺部组织特征的图像弹性配准研究

图像配准是一种建立两幅图像空间对应关系的过程,它被广泛应用于计算机视觉、遥感数据分析及图像处理中,特别是在影像引导放射治疗领域,图像配准发挥着巨大作用。但由于受呼吸运动的影响,精确的肺部影像配准依然是一个充满挑战的难题。目前,尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)已被用于医学图像配准中,并且取得了较理想的结果。然而,SIFT检测到的仅是图像的块特征,不能有效的反映肺部的运动。文章提出了一种基于Harris和SIFT算子的杂交型特征检测方法,这种方法能有效检测肺部的组织特征,如血管分叉点和肺部边界等。除此之外,为了有效去除特征匹配过程中产生的错配点,还提出了一种基于互相关和组织结构不变性的滤除错配点方法。文章最后采用一系列不同呼吸周期的肺部CT影像来对所提出的算法进行验证。定性和定量的结果表明,该算法较传统的SIFT算法更具优越性。     

Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames

Pathfinding is becoming more and more common in autonomous vehicle navigation, robot localization, and other computer vision applications. In this paper, a novel approach to mapping and localization is presented that extracts visual landmarks from a robot dataset acquired by a Kinect sensor. The visual landmarks are detected and recognized using the improved scale-invariant feature transform (I-SIFT) method. The methodology is based on detecting stable and invariant landmarks in consecutive (red-green-blue depth) RGB-D frames of the robot dataset. These landmarks are then used to determine the robot path, and a map is constructed by using the visual landmarks. A number of experiments were performed on various datasets in an indoor environment. The proposed method performs efficient landmark detection in various environments, which includes changes in rotation and illumination. The experimental results show that the proposed method can solve the simultaneous localization and mapping (SLAM) problem using stable visual landmarks, but with less computation time.     

用于高精度三维计算机视觉的图象系统标定和误差补偿

为了对计算机视觉系统进行标定,并削弱由系统模型与真实系统间差异所产生的误差,在详细分析计算机数字成象过程的基础上,提出了一种综合数字成象过程中内部的及外部的,已知的及未知的等各种因素的图象系统标定方法,其标定的具体对象包括光学成象过程中的线性几何投影参数及非线性畸变参数、光电转换过程中的参数和图象信号的D／A及A／D转换过程中的参数等;同时提出了基于该标定方法的三维计算机视觉系统采集三维数据的误差补偿方法及其参数的确定过程。上述方法已经成功地应用于三维计算机视觉系统,并取得了很好的三维精度。     

多介质下空间目标的视觉测量

基于计算机视觉的无接触三维测量原理和光的折射定律,提出一种多介质下空间目标视觉测量方法。采用多相机捆绑调整及多角度相互校正,通过双目立体视觉技术结合光线在多介质中发生两次折射的数学模型对运动体上各标志点进行测量,获取各标志点的三维空间坐标,经平面拟合、坐标转换,最终解算得到运动体六自由度空间坐标。实验结果表明,该方法适用于多介质视觉测量,具有较高的测量精度和良好的稳定性。     

An EyeTap video-based featureless projective motion estimation assisted by gyroscopic tracking for wearable computer mediated reality

In this paper we present a computationally economical method of recovering the projective motion of head mounted cameras or EyeTap devices, for use in wearable computer-mediated reality. The tracking system combines featureless vision and inertial methods in a closed loop system to achieve accurate robust head tracking using inexpensive sensors. The combination of inertial and vision techniques provides the high accuracy visual registration needed for fitting computer graphics onto real images and the robustness to large interframe camera motion due to fast head rotations. Operating on a 1.2 GHz Pentium III wearable computer with graphics accelerated hardware, the system is able to register live video images with less than 2 pixels of error (0.3 degrees) at 12 frames per second. Fast image registration is achieved by offloading computer vision computation onto the graphics hardware, which is readily available on many wearable computer systems. As an application of this tracking approach, we present a system which allows wearable computer users to share views of their current environments that have been stabilised to another viewer's head position.

面向远程光体积描记的人脸检测与跟踪

目的 远程光体积描记（remote photoplethysmograph,rPPG）是一种基于视频的非接触心率测量方法,通过跟踪人脸皮肤区域并从中提取周期性微弱变化的颜色信号估计出心率。目前基于级联回归树的人脸地标方法训练的Dlib库,由于能快速准确定位人脸轮廓,正逐渐被研究者用于跟踪皮肤感兴趣区域（region of interest,ROI）。由于实际应用中存在地标无规则抖动,且现有研究没有考虑目标晃动的影响,因此颜色信号提取不准确,心率估计精度不佳。为了克服以上缺陷,提出一种基于Dlib的抗地标抖动和运动晃动的跟踪方法。方法 本文方法主要包含两个步骤：首先,通过阈值判断两帧间地标的区别,若近似则沿用地标,反之使用当前帧地标以解决抖动问题。其次,针对运动晃动,通过左右眼地标中点计算旋转角度,矫正晃动的人脸,保证ROI在运动中也能保持一致。结果 通过信噪比（signal-to-noise,SNR）、平均绝对误差（mean absolute error,MAE）和均方根误差（root mean squared error,RMSE）来评价跟踪方法在rPPG中的测量表现。经在UBFC-RPPG（stands for Univ.Bourgogne Franche-Comté Remote PhotoPlethysmoGraphy）和PURE（Pulse Rate Detection Dataset）数据集测试,与Dlib相比,本文方法rPPG测量结果在UBFC-RPPG中SNR提高了约0.425 dB,MAE提高0.291 5 bpm,RMSE降低0.645 3 bpm;在PURE中SNR降低了0.041 1 dB,MAE降低0.065 2 bpm,RMSE降低0.271 8 bpm。结论 本文方法相比于Dlib有效提高跟踪框稳定性,在静止和运动中都能跟踪相同ROI,适合rPPG应用。     

Tracking a Ground Moving Target with a Quadrotor Using Switching Control

An unmanned aerial vehicle (UAV) stabilization strategy based on computer vision and switching controllers is proposed. The main goal of this system is to perform tracking of a moving target on ground. The architecture implemented consists of a quadrotor equipped with an embedded camera which provides real-time video to a computer vision algorithm where images are processed. A vision-based estimator is proposed, which makes use of 2-dimensional images to compute the relative 3-dimensional position and translational velocity of the UAV with respect to the target. The proposed estimator provides the required states measurements to a micro-controller for stabilizing the vehicle during flight. The control strategy consists of switching controllers, which allows making decisions when the target is lost temporarily or when it is out of the camera’s field of view. Real time experiments are presented to demonstrate the performance of the target-tracking system proposed.     

Embedded omni-vision navigator based on multi-object tracking

This paper presents an embedded omni-vision navigation system which involves landmark recognition, multi-object tracking, and vehicle localization. A new tracking algorithm, the feature matching embedded particle filter, is proposed. Landmark recognition is used to provide the front-end targets. A global localization method for omni-vision based on coordinate transformation is also proposed. The digital signal processor (DSP) provides a hardware platform for on-board tracker. Dynamic navigator employs DSP tracker to follow the landmarks in real time during the arbitrary movement of the vehicle and computes the position for localization based on time sequence images analysis. Experimental results demonstrated that the navigator can efficiently offer the vehicle guidance.     

Principal warps: thin-plate splines and the decomposition ofdeformations

The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images in indicated     

Salient and non-salient fiducial detection using a probabilistic graphical model

Deformable shape detection is an important problem in computer vision and pattern recognition. However, standard detectors are typically limited to locating only a few salient landmarks such as landmarks near edges or areas of high contrast, often conveying insufficient shape information. This paper presents a novel statistical pattern recognition approach to locate a dense set of salient and non-salient landmarks in images of a deformable object. We explore the fact that several object classes exhibit a homogeneous structure such that each landmark position provides some information about the position of the other landmarks. In our model, the relationship between all pairs of landmarks is naturally encoded as a probabilistic graph. Dense landmark detections are then obtained with a new sampling algorithm that, given a set of candidate detections, selects the most likely positions as to maximize the probability of the graph. Our experimental results demonstrate accurate, dense landmark detections within and across different databases.     

Context modeling for facial landmark detection based on Non-Adjacent Rectangle (NAR) Haar-like feature

Automatically locating facial landmarks in images is an important task in computer vision. This paper proposes a novel context modeling method for facial landmark detection, which integrates context constraints together with local texture model in the cascaded AdaBoost framework. The motivation of our method lies in the basic human psychology observation that not only the local texture information but also the global context information is used for human to locate facial landmarks in faces. Therefore, in our solution, a novel type of feature, called Non-Adjacent Rectangle (NAR) Haar-like feature, is proposed to characterize the co-occurrence between facial landmarks and its surroundings, i.e., the context information, in terms of low-level features. For the locating task, traditional Haar-like features (characterizing local texture information) and NAR Haar-like features (characterizing context constraints in global sense) are combined together to form more powerful representations. Through Real AdaBoost learning, the most discriminative feature set is selected automatically and used for facial landmark detection. To verify the effectiveness of the proposed method, we evaluate our facial landmark detection algorithm on BioID and Cohn-Kanade face databases. Experimental results convincingly show that the NAR Haar-like feature is effective to model the context and our proposed algorithm impressively outperforms the published state-of-the-art methods. In addition, the generalization capability of the NAR Haar-like feature is further validated by extended applications to face detection task on FDDB face database.     

基于改进时间差分的视觉/惯性组合导航研究

针对全球导航卫星系统(GNSS,Global Navigation Satellite System)在军事战争、室内和水下等情况下存在因信号缺失导致的全球定位系统（GPS,Global Positioning System）无法使用和惯性导航系统(INS,Inertial Navigation System)状态误差发散过快的问题,提出了一种基于连续帧时间差分视觉辅助导航的方法。为了抑制状态误差的快速发散,提高INS在长航时工作上的性能,分析了机器视觉连续帧间差分法,并对其进行了计算上的改进,设计了一种时间差分视觉/惯性组合系统,并进行了仿真实验和分析。结果与纯INS相比,均方根误差(RMSE,Root Mean Square Error)在北向位置上减少了19.0%,在东向误差上减少了32.1%,表明该方法有效抑制了纯惯性导航速度和位置的误差发散,延长了惯性导航的可用时间。     

基于近红外视觉的机器人室外定位系统

在光照条件可变且存在电磁干扰的环境下,针对机器人室外导航任务,提出了一种基于全景近红外视 觉和编码路标的自定位系统．通过近红外光源照明,利用全景视觉识别采用条形编码格式的路标,并利用扩展卡尔 曼滤波算法（EKF）融合视觉数据和里程计数据,从而实现机器人自定位．实验证明,该方法消除了室外大范围导航 时光照变化对机器人定位结果的影响．     

一种改进的区域双目立体匹配方法

双目立体匹配是机器视觉中的热点、难点问题。分析了区域立体匹配方法的优缺点,提出了改进的区域立体匹配方法。首先,采集双目视觉图像对对图像对进行校正、去噪等处理,利用颜色特征进行图像分割,再用一种快速有效的块立体匹配算法对图像进行立体匹配。然后,在匹配过程中使用绝对误差累积(SAD)的小窗口来寻找左右两幅图像之间的匹配点。最后,通过滤波得到最终的视差图。实验表明:该方法能够有效地解决重复区域、低纹理区域、纹理相似区域、遮挡区域等带来的误匹配问题,能得到准确清晰的稠密视差图。     

A method of parallel-sequential detection of image features in computer vision systems

The paper is devoted to the problem of formalization of software-hardware solutions in designing real-time computer vision systems. The main attention is paid to the methods of implementation of low-level operations that find features (simple elements) in the image input to the system. Algorithmic types of detectors of simple elements in images are analyzed from the point of view of hardware organization in computer vision systems. In this connection, the necessary performance and memory resources are estimated. The capabilities of parallel and pipeline execution of detector algorithms are investigated. A method of using a field programmable gate array and a digital signal processor in solving the problem of image processing in real-time computer vision systems is considered in detail.