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Fourth Canadian Conference on Computer and Robot Vision (CRV '07)最新文献

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A Factorized Recursive Estimation of Structure and Motion from Image Velocities 基于图像速度的结构和运动的因式递归估计
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.2
Adel H. Fakih, J. Zelek
We propose a new approach for the recursive estimation of structure and motion from image velocities. The estimation of structure and motion from image velocities is preferred to the estimation from pixel correspondences when the image displacements are small, since the former approach provides a stronger constraint being based on the instantaneous equation of rigid bodies motion. However the recursive estimation when dealing with image velocities is harder than its counterpart (in the case of pixel correspondences) since the number of points is usually larger and the equations are more involved. For this reason, in contrast to the case of point correspondences, the approaches presented so far are mostly limited to assuming a known 3D motion, or estimating the motion and structure independently. The approach presented in this paper introduces a factorized particle filter for estimating simultaneously the 3D motion and depth. Each particle consists of a 3D motion and a set of probability distributions of the depths of the pixels. The recursive estimation is done in three stages. (1) a resampling and a prediction of new samples; (2) a recursive filtering of the individual depths distributions performed using Extended Kalman Filters; and (3)finally a reweighting of the particles based on the image measurement. Results on simulation data show the efficiency of the approach. Future work will focus on incorporating an estimation of object boundaries to be used in a following regularization step.
我们提出了一种从图像速度递归估计结构和运动的新方法。当图像位移较小时,从图像速度估计结构和运动比从像素对应估计更好,因为前一种方法基于刚体运动的瞬时方程提供了更强的约束。然而,处理图像速度时的递归估计比它的对应(在像素对应的情况下)更难,因为点的数量通常更大,方程更复杂。因此,与点对应的情况相反,迄今为止提出的方法大多局限于假设已知的3D运动,或独立估计运动和结构。本文提出了一种同时估计三维运动和深度的分解粒子滤波方法。每个粒子由一个3D运动和一组像素深度的概率分布组成。递归估计分三个阶段完成。(1)对新样本进行重采样和预测;(2)使用扩展卡尔曼滤波器对单个深度分布进行递归滤波;(3)最后基于图像测量对粒子进行加权。仿真结果表明了该方法的有效性。未来的工作将集中在纳入目标边界的估计,以用于后续的正则化步骤。
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引用次数: 2
Constructing Face Image Logs that are Both Complete and Concise 构建完整而简洁的面部图像日志
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.20
Adam Fourney, R. Laganière
This paper describes a construct that we call a face image log. Face image logs are collections of time stamped images representing faces detected in surveillance videos. The techniques demonstrated in this paper strive to construct face image logs that are complete and concise in the sense that the logs contain only the best images available for each individual observed. We begin by describing how to assess and compare the quality of face images. We then illustrate a robust method for selecting high quality images. This selection process takes into consideration the limitations inherent in existing face detection and person tracking techniques. Experimental results demonstrate that face logs constructed in this manner generally contain fewer than 5% of all detected faces, yet these faces are of high quality, and they represent all individuals detected in the video sequence.
本文描述了一个我们称之为人脸图像日志的结构。人脸图像日志是在监控视频中检测到的带有时间戳的人脸图像的集合。本文展示的技术力求构建完整而简洁的人脸图像日志,因为日志只包含每个观察到的个体的最佳图像。我们首先描述如何评估和比较面部图像的质量。然后,我们说明了一种鲁棒的方法来选择高质量的图像。这种选择过程考虑了现有人脸检测和人员跟踪技术固有的局限性。实验结果表明,以这种方式构建的人脸日志通常只包含不到5%的所有检测到的人脸,但这些人脸的质量很高,它们代表了视频序列中检测到的所有个体。
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引用次数: 31
Learning Saccadic Gaze Control via Motion Prediciton 通过运动预测学习扫视控制
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.42
Per-Erik Forssén
This paper describes a system that autonomously learns to perform saccadic gaze control on a stereo pan-tilt unit. Instead of learning a direct map from image positions to a centering action, the system first learns a forward model that predicts how image features move in the visual field as the gaze is shifted. Gaze control can then be performed by searching for the action that best centers a feature in both the left and the right image. By attacking the problem in a different way we are able to collect many training examples in each action, and thus learning converges much faster. The learning is performed using image features obtained from the scale invariant feature transform (SIFT) detected and matched before and after a saccade, and thus requires no special environment during the training stage. We demonstrate that our system stabilises already after 300 saccades, which is more than 100 times fewer than the best current approaches.
本文描述了一种在立体平移装置上自主学习进行眼球注视控制的系统。该系统不是学习从图像位置到居中动作的直接映射,而是首先学习一个前向模型,该模型预测图像特征在视线转移时如何在视野中移动。然后,可以通过搜索在左右图像中最能集中一个特征的动作来进行凝视控制。通过以不同的方式解决问题,我们能够在每个动作中收集许多训练样例,因此学习收敛得更快。学习是使用在扫视前后检测和匹配的尺度不变特征变换(SIFT)得到的图像特征进行的,因此在训练阶段不需要特殊的环境。我们证明,我们的系统在300次扫视后就已经稳定了,这比目前最好的方法少了100多倍。
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引用次数: 18
A Framework for 3D Hand Tracking and Gesture Recognition using Elements of Genetic Programming 基于遗传规划的三维手部跟踪和手势识别框架
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.3
A. El-Sawah, C. Joslin, N. Georganas, E. Petriu
In this paper we present a framework for 3D hand tracking and dynamic gesture recognition using a single camera. Hand tracking is performed in a two step process: we first generate 3D hand posture hypothesis using geometric and kinematics inverse transformations, and then validate the hypothesis by projecting the postures on the image plane and comparing the projected model with the ground truth using a probabilistic observation model. Dynamic gesture recognition is performed using a Dynamic Bayesian Network model. The framework utilizes elements of soft computing to resolve the ambiguity inherent in vision-based tracking by producing a fuzzy hand posture output by the hand tracking module and feeding back potential posture hypothesis from the gesture recognition module.
在本文中,我们提出了一个使用单个相机进行三维手部跟踪和动态手势识别的框架。手部跟踪分两步进行:首先使用几何和运动学逆变换生成三维手部姿势假设,然后通过在图像平面上投影姿势并使用概率观察模型将投影模型与地面真实情况进行比较来验证假设。动态手势识别是使用动态贝叶斯网络模型执行的。该框架利用软计算的元素,通过手部跟踪模块产生一个模糊的手部姿态输出,并从手势识别模块反馈潜在的姿态假设,来解决基于视觉的跟踪固有的模糊性。
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引用次数: 33
Vehicle Tracking and Distance Estimation Based on Multiple Image Features 基于多图像特征的车辆跟踪与距离估计
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.68
Yixin Chen, M. Das, D. Bajpai
In this paper, we introduce a vehicle tracking algorithm based on multiple image features to detect and track the front car in a collision avoidance system (CAS) application. The algorithm uses multiple image features, such as corner, edge, gradient, vehicle symmetry property, and image matching technique to robustly detect the vehicle bottom corners and edges, and estimate the vehicle width. Based on the estimated vehicle width, a few pre-selected edge templates are used to match the image edges that allow us to estimate the vehicle height, and also the distance between the front vehicle and the host vehicle. Some experimental results based on real world video images are presented. These seem to indicate that the algorithm is capable of identifying a front vehicle, tracking it, and estimating its distance from the host vehicle.
本文介绍了一种基于多图像特征的车辆跟踪算法,用于防撞系统中前车的检测和跟踪。该算法利用图像的角、边、梯度、车辆对称性等多种特征,结合图像匹配技术对车辆底部角、边缘进行鲁棒检测,并对车辆宽度进行估计。基于估计的车辆宽度,使用几个预先选择的边缘模板来匹配图像边缘,使我们能够估计车辆高度,以及前车与主车之间的距离。给出了一些基于真实世界视频图像的实验结果。这些似乎表明,该算法能够识别前方车辆,跟踪它,并估计其与主车辆的距离。
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引用次数: 21
Using Feature Selection For Object Segmentation and Tracking 利用特征选择进行目标分割和跟踪
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.67
M. S. Allili, D. Ziou
Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance was used.
过去的大多数图像分割算法都是基于优化目标函数,目的是实现几个低级特征之间的相似性,从而将图像划分为均匀区域。在本文中,我们提出结合分组特征的相关性(选择)来强制分割,以捕获感兴趣的对象。特征的相关性是通过用户先验定义的特定对象的一组正面和负面示例来确定的。特征相关性的计算是通过最大化一个目标函数来完成的,该目标函数定义在正、负对象示例集的混合似然上。结合目标分割中的特征相关性,通过使用水平集活动轮廓最小化的能量函数来制定。我们在几个使用特征相关性的兴趣对象分割和跟踪示例上展示了该方法的效率。
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引用次数: 3
A new segmentation method for MRI images of the shoulder joint 一种新的肩关节MRI图像分割方法
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.4
N. Nguyen, D. Laurendeau, A. Albu
This paper presents an integrated region-based and gradient-based supervised method for segmentation of a patient magnetic resonance images (MRI) of the shoulder joint. The method is noninvasive, anatomy-based and requires only simple user interaction. It is generic and easily customizable for a variety of routine clinical uses in orthopedic surgery.
本文提出了一种基于区域和梯度的综合监督方法,用于患者肩关节磁共振图像的分割。该方法是无创的,基于解剖学,只需要简单的用户交互。它是通用的,很容易定制的各种常规临床应用在骨科手术。
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引用次数: 14
Oriented-Filters Based Head Pose Estimation 基于定向过滤器的头部姿态估计
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.48
M. Dahmane, J. Meunier
The aim of this study is to elaborate and validate a methodology to automatically assess head orientation with respect to a camera in a video sequence. The proposed method uses relatively stable facial features (upper points of the eyebrows, upper nasolabial-furrow corners and nasal root) that have symmetric properties to recover the face slant and tilt angles. These fiducial points are characterized by a bank of steerable filters. Using the frequency domain, we present an elegant formulation to linearly decompose a Gaussian steerable filter into a set of x, y separable basis Gaussian kernels. A practical scheme to estimate the position of the occasionally occluded nasolabial-furrow facial feature is also proposed. Results show that head motion can be estimated with sufficient precision to obtain the gaze direction without camera calibration or any other particular settings are required for this purpose.
本研究的目的是详细阐述和验证一种方法,以自动评估头部方向相对于摄像机在视频序列。该方法利用相对稳定的具有对称特性的面部特征(眉毛上点、鼻唇沟上角和鼻根)来恢复面部的倾斜和倾斜角度。这些基点由一组可操纵滤波器来表征。利用频域,我们提出了一个优雅的公式,将高斯可导滤波器线性分解成一组x, y个可分离基高斯核。提出了一种估计偶尔闭塞的鼻唇沟面部特征位置的实用方案。结果表明,在不需要相机校准或任何其他特殊设置的情况下,可以以足够的精度估计头部运动以获得凝视方向。
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引用次数: 2
Real-Time Commercial Recognition Using Color Moments and Hashing 利用颜色矩和哈希的实时商业识别
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.53
Abhishek Shivadas, J. Gauch
In this paper, our focus is on real-time commercial recognition. In particular, our goal is to correctly identify all commercials that are stored in our commercial database within the first second of their broadcast. To meet this objective, we make use of 27 color moments to characterize the content of every video frame. This representation is much more compact than most color histogram representations, and it less sensitive to noise and other distortion. We use frame-level hashing with subsequent matching of moment vectors and video frames to perform commercial recognition. Hashing provides constant time access to millions of video frames, so this approach can perform in real-time for databases containing thousands of commercials. In our experiments with a database of 63 commercials, we achieved 96% recall, 100% precision, and 98% utility while recognizing commercials within the first 1/2 second of their broadcast.
本文的研究重点是实时商业识别。特别是,我们的目标是在播放的第一秒内正确识别存储在我们的商业数据库中的所有广告。为了实现这一目标,我们使用27个彩色矩来表征每个视频帧的内容。这种表示比大多数颜色直方图表示要紧凑得多,并且对噪声和其他失真不太敏感。我们使用帧级哈希,随后匹配矩向量和视频帧来执行商业识别。散列提供了对数百万视频帧的恒定时间访问,因此这种方法可以实时执行包含数千个商业广告的数据库。在我们对63个广告的数据库进行的实验中,我们在广告播出的前1/2秒内识别广告,达到了96%的召回率,100%的准确率和98%的实用性。
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引用次数: 32
Registration of IR and EO Video Sequences based on Frame Difference 基于帧差的IR和EO视频序列配准
Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.56
Zheng Liu, R. Laganière
Multi-modal imaging sensors are employed in advanced surveillance systems in the recent years. The performance of surveillance systems can be enhanced by using information beyond the visible spectrum, for example, infrared imaging. To ensure correctness of low- or high-level processing, multi-modal imagers must be fully calibrated or registered. In this paper, an algorithm is proposed to register the video sequences acquired by an infrared and an electro-optical (CCD) camera. The registration method is based on the silhouette extracted by differencing adjacent frames. This difference is found by an image structural similarity measurement. Initial registration is implemented by tracing the top head points in consecutive frames. Finally, an optimization procedure to maximize mutual information is employed to refine the registration results.
近年来,多模态成像传感器被广泛应用于先进的监控系统中。监视系统的性能可以通过使用可见光谱以外的信息来增强,例如红外成像。为了确保低层或高层处理的正确性,必须对多模态成像仪进行完全校准或注册。本文提出了一种对红外摄像机和光电摄像机采集的视频序列进行配准的算法。该配准方法是基于相邻帧的差分提取轮廓。这种差异是通过图像结构相似性测量发现的。初始注册是通过跟踪连续帧中的顶头点来实现的。最后,采用互信息最大化的优化方法对配准结果进行细化。
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引用次数: 3
期刊
Fourth Canadian Conference on Computer and Robot Vision (CRV '07)
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