一种简易的线结构光投影三维形状获取方法

利用普通的LCD投影仪和数码相机获得物体的三维形状.投影仪作为光源,投射线结构光在物体表面,从数码相机拍摄的照片序列中,恢复物体的形状.提出了根据已经标定好的相机参数,继续对投影仪进行标定的方法;同时,针对物体表面颜色/纹理较为复杂的情况,提出了一次投射三色线结构光的改进方法,克服了不易恢复此类物体形状的困难.实验结果表明,该方法可以获得物体的稠密点云用于进一步的重建,适于组建一个廉价但却保持相对精度的扫描系统.     

Camera–projector matching using unstructured video

This paper presents a novel approach for matching 2-D points between a video projector and a digital camera. Our method is motivated by camera–projector applications for which the projected image needs to be warped to prevent geometric distortion. Since the warping process often needs geometric information on the 3-D scene obtained from a triangulation, we propose a technique for matching points in the projector to points in the camera based on arbitrary video sequences. The novelty of our method lies in the fact that it does not require the use of pre-designed structured light patterns as is usually the case. The backbone of our application lies in a function that matches activity patterns instead of colors. This makes our method robust to pose, severe photometric and geometric distortions. It also does not require calibration of the color response curve of the camera–projector system. We present quantitative and qualitative results with synthetic and real-life examples, and compare the proposed method with the scale invariant feature transform (SIFT) method and with a state-of-the-art structured light technique. We show that our method performs almost as well as structured light methods and significantly outperforms SIFT when the contrast of the video captured by the camera is degraded.     

基于HDR的投影机亮度曲线标定

多台投影机的无缝拼接校正过程主要是投影机的几何校正和颜色校正。多台投影机的颜色校正要求是求出每台投影机的颜色曲线,并对其颜色的相同输出做对应的输入使之达到全局的颜色统一。采用基于相机HDR的方法,先求出相机的亮度响应曲线,再以相机的亮度响应曲线为基础,使相机的输入为投影机的输出,间接求得投影机的亮度响应曲线。实验结果表明,基于HDR的亮度曲线测量,与直接使用照度计比较,结果相差在10%以内。     

基于BP神经网络的彩色温度软测量

基于高温物体的温度不同,与之相对应通过数码相机摄取的高温物体的颜色也不同,提出一种神经网络的图像颜色测温方法.选取RGB模型的R、G和B作为模式特征向量,用BP网络拟合高温物体的颜色和温度之间的非线性关系.实验结果表明,该方法精度高,运行速度快,切实可行.     

车牌识别中的颜色分析

车牌识别在交通系统中有着许多应用。用计算机和CCD摄像头识别出汽车牌照号码的车牌识别系统,已经广泛应用到高速公路不停车收费,车辆检测,停车场监控与管理,路面行驶车辆监控等犤1犦应用中。文章提出了一种在车牌识别中进行颜色分析的方法。这种颜色分析方法可以高效地去除错误的候选车牌区域。     

小样本红外图像的样本扩增与目标检测算法

深度卷积神经网络模型在很多公开的可见光目标检测数据集上表现优异, 但是在红外目标检测领域, 目标 样本稀缺一直是制约检测识别精度的难题. 针对该问题, 本文提出了一种小样本红外图像的样本扩增与目标检测算 法. 采用基于注意力机制的生成对抗网络进行红外样本扩增, 生成一系列保留原始可见光图像关键区域的红外连 续图像, 并且使用空间注意力机制等方法进一步提升YOLOv3目标检测算法的识别精度. 在Grayscale-Thermal与 OSU Color-Thermal红外–可见光数据集上的实验结果表明, 本文算法使用的红外样本扩增技术有效提升了深度网 络模型对红外目标检测的精度, 与原始YOLOv3算法相比, 本文算法最高可提升近20%的平均精确率(mean average precision, mAP).     

Fast Spectral Reflectance Recovery Using DLP Projector

Spectral reflectance is an intrinsic characteristic of objects that is independent of illumination and the used imaging sensors. This direct representation of objects is useful for various computer vision tasks, such as color constancy and material discrimination. In this work, we present a novel system for spectral reflectance recovery with high temporal resolution by exploiting the unique color-forming mechanism of digital light processing (DLP) projectors. DLP projectors use color wheels, which are composed of a number of color segments and rotate quickly to produce the desired colors. Making effective use of this mechanism, we show that a DLP projector can be used as a light source with spectrally distinct illuminations when the appearance of a scene under the projector’s irradiation is captured with a high-speed camera. Based on the measurements, the spectral reflectance of scene points can be recovered using a linear approximation of the surface reflectance. Our imaging system is built from off-the-shelf devices, and is capable of taking multi-spectral measurements as fast as 100 Hz. We carefully evaluated the accuracy of our system and demonstrated its effectiveness by spectral relighting of static as well as dynamic scenes containing different objects.     

Robust active stereo vision using Kullback-Leibler divergence

Active stereo vision is a method of 3D surface scanning involving the projecting and capturing of a series of light patterns where depth is derived from correspondences between the observed and projected patterns. In contrast, passive stereo vision reveals depth through correspondences between textured images from two or more cameras. By employing a projector, active stereo vision systems find correspondences between two or more cameras, without ambiguity, independent of object texture. In this paper, we present a hybrid 3D reconstruction framework that supplements projected pattern correspondence matching with texture information. The proposed scheme consists of using projected pattern data to derive initial correspondences across cameras and then using texture data to eliminate ambiguities. Pattern modulation data are then used to estimate error models from which Kullback-Leibler divergence refinement is applied to reduce misregistration errors. Using only a small number of patterns, the presented approach reduces measurement errors versus traditional structured light and phase matching methodologies while being insensitive to gamma distortion, projector flickering, and secondary reflections. Experimental results demonstrate these advantages in terms of enhanced 3D reconstruction performance in the presence of noise, deterministic distortions, and conditions of texture and depth contrast.     

Constrained pose refinement of parametric objects

Pose refinement is an essential task for computer vision systems that require the calibration and verification of model and camera parameters. Typical domains include the real-time tracking of objects and verification in model-based recognition systems. A technique is presented for recovering model and camera parameters of 3D objects from a single two-dimensional image. This basic problem is further complicated by the incorporation of simple bounds on the model and camera parameters and linear constraints restricting some subset of object parameters to a specific relationship. It is demonstrated in this paper that this constrained pose refinement formulation is no more difficult than the original problem based on numerical analysis techniques, including active set methods and lagrange multiplier analysis. A number of bounded and linearly constrained parametric models are tested and convergence to proper values occurs from a wide range of initial error, utilizing minimal matching information (relative to the number of parameters and components). The ability to recover model parameters in a constrained search space will thus simplify associated object recognition problems.     

Color-based underwater object recognition using water light attenuation

In this article we present a new approach for object recognition in a robotic underwater context. Color is an attractive feature because of its simplicity and its robustness to scale changes, object positions and partial occlusions. Unfortunately, in the underwater medium, the colors are modified by attenuation and are not constant with the distance. To perform a color-based recognition of an object, we develop an algorithm robust with respect to the attenuation which takes into account the light modification during its path between the light source and the camera. Therefore, a given underwater object can be identified in an image by detecting all the colors compatible with its prior known color. Our method is fast, robust and needs a very few computers resources. We successfully used it when experimenting in the sea using a system we built. It is suitable for robotic applications where computers resources are limited and shared between various embedded devices. This novel concept enables the use of the color in many applications such as target interception, object tracking or obstacle detection.     

Surround structured lighting: 3-D scanning with orthographic illumination

This paper presents a new system for rapidly acquiring complete 3-D surface models using a single orthographic structured light projector, a pair of planar mirrors, and one or more synchronized cameras. Using the mirrors, we project structured light patterns that illuminate the object from all sides (not just the side of the projector) and are able to observe the object from several vantage points simultaneously. This system requires that projected planes of light to be parallel, so we construct an orthographic projector using a Fresnel lens and a commercial DLP projector. A single Gray code sequence is used to encode a set of vertically-spaced light planes within the scanning volume, and five views of the illuminated object are obtained from a single image of the planar mirrors located behind it. From each real and virtual camera we recover a dense 3-D point cloud spanning the entire object surface using traditional structured light algorithms. A key benefit of this design is to ensure that each point on the object surface can be assigned an unambiguous Gray code sequence, despite the possibility of being illuminated from multiple directions. In addition to presenting a prototype implementation, we also develop a complete set of mechanical alignment and calibration procedures for utilizing orthographic projectors in computer vision applications. As we demonstrate, the proposed system overcomes a major hurdle to achieving full 360° reconstructions using a single structured light sequence by eliminating the need for merging multiple scans or multiplexing several projectors.     

一种基于条纹扫描的结构光三维信息获取方法

提出一种基于结构光的三维信息获取方法,利用投影仪向物体投射黑色条纹,移动的条纹扫描整个物体,根据条纹在物体上的变形来估计物体的三维形状,实现了三维信息的获取。该方法仅使用投影仪和数码相机等简单设备,实现简单快速,精度相对比较高,适用范围广,可扩展性强。     

Acoustic and video sensor integration for object recognition

Through our studies of methods for measuring the shapes of objects in three-dimensional object recognition, we have developed a method for constructing detailed solid object images that employs the fusion of the data from acoustic sensors and a charge-coupled device (CCD) camera. This method uses a matrix of ultrasonic sensors to obtain data on the position and height of the object. These data are used to automatically extract the two-dimensional images of the object from gray-scale camera images. By combining the results with distance information, a detailed solid image of the object is obtained. This method produces markedly better resolution than using acoustic data alone. Thus, by using it in combination with a neural network recognition mechanism, it is possible to automatically recognize small objects that are difficult to distinguish by means of acoustic sensing alone, even if they can be detected. This paper reports the newly developed sensor fusion mechanism, presents the results of experiments on an experimental system, and discusses the features of the method.     

Adaptive color segmentation-a comparison of neural and statisticalmethods

With the availability of more powerful computers it is nowadays possible to perform pixel based operations on real camera images even in the full color space. New adaptive classification tools like neural networks make it possible to develop special-purpose object detectors that can segment arbitrary objects in real images with a complex distribution in the feature space after training with one or several previously labeled image(s). The paper focuses on a detailed comparison of a neural approach based on local linear maps (LLMs) to a classifier based on normal distributions. The proposed adaptive segmentation method uses local color information to estimate the membership probability in the object, respectively, background class. The method is applied to the recognition and localization of human hands in color camera images of complex laboratory scenes.     

AIoU: Adaptive bounding box regression for accurate oriented object detection

Object detection consists of two key steps: class recognition and object localization. Class recognition is fundamental because the quality of the obtained feature representations is key to detection accuracy; for locating the object, bounding box refinement is the most intuitive method for improving the localization accuracy of the utilized detector; that is, selecting a better loss function metric when computing the best-fitted bounding box for the object of interest. However, current class activation mapping (CAM) scores cannot help effectively distinguish the object from background noise and involve fixed weights for the geometric characteristics of the anchor box, leading to inaccurate object detection. In this paper, we proposed a mixed-CAM method to obtain improved category scores for class recognition, and an adaptive intersection-over-union method (AIoU) that improves the localization performance for object detection. The mixed-CAM method combines an original image response and CAM information to provide a confidence score for the final feature map and, in the meantime, considers this score as a sample selection criterion for the following localization regression stage. The AIoU method designs a new loss function metric for bounding box localization regression. In doing so, the proposed method considers the weight of each geometric characteristic of the bounding box in the network training process via a hyperparameter and adopts a new positive and negative sample selection mechanism for sample training. Experimental results show that the proposed framework achieves better prediction accuracy and a higher average precision value than those yielded by the classical backbone networks. Moreover, the AIoU method can be easily coupled with existing convolutional neural network architectures and thus possesses the great potential of adaptability in many application fields, such as intelligent transportation.     

Practical Radiometric Compensation for Projection Display on Textured Surfaces using a Multidimensional Model

Radiometric compensation methods remove the effect of the underlying spatially varying surface reflectance of the texture when projecting on textured surfaces. All prior work sample the surface reflectance dependent radiometric transfer function from the projector to the camera at every pixel that requires the camera to observe tens or hundreds of images projected by the projector. In this paper, we cast the radiometric compensation problem as a sampling and reconstruction of multi‐dimensional radiometric transfer function that models the color transfer function from the projector to an observing camera and the surface reflectance in a unified manner. Such a multi‐dimensional representation makes no assumption about linearity of the projector to camera color transfer function and can therefore handle projectors with non‐linear color transfer functions(e.g. DLP, LCOS, LED‐based or laser‐based). We show that with a well‐curated sampling of this multi‐dimensional function, achieved by exploiting the following key properties, is adequate for its accurate representation: (a) the spectral reflectance of most real‐world materials are smooth and can be well‐represented using a lower‐dimension function; (b) the reflectance properties of the underlying texture have strong redundancies – for example, multiple pixels or even regions can have similar surface reflectance; (c) the color transfer function from the projector to camera have strong input coherence. The proposed sampling allows us to reduce the number of projected images that needs to be observed by a camera by up to two orders of magnitude, the minimum being only two. We then present a new multi‐dimensional scattered data interpolation technique to reconstruct the radiometric transfer function at a high spatial density (i.e. at every pixel) to compute the compensation image. We show that the accuracy of our interpolation technique is higher than any existing methods.     

基于深度卷积神经网络的物体识别算法

针对传统物体识别算法中人工设计出来的特征易受物体形态多样性、光照和背景的影响,提出了一种基于深度卷神经网络的物体识别算法。该算法基于NYU Depth V2场景数据库,首先将单通道深度信息转换为三通道;再用训练集中的彩色图片和转换后的三通道深度图片分别微调两个深度卷积神经网络模型;然后用训练好的模型对重采样训练集中的彩色和深度图片提取模型第一个全连接层的特征,并将两种模态的特征串联起来,训练线性支持向量机（LinSVM）;最后将所提算法应用到场景理解任务中的超像素特征提取。所提方法在测试集上的物体分类准确度可达到91.4%,比SAE-RNN方法提高4.1个百分点。实验结果表明所提方法可提取彩色和深度图片高层特征,有效提高物体分类准确度。     

基于机器视觉的动态多点手势识别方法

提出了一种高效的基于HSV颜色空间的多目标检测跟踪方法,实现通过摄像机实时检测跟踪多个指尖目标;定义了一套基于指尖运动轨迹的动态手势模型,并提出了动态手势识别方法;对于两点动态手势,通过BP神经网络进行手势学习和手势识别,而对于模拟鼠标手势和四点动态手势,利用指尖之间相互位置关系进行手势识别.测试结果表明,该方法能够快速、准确的跟踪多个运动的指尖目标并进行动态多点手势识别.     

面向交叉点网络的符号解码方法的研究

计算机视觉是近年来研究的热点,结构光视觉是双目计算机视觉的一个分支,该技术采用一台投射器代替双目立体视觉的一台摄像机,由于其投射的结构光具有编码唯一性的特点,从而解决了双目视觉中像素匹配的难题。结构光技术中解码成为了一项重要的工作,根据符号的特点提出一种基于交叉点网络的符号解码方法。该方法有效地实现了交叉点的提取,并形成全局的交叉点网络。针对细化的十字交叉变形,研究了交叉点合并方法,提出了基于识圈法的内部交叉点定位方法,以及基于向量角度的符号分类方法,有效地实现了符号码字的还原。最终实现物体的三维重建目标。