基于目标特征的植株深度图像修复

针对植株深度图像的像素错误和缺失、常见的滤波方法无法准确修复植株深度图 像的问题,提出一种基于目标特征的植株深度图像修复方法。首先基于颜色和空间信息的图像 分割算法对植株彩色图像进行目标分割,再检索每个目标的外轮廓,并对外轮廓进行多边形拟 合;其次,基于目标区域搜索深度图像中具有正确深度值的像素作为目标区域采样点,并对叶 片区域的图像进行归一化;最后,利用空间拟合法计算各目标区域的方程,修复区域内小面积 错误和缺失的深度值,同时采用支持向量机和空间变换运算对大面积错误和缺失深度值的叶片 区域进行修复。实验结果表明,该方法能够准确地修复植株深度图像中错误、缺失的深度数据, 且能够有效地保护目标区域的边缘信息。     

Extracting View-Dependent Depth Maps from a Collection of Images

Stereo correspondence algorithms typically produce a single depth map. In addition to the usual problems of occlusions and textureless regions, such algorithms cannot model the variation in scene or object appearance with respect to the viewing position. In this paper, we propose a new representation that overcomes the appearance variation problem associated with an image sequence. Rather than estimating a single depth map, we associate a depth map with each input image (or a subset of them). Our representation is motivated by applications such as view interpolation and depth-based segmentation for model-building or layer extraction. We describe two approaches to extract such a representation from a sequence of images.The first approach, which is more classical, computes the local depth map associated with each chosen reference frame independently. The novelty of this approach lies in its combination of shiftable windows, temporal selection, and graph cut optimization. The second approach simultaneously optimizes a set of self-consistent depth maps at multiple key-frames. Since multiple depth maps are estimated simultaneously, visibility can be modeled explicitly and disparity consistency imposed across the different depth maps. Results, which include a difficult specular scene example, show the effectiveness of our approach.     

从同心拼图中恢复深度信息

同心拼图（Concentric Mosaics,CM)是一种重要的基于图像的绘制方法。如果利用深度信息则可以进一步提高绘制质量并减少数据量。作者发现在CM序列中也存在着近拟的极线平面图像（Epipolar Plane Image,EPI）,而且像点在EPI图像上的轨迹斜率和物点的深度呈近似线性关系。基于此发现,该文提出了一种从CM序列中自动动恢复深度信息的方法。这个方法首先利用EPI图像的频谱分析对场景的深度分布范围做出估计,然后根据全光采样的原理在EPI的斜率空间均匀的分割投票箱,并对给定窗口内频谱能量进行投票,以求得能量的最大方向,从而获得这个窗口所对应的深度,最后组合成CM的场景深度图,实验结果证实了上述方法的有效性。     

Unsupervised Scale-Consistent Depth Learning from Video

International Journal of Computer Vision - We propose a monocular depth estimation method SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent...     

Consistent Shape Maps via Semidefinite Programming

Recent advances in shape matching have shown that jointly optimizing the maps among the shapes in a collection can lead to significant improvements when compared to estimating maps between pairs of shapes in isolation. These methods typically invoke a cycle‐consistency criterion — the fact that compositions of maps along a cycle of shapes should approximate the identity map. This condition regularizes the network and allows for the correction of errors and imperfections in individual maps. In particular, it encourages the estimation of maps between dissimilar shapes by compositions of maps along a path of more similar shapes. In this paper, we introduce a novel approach for obtaining consistent shape maps in a collection that formulates the cycle‐consistency constraint as the solution to a semidefinite program (SDP). The proposed approach is based on the observation that, if the ground truth maps between the shapes are cycle‐consistent, then the matrix that stores all pair‐wise maps in blocks is low‐rank and positive semidefinite. Motivated by recent advances in techniques for low‐rank matrix recovery via semidefinite programming, we formulate the problem of estimating cycle‐consistent maps as finding the closest positive semidefinite matrix to an input matrix that stores all the initial maps. By analyzing the Karush‐Kuhn‐Tucker (KKT) optimality condition of this program, we derive theoretical guarantees for the proposed algorithm, ensuring the correctness of the recovery when the errors in the inputs maps do not exceed certain thresholds. Besides this theoretical guarantee, experimental results on benchmark datasets show that the proposed approach outperforms state‐of‐the‐art multiple shape matching methods.     

视频图象序列的实时镶嵌技术研究

阐述了一种快速而高效的由视频图象或视频图象序列生成全景的配准方法,为了估计图象配准的校正参数,该方法计算伪运动矢量,这些伪运动矢量是光流在每一选定象素处的粗略估计,使用方法,实现了一个在低价PC上就能实时创建和显示全景图像的软件。     

Fast, On-Line Learning of Globally Consistent Maps

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.     

Consistent Video Filtering for Camera Arrays

Visual formats have advanced beyond single‐view images and videos: 3D movies are commonplace, researchers have developed multi‐view navigation systems, and VR is helping to push light field cameras to mass market. However, editing tools for these media are still nascent, and even simple filtering operations like color correction or stylization are problematic: naively applying image filters per frame or per view rarely produces satisfying results due to time and space inconsistencies. Our method preserves and stabilizes filter effects while being agnostic to the inner working of the filter. It captures filter effects in the gradient domain, then uses input frame gradients as a reference to impose temporal and spatial consistency. Our least‐squares formulation adds minimal overhead compared to naive data processing. Further, when filter cost is high, we introduce a filter transfer strategy that reduces the number of per‐frame filtering computations by an order of magnitude, with only a small reduction in visual quality. We demonstrate our algorithm on several camera array formats including stereo videos, light fields, and wide baselines.     

Space-Variant Approaches to Recovery of Depth from Defocused Images

The recovery of depth from defocused images involves calculating the depth of various points in a scene by modeling the effect that the focal parameters of the camera have on images acquired with a small depth of field. In the approach to depth from defocus (DFD), previous methods assume the depth to be constant over fairly large local regions and estimate the depth through inverse filtering by considering the system to be shift-invariant over those local regions. But a subimage when analyzed in isolation introduces errors in the estimate of the depth. In this paper, we propose two new approaches for estimating the depth from defocused images. The first approach proposed here models the DFD system as a block shift-variant one and incorporates the interaction of blur among neighboring subimages in an attempt to improve the estimate of the depth. The second approach looks at the depth from defocus problem in the space-frequency representation framework. In particular, the complex spectrogram and the Wigner distribution are shown to be likely candidates for recovering the depth from defocused images. The performances of the proposed methods are tested on both synthetic and real images. The proposed methods yield good results and the quality of the estimates obtained using these methods is compared with the existing method.     

Articulated Object Reconstruction and Markerless Motion Capture from Depth Video

We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise‐rigid object using a single depth video camera. The algorithm identifies and tracks the rigid components in each frame, while accumulating the geometric information acquired over time, possibly from different viewpoints. The algorithm also reconstructs the dynamic skeleton of the object, thus can be used for markerless motion capture. The acquired model can then be animated to novel poses. We show the results of the algorithm applied to synthetic and real depth video.     

Obstacle-Free Pathway Detection by Means of Depth Maps

The detection of surrounding obstacle-free space is an essential task for many intelligent automotive and robotic applications. In this paper we present a method to detect obstacle-free pathways in real-time using depth maps from a pair of stereo images. Depth maps are obtained by processing the disparity between left and right images from a stereo-vision system. The proposed technique assumes that depth of pixels in obstacle-free pathways should increase slightly and linearly from the bottom of the image to the top. The proposed real-time detection checks whether the depth of groups of image columns matches a linear model. Only pixels fulfilling the matching requirements are identified as obstacle-free pathways. Experimental results with real outdoor stereo images show that the method performance is promising.     

Test-Time Optimization for Video Depth Estimation Using Pseudo Reference Depth

In this paper, we propose a learning-based test-time optimization approach for reconstructing geometrically consistent depth maps from a monocular video. Specifically, we optimize an existing single image depth estimation network on the test example at hand. We do so by introducing pseudo reference depth maps which are computed based on the observation that the optical flow displacement for an image pair should be consistent with the displacement obtained by depth-reprojection. Additionally, we discard inaccurate pseudo reference depth maps using a simple median strategy and propose a way to compute a confidence map for the reference depth. We use our pseudo reference depth and the confidence map to formulate a loss function for performing the test-time optimization in an efficient and effective manner. We compare our approach against the state-of-the-art methods on various scenes both visually and numerically. Our approach is on average 2.5× faster than the state of the art and produces depth maps with higher quality.     

Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles

We propose a depth and image scene flow estimation method taking the input of a binocular video. The key component is motion-depth temporal consistency preservation, making computation in long sequences reliable. We tackle a number of fundamental technical issues, including connection establishment between motion and depth, structure consistency preservation in multiple frames, and long-range temporal constraint employment for error correction. We address all of them in a unified depth and scene flow estimation framework. Our main contributions include development of motion trajectories, which robustly link frame correspondences in a voting manner, rejection of depth/motion outliers through temporal robust regression, novel edge occurrence map estimation, and introduction of anisotropic smoothing priors for proper regularization.     

基于视频序列的平面海报生成方法

视频摘要是海量视频浏览的重要手段,现有的方法一般生成短帧视频或多帧序列图像以概括原视频,但它们都受限于原有时间序列,难以高效地表达信息.为此,提出了一种视频海报的自动生成方法来制作更为精练的视频摘要.如何提取视频中的关键画面与如何实现海报自动排版是其中的2个核心问题.对现有的视频关键帧提取方法进行扩展,采用综合视觉关注度模型,提出了基于视觉重要性的关键帧排序算法;在现有排版规则基础上,增加了版面位置对视觉心理感知的影响,设计出位置重要性驱动的视频海报自动排版算法.实验结果证明了文中算法的有效性.     

Displacement Following of Hidden Objects in a Video Sequence

In a video sequence, computing the motion of an object requires the continuity of the apparent velocity field. This property does not hold when the object is hidden by an occlusion during its motion. The minimization of an energy functional leads to a simple algorithm which allows the recovery of the most likely trajectory of the occluded object from optical flow data at the border of the occlusion. Optical flow used for developing our method is an improvement on any variational technique of computing it. This improvement is based on a multichannel segmentation.     

基于Kinect深度数据的视频艺术化处理

利用深度摄像机Kinect for XBOX360提取视频的深度信息来实现视频前景和背景的分离,并分别对视频前景和背景进行多风格的艺术渲染,从而获取更好的视频风格化定制效果。首先,系统利用Kinect深度数据实现视频前景的提取;然后在光流场指导下,利用基于纹理传输的方法对视频前景和背景进行不同风格的艺术化渲染;最后,将风格化后的前景视频和背景视频进行融合,从而得到最终的风格化艺术视频。另外,由于采用纹理传输的方式实现对视频的艺术化处理,因此用户可以选择不同的纹理样本来实现自定义的多风格艺术渲染。经过实验测试,前景和背景视频融合后生成的风格化视频取得了较好的艺术效果,从而证明了该系统具有较好的视频前景提取能力和视频风格化渲染能力。     

基于形变模型由立体序列图象恢复物体的3D形状

结合立体视觉和形变模型提出了一种新的物体3D形状的恢复方法。采用立体视觉方法导出物体表面的3D坐标;利用光流模型估计物体的3D运动,根据此运动移动形变模型,使其对准物体的表面块;由形变模型将由各幅图象得到的离散的3D点融为一起,得到物体的表面形状。实验结果表明该方法能用于形状复杂的物体恢复。     

Temporally Consistent Motion Segmentation From RGB‐D Video

Temporally consistent motion segmentation from RGB‐D videos is challenging because of the limitations of current RGB‐D sensors. We formulate segmentation as a motion assignment problem, where a motion is a sequence of rigid transformations through all frames of the input. We capture the quality of each potential assignment by defining an appropriate energy function that accounts for occlusions and a sensor‐specific noise model. To make energy minimization tractable, we work with a discrete set instead of the continuous, high dimensional space of motions, where the discrete motion set provides an upper bound for the original energy. We repeatedly minimize our energy, and in each step extend and refine the motion set to further lower the bound. A quantitative comparison to the current state of the art demonstrates the benefits of our approach in difficult scenarios.     

一种利用整体变分的深度恢复算法

由散焦图像恢复三维景物的深度信息是一个不适定问题.提出一种新的基于整体变分的散焦图像深度恢复算法:首先将散焦图像深度恢复转化为带有整体变分正则化项的能量泛函极值问题,然后采用变分原理将其中的最小化问题转为偏微分方程的求解,最后通过方程迭代获得深度的最优解.该算法避免了解不适定问题的逆,恢复聚焦图像等问题.模拟图像和真实图像的实验结果表明该算法是有效的,与最小二乘法相比具有较小的均方根误差.