Spectral 3D mesh segmentation with a novel single segmentation field

We present an automatic mesh segmentation framework that achieves 3D segmentation in two stages, hierarchical spectral analysis and isoline-based boundary detection. During the hierarchical spectral analysis stage, a novel segmentation field is defined to capture a concavity-aware decomposition of eigenvectors from a concavity-aware Laplacian. Specifically, a sufficient number of eigenvectors is first adaptively selected and simultaneously partitioned into sub-eigenvectors through spectral clustering. Next, on the sub-eigenvectors level, we evaluate the confidence of identifying a spectral-sensitive mesh boundary for each sub-eigenvector by two joint measures, namely, inner variations and part oscillations. The selection and combination of sub-eigenvectors are thereby formulated as an optimization problem to generate a single segmentation field. In the isoline-based boundary detection stage, the segmentation boundaries are recognized by a divide-merge algorithm and a cut score, which respectively filters and measures desirable isolines from the concise single segmentation field. Experimental results on the Princeton Segmentation Benchmark and a number of other complex meshes demonstrate the effectiveness of the proposed method, which is comparable to recent state-of-the-art algorithms.     

ROI Scissor: Interactive Segmentation of Feature Region of Interest in a Triangular Mesh

We present a simple and effective method for the interactive segmentation of feature regions in a triangular mesh. From the user-specified radius and click position, the candidate region that contains the desired feature region is defined as geodesic disc on a triangle mesh. A concavity-aware harmonic field is then computed on the candidate region using the appropriate boundary constraints. An initial isoline is chosen by evaluating the uniformly sampled ones on the harmonic field based on the gradient magnitude. A set of feature points on the initial isoline is selected and the anisotropic geodesics passing through them are then determined as the final segmentation boundary, which is smooth and locally shortest. The experimental results show several segmentation results for various 3D models, revealing the effectiveness of the proposed method.     

Mesh Decomposition with Cross‐Boundary Brushes

We present a new intuitive UI, which we call cross‐boundary brushes, for interactive mesh decomposition. The user roughly draws one or more strokes across a desired cut and our system automatically returns a best cut running through all the strokes. By the different natures of part components (i.e., semantic parts) and patch components (i.e., flatter surface patches) in general models, we design two corresponding brushes: part‐brush and patch‐brush. These two types of brushes share a common user interface, enabling easy switch between them. The part‐brush executes a cut along an isoline of a harmonic field driven by the user‐specified strokes. We show that the inherent smoothness of the harmonic field together with a carefully designed isoline selection scheme lead to segmentation results that are insensitive to noise, pose, tessellation and variation in user's strokes. Our patch‐brush uses a novel facet‐based surface metric that alleviates sensitivity to noise and fine details common in region‐growing algorithms. Extensive experimental results demonstrate that our cutting tools can produce user‐desired segmentations for a wide variety of models even with single strokes. We also show that our tools outperform the state‐of‐art interactive segmentation tools in terms of ease of use and segmentation quality.     

基于SIFT特征和图割算法的图像分割方法研究

图割算法是图像分割方法中的一种高效的最优化计算方法,针对图像中目标物体的旋转尺度光照变化导致的分割不准确问题,提出了一种基于SIFT(Scale-Invariant Feature Transform)特征的图割(Graph Cuts)算法;该方法将SIFT特征的尺度旋转不变性和图割算法的准确快速性结合在一起,通过提取图像中物体SIFT特征点做为图割算法的种子点,求解最小能量函数快速从而获得该图像的最优分割;实验结果表明,该方法鲁棒性较好,能准确地分割出目标物体在图像中的轮廓。     

Mesh segmentation with concavity-aware fields

This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme. Isolines sampled from the concavity-aware fields naturally gather at concave seams, serving as good cutting boundary candidates. In addition, the fields provide sufficient information allowing efficient evaluation of the candidate cuts. We perform a summarization of all field gradient magnitudes to define a score for each isoline and employ a score-based greedy algorithm to select the best cuts. Extensive experiments and quantitative analysis have shown that the quality of our segmentations are better than or comparable with existing state-of-the-art more complex approaches.     

基于保特征调和场的交互式网格分片

网格模型分片在计算机图形学应用中具有重要意义,本文提出了一种基于网格上的调和场和图割技术的网格模型分片算法.用户可以通过划线的方式来指定网格上感兴趣的区域;算法自动构建反映该区域细节特征的调和场,进而采用图割技术,得到满足用户要求的分片结果;通过对网格分割边界的光滑处理,可有效改善锯齿型分割边界.实验结果表明,我们的算法对于特征单一或复杂的网格模型都能得到符合用户意图的分割结果.     

结合纹理与极化分解的面向对象极化SAR水体提取方法

合成孔径雷达(Synthetic Aperture Radar,SAR)拥有全天时全天候的工作能力,能够有效地连续对地观测,是土地管理、水体监测、灾害评估等多种应用的稳定数据来源。基于面向对象的思想,提出一种高精度、低虚警率的极化SAR(Polarimetric SAR,PolSAR)水体提取方法。此方法首先对极化SAR图像进行分割,再结合纹理与极化分解特征,对分割区域进行投票,识别水体区域。利用Radarsat-2数据和TerraSAR-X数据开展实验,并将提出方法与基于单一纹理和基于极化分解等水体提取方法进行对比,结果表明该方法在两种数据中均具有最高的总分类精度,其中基于分割技术能够保持完整的水陆边界,纹理与极化特征能够区分浅草、裸地和阴影等与水体相似的地物,结合投票方法能够提高小型水体检测率。     

An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds

Grouping processes, which “organize” a given data by eliminating the irrelevant items and sorting the rest into groups, each corresponding to a particular object, can provide reliable pre-processed information to higher level computer vision functions, such as object detection and recognition. In this paper, we consider the problem of grouping oriented segments in highly cluttered images. In this context, we have developed a general and powerful method based on an iterative, multiscale tensor voting approach. Segments are represented as second-order tensors and communicate with each other through a voting scheme that incorporates the Gestalt principles of visual perception. The key idea of our approach is removing background segments conservatively on an iterative fashion, using multi-scale analysis, and re-voting on the retained segments. We have performed extensive experiments to evaluate the strengths and weaknesses of our approach using both synthetic and real images from publicly available datasets including the Williams and Thornber’s fruit-texture dataset [L. Williams, Fruit and texture images. Available from: <http://www.cs.unm.edu/~williams/saliency.html>, 2008 (last viewed in July 2008)] and the Berkeley segmentation dataset [C.F.P. Arbelaez, D. Martin, The berkeley segmentation dataset and benchmark. Available from: <http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/>, 2008 (last viewed in July 2008)]. Our results and comparisons indicate that the proposed method improves segmentation results considerably, especially under severe background clutter. In particular, we show that using the iterative multiscale tensor voting approach to post-process the posterior probability map, produced by segmentation methods, improves boundary detection results in 84% of the grayscale test images in the Berkeley segmentation benchmark.     

Segmentation of discrete vector fields

In this paper, we propose an approach for 2D discrete vector field segmentation based on the Green function and normalized cut. The method is inspired by discrete Hodge decomposition such that a discrete vector field can be broken down into three simpler components, namely, curl-free, divergence-free, and harmonic components. We show that the Green function method (GFM) can be used to approximate the curl-free and the divergence-free components to achieve our goal of the vector field segmentation. The final segmentation curves that represent the boundaries of the influence region of singularities are obtained from the optimal vector field segmentations. These curves are composed of piecewise smooth contours or streamlines. Our method is applicable to both linear and nonlinear discrete vector fields. Experiments show that the segmentations obtained using our approach essentially agree with human perceptual judgement.     

Low-complexity progressive image transmission scheme based on quadtree segmentation

A novel progressive image transmission scheme based on the quadtree segmentation technique is introduced in this paper. A 3-level quadtree is used in the quadtree segmentation technique to partition the original image into blocks of different sizes. Image blocks of different sizes are encoded by their block mean values. The relatively addressing technique is employed to cut down the storage cost of block mean values.In the proposed scheme, the number of image hierarchies can be adaptively selected according to the specific applications. By exploiting inter-pixel correlation and differently sized blocks for segmentation, the proposed scheme provides good image qualities at low bit rates and consumes very little computational cost in both image encoding and decoding procedures. It is quite suitable for real-time progressive image transmission.     

Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings

Edge preserving smoothing and image simplification is of fundamental importance in a variety of remote sensing applications during feature extraction and object detection procedures. The construction of a pre‐processing filtering tool for edge detection and segmentation tasks is still an open matter. Towards this end, this paper brings together two advanced nonlinear scale space representations, anisotropic diffusion filtering and morphological levellings, forming a processing scheme by their combination. The proposed scheme was applied to edge detection and watershed segmentation tasks. The experimental results showed that the developed scheme generated an effective pre‐processing tool for automatic olive tree detection and solving watershed over‐segmentation problems.     

Real-time line detection through an improved Hough transform voting scheme

The Hough transform (HT) is a popular tool for line detection due to its robustness to noise and missing data. However, the computational cost associated to its voting scheme has prevented software implementations to achieve real-time performance, except for very small images. Many dedicated hardware designs have been proposed, but such architectures restrict the image sizes they can handle. We present an improved voting scheme for the HT that allows a software implementation to achieve real-time performance even on relatively large images. Our approach operates on clusters of approximately collinear pixels. For each cluster, votes are cast using an oriented elliptical-Gaussian kernel that models the uncertainty associated with the best-fitting line with respect to the corresponding cluster. The proposed approach not only significantly improves the performance of the voting scheme, but also produces a much cleaner voting map and makes the transform more robust to the detection of spurious lines.     

融合时空多特征表示的无监督视频分割算法

针对视频分割的难点在于分割目标的无规则运动、快速变换的背景、目标外观的任意变化与形变等,提出了一种基于时空多特征表示的无监督视频分割算法,通过融合像素级、超像素级以及显著性三类特征设计由细粒度到粗粒度的稳健特征表示。首先,采用超像素分割对视频序列进行处理以提高运算效率,并设计图割算法进行快速求解;其次,利用光流法对相邻帧信息进行匹配,并通过K-D树算法实现最近邻搜索以引入各超像素的非局部时空颜色特征,从而增强分割的鲁棒性;然后,对采用超像素计算得到的分割结果,设计混合高斯模型进行完善;最后,引入图像的显著性特征,协同超像素分割与混合高斯模型的分割结果,设计投票获得更加准确的视频分割结果。实验结果表明,所提算法是一种稳健且有效的分割算法,其结果优于当前大部分无监督视频分割算法及部分半监督视频分割算法。     

基于SEAL库的同态加权电子投票系统

电子投票比传统纸质投票更为灵活高效,能节省大量人力物力,在不同选举场合中的地位越来越重要.同态加密技术可以在电子投票过程中发挥重要作用,同态加密技术结合其他安全技术和手段来设计的电子投票系统,可以在计票过程中有效保护投票者的身份隐私,相比其他类型的电子投票系统也更为简洁高效.为了解决电子投票中的身份隐私保护和实现效率问题,本文提出了一种基于SEAL库的同态加权电子投票系统,通过同态操作实现密文计票,可有效抵抗来自计票中心内部的恶意攻击,保证选票保密性和计票结果正确性;通过在选票中引入投票权重,可以使电子投票系统实现加权投票;通过将选票信息密态存储在云端数据库,将计票中心部署在云端,可在保证安全的前提下借助云计算服务实现高效计票;系统中加密算法的安全性基于格上RLWE困难问题,可以抵抗量子计算攻击.对所设计投票系统的效率测试表明,完成对1000张选票的计票工作仅耗时1.867 s,相比Will等人在ICCCRI2015中提出的基于Paillier的电子投票系统计票耗时减少了32.73%,相比Wang等人在2017年提出的基于Helib的电子投票系统计票耗时减少了99.26%,相比Li在2017年提出的基于Helib的电子投票系统计票耗时减少了91.81%.本文提出的同态加权电子投票系统可以满足多个候选人投票和加权投票,能够适用于多种投票场景,且计票效率可以满足大规模投票的应用需求.     

Space‐Time Co‐Segmentation of Articulated Point Cloud Sequences

Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter‐frame variation across the whole sequence. We propose a local‐to‐global approach to co‐segment point cloud sequences of articulated objects into near‐rigid moving parts. Our method starts from a per‐frame point clustering, derived from a robust voting‐based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space‐time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co‐segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.     

On improving the efficiency of tensor voting

This paper proposes two alternative formulations to reduce the high computational complexity of tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. The first scheme consists of numerical approximations of the votes, which have been derived from an in-depth analysis of the plate and ball voting processes. The second scheme simplifies the formulation while keeping the same perceptual meaning of the original tensor voting: The stick tensor voting and the stick component of the plate tensor voting must reinforce surfaceness, the plate components of both the plate and ball tensor voting must boost curveness, whereas junctionness must be strengthened by the ball component of the ball tensor voting. Two new parameters have been proposed for the second formulation in order to control the potentially conflictive influence of the stick component of the plate vote and the ball component of the ball vote. Results show that the proposed formulations can be used in applications where efficiency is an issue since they have a complexity of order O(1). Moreover, the second proposed formulation has been shown to be more appropriate than the original tensor voting for estimating saliencies by appropriately setting the two new parameters.     

面向语义分割模型的外接多尺度投票网络

针对语义分割领域中多尺度共享网络训练复杂度高,以及网络在小目标、长条状目标、目标边缘处拟合效果不佳的问题,提出一种新型外接多尺度投票网络。通过投票网络融合各尺度分割结果,降低网络训练复杂度,并将共享网络中的分割网络与各尺度注意力头剥离开,仅训练各尺度注意力头,以便于网络收敛。在投票网络的结构设计中,使用多类别投票方法扩大投票空间,通过融入混合池化模块聚合近程与远程权值,扩大网络感受野,缓解权值图中长条状目标拟合间断与缺失的问题。在此基础上引入类内、类间投票注意力模块获取权值及类间关系,并采用不规则卷积,改善投票权值图的边缘拟合效果。在Cityscapes数据集上的实验结果表明,相比FCN、PSPNet、DeepLabv3+分割网络,该网络的平均交并比分别提升了0.92、0.88、0.80个百分点,与共享网络相比,其训练复杂度更低,精度更高。     

Color image segmentation using tensor voting based color clustering

A novel color image segmentation method using tensor voting based color clustering is proposed. By using tensor voting, the number of dominant colors in a color image can be estimated efficiently. Furthermore, the centroids and structures of the color clusters in the color feature space can be extracted. In this method, the color feature vectors are first encoded by second order, symmetric, non-negative definite tensors. These tensors then communicate with each other by a voting process. The resulting tensors are used to determine the number of clusters, locations of the centroids, and structures of the clusters used for performing color clustering. Our method is based on tensor voting, a non-iterative method, and requires only the voting range as its input parameter. The experimental results show that the proposed method can estimate the dominant colors and generate good segmented images in which those regions having the same color are not split up into small parts and the objects are separated well. Therefore, the proposed method is suitable for many applications, such as dominant colors estimation and multi-color text image segmentation.     

Fast and robust road sign detection in driver assistance systems

Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.     

Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.