Mean shift denoising of point-sampled surfaces

This paper presents an anisotropic denoising/smoothing algorithm for point-sampled surfaces. Motivated by the impressive results of mean shift filtering on image denoising, we extend the concept to 3D surface smoothing by taking the vertex normal and the curvature as the range component and the vertex position as the spatial component. Then the local mode of each vertex on point-based surfaces is computed by a 3D mean shift procedure dependent on local neighborhoods that are adaptively obtained by a kdtree data structure. Clustering pieces of point-based surfaces of similar local mode provides a meaningful model segmentation. Based on the adaptively clustered neighbors, we finally apply a trilateral point filtering scheme that adjusts the position of sample points along their normal directions to successfully reduce noise from point-sampled surfaces while preserving geometric features.     

Differentials-Based Segmentation and Parameterization for Point-Sampled Surfaces

Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a differentialslbased segmentation and parameterization approach is proposed in this paper. Our approach partitions the point-sampled geometry based on two criteria： variation of Euclidean distance between sample points, and angular difference between surface differential directions. According to the analysis of normal curvatures for some specified directions, a new projection approach is adopted to estimate the local surface differentials. Then a k-means clustering （k-MC） algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes. Finally, each chart is parameterized with a statistical method -- multidimensional scaling （MDS） approach, and the parameterization results of all charts form an atlas for compact storage.     

基于球面参数化的点模型渐变

为了获得光滑自然的点模型渐变效果,基于球面参数化,提出了一种鲁棒的渐变算法。该算法首先对源和目标模型进行球面参数化,使得参数化后的模型嵌入到单位球面上;然后在球面上自适应地对齐模型间的相应特征点,并将球面映射到矩形参数域上,基于该域建立模型间各采样点的对应关系;接着在渐变过程中,采用拉普拉斯算子计算出中间点模型的几何位置,以保持模型的细节;最后利用移动最小二乘曲面进行动态上采样,以消除中间模型的裂缝。实验结果表明,该算法具有良好匹配的采样点对应和光滑的渐变过程。     

A quasi-Monte Carlo method for computing areas of point-sampled surfaces

A novel and efficient quasi-Monte Carlo method for computing the area of a point-sampled surface with associated surface normal for each point is presented. Our method operates directly on the point cloud without any surface reconstruction procedure. Using the Cauchy-Crofton formula, the area of the point-sampled surface is calculated by counting the number of intersection points between the point cloud and a set of uniformly distributed lines generated with low-discrepancy sequences. Based on a clustering technique, we also propose an effective algorithm for computing the intersection points of a line with the point-sampled surface. By testing on a number of point-based models, experiments suggest that our method is more robust and more efficient than those conventional approaches based on surface reconstruction.     

EWA splatting

We present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's (1989) EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.     

Performance Modeling and Algorithm Characterization for Robust Image Segmentation

This paper presents a probabilistic framework based on Bayesian theory for the performance prediction and selection of an optimal segmentation algorithm. The framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm. The input image information content is measured in terms of image features while the candidate segmentation algorithm’s behavioral characteristics are captured through the use of segmentation quality features. Gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the Berkeley Segmentation Dataset using four candidate segmentation algorithms.     

三维离散点采样表面基于全局优化的纹理合成算法

提出了一个直接在三维离散点采样表面上进行基于全局优化的纹理合成算法．算法首先对点模型表面进行基于协方差分析的递归式聚类削分，然后通过对各初始聚类进行优化调整和区域生长，得到更为均匀的计算单元块且使相邻单元块间具有重叠区域．基于所得到的计算单元块和样本纹理，在点模型表面上建立了一个全局的纹理合成能量方程，并采用最大期卑值算法迭代优化求解出一个全局的能量最小值．在全局优化算法中，通过加入的约束条件，进而给出了点模型颜色纹理的修复算法和流引导的可控纹理合成算法．由于点模型表面离散点元的稠密性，通过对离散点元的法向基于纹理合成结果进行相应的扰动，进一步增强了表面纹理的几何粗糙感．实验结果表明，该文的算法在点采样几何表面上有效地保持了生成的纹理结构和视觉效果的连续性和平滑性，得到了令人满意的结果。     

点模型上的纹理快速生成算法

该算法摈弃了传统的将三维点模型三角面片化的繁琐过程,直接在三维点模型上进行纹理合成。该方法建立三维点模型上各离散点之间的邻域关系,在点模型上建立光顺的方向场,控制模型表面纹理合成的方向。以方向场为基础,建立三维模型与样图的映射关系,实现纹理合成。采用不同大小、不同纹理结构的样图,在点模型上进行三维纹理合成实验。实验结果表明,与传统的基于网格模型的三维纹理合成方法相比,此方法在保证纹理合成质量的同时,大大提高了合成速度。     

Fuzzy segmentation of video shots using hybrid color spaces and motion information

Video segmentation can be defined as the process of partitioning video into spatio-temporal objects that are homogeneous in some feature space, with the choice of features being very important to the success of the segmentation process. Fuzzy segmentation is a semi-automatic region-growing segmentation algorithm that assigns to each element in an image a grade of membership in an object. In this paper, we propose an extension of the multi-object fuzzy segmentation algorithm to segment pre-acquired color video shots. The color features are selected from channels belonging to different color models using two different heuristics: one that uses the correlations between the color channels to maximize the amount of information used in the segmentation process, and one that chooses the color channels based on the separation of the clusters formed by the seed spels for all possible color spaces. Motion information is also incorporated into the segmentation process by making use of dense optical flow maps. We performed experiments on synthetic videos, with and without noise, as well as on some real videos. The experiments show promising results, with the segmentations of real videos produced using hybrid color spaces being more accurate than the ones produced using three other color models. We also show that our method compares favorably to a state-of-the art video segmentation algorithm.     

Surface feature based mesh segmentation

Mesh segmentation has a variety of applications in product design, reverse engineering, and rapid prototyping fields. This paper presents a novel algorithm of mesh segmentation from original scanning data points, which essentially consists of three steps. Normal based initial decomposing is first performed to recognize plane features. Then we implement further segmentation based on curvature criteria and Gauss mapping, followed by the detection of quadric surface features. The segmentation refinement is finally achieved using B-spline surface fitting technology. The experimental results on many 3D models have demonstrated the effectiveness and robustness of the proposed segmentation method.     

Fine-grained histopathological cell segmentation through residual attention with prior embedding

In the task of histopathological cell segmentation, traditional algorithms struggle with cell edge processing, which leads to the blurring of cell edges. To strengthen the ability to learn the features of cell edges, this paper develops a novel deep neural network for robust and fine-grained cell segmentation. The proposed deep model mines global and local features by multiscale convolution and dilated convolution. Subsequently, the residual attention module is introduced in the third to fifth layers of the encoder; this module assigns a group of weight coefficients to all the deep features to boost the segmentation performance. In addition, to further improve the quality of the features in the decoder, we first introduce the strategy of U-Net for the extraction of prior information, where we filter the fused features and compress the features by using the prior information and the filtered features again to integrate more semantic information into the feature refinement in the decoding process. We tested the model on three public data sets: Multiorgan Nucleus Segmentation (MoNuSeg) (Dice 94.9%), Triple Negative Breast Cancer (TNBC) (Dice 95.4%) and Data Science Bowl (Dice 98.2%). Extensive experiments demonstrate the superior performance of our proposed method in comparison with that of state-of-the-art models; our method can effectively identify cell edges to produce fine-grained segmentation results.

     

一种改进的多尺度分形网络演化分割方法

面向对象技术是提高高分辨率图像分类精度的关键技术之一。针对eCognition的分形网络演化分割算法仅仅采用光谱特征和形状特征进行图像多尺度分割的不足,提出了将边缘特征引入其分割过程中,以提高多尺度分割的效果,来获得边缘平滑且分割对象与实际地物更加一致的分割结果,从而改善eCognition的分形网络演化分割方法中存在的过分割、欠分割和边缘粗糙等情况。通过实验,证明了引入边缘特征的分形网络演化分割方法提高了图像分割结果,减小了过分割和欠分割的产生,使得分割结果与实际地物更加一致。     

点模型的多分辨率形状编辑

提出了一种基于几何细节映射的点模型的形状编辑方法.几何细节是曲面的一个重要属性,定义几何细节为原始曲面及其基曲面之间的向量差,该基曲面由多层次B样条所构成.通过基曲面上的局部仿射坐标,则可以得到与之对应的多分辨率几何细节表示,曲面的低频信息和高频信息易被用户所指定的频段分离.通过调节基曲面的形状,再将这些几何细节映射上去,可以对模型进行保细节的变形;如果将几何细节映射到其他物体上,将可以得到几何细节迁移的结果.为点模型开发了多种特征保持的编辑算子,实验结果表明,所提出的方法是一种有效的点模型造型算法.     

点模型的几何图像简化法

提出一种基于几何图像的曲率自适应点模型简化算法.首先将点模型的球面极坐标映射到平面上,构造其几何图像;然后利用几何图像确定点模型中点的k-最近邻域及其曲面变分;最后结合曲面变分和简化密度对点集曲面重采样,并通过移动最小二乘曲面评估简化的误差.实验结果表明,该算法执行速度快、易于控制采样密度和保持曲面细节,且能够生成高质量的简化曲面.     

Blood Vessel Segmentation via Neural Network in Histological Images

In this paper we utilize the Kohonen's self-organizing feature map to segment blood vessels from biopsies in tumor tissue. The ability of this kind of neural network to recognize very complex patterns makes it an effective computational tool for the segmentation. We propose a strategy of blood vessels segmentation using a neural network, taking into account the quality of our images and its features: complexity in shape and variability in size. Segmentation results are contingently manually corrected. The proposed segmentation strategy is tested on manual segmentation, where segmentation errors of less than 3.5% are observed. This work is a part of a global image analysis process and these images will be subject to a further morphometrical analysis in order to diagnose and prognosticate automatically malignant tumours.     

基于图像内容层次表征的遥感图像 分割方法

提出基于图像内容层次表征的高分辨率遥感图像快速多精度分割方法。首先根据初始分割结果建立区域邻接图(RAG),并将其定义为马尔可夫随机场(MRF);然后引入光谱、形状和边缘等图像特征进行层次合并,通过记录层次合并过程获得图像内容的层次表征;最后根据层次表征中不同层级对象之间的关系快速生成任意不同精度的分割结果,以满足不同应用的需求。利用QuickBird卫星图像进行实验和评价的结果表明,本文方法具有较高的精度和效率。     

Deep learning-based optimal segmentation of 3D printed product for surface quality improvement and support structure reduction

Large-sized product cannot be printed as one piece by a 3D printer because of the volume limitation of most 3D printers. Some products with the complex structure and high surface quality should also not be printed into one piece to meet requirement of the printing quality. For increasing the surface quality and reducing support structure of 3D printed models, this paper proposes a 3D model segmentation method based on deep learning. Sub-graphs are generated by pre-segmenting 3D triangular mesh models to extract printing features. A data structure is proposed to design training data sets based on the sub-graphs with printing features of the original 3D model including surface quality, support structure and normal curvature. After training a Stacked Auto-encoder using the training set, a 3D model is pre-segmented to build an application set by the sub-graph data structure. The application set is applied by the trained deep-learning system to generate hidden features. An Affinity Propagation clustering method is introduced in combining hidden features and geometric information of the application set to segment a product model into several parts. In the case study, samples of 3D models are segmented by the proposed method, and then printed using a 3D printer for validating the performance.     

一种统一的硬件加速自适应EWA Splatting算法

提出了一种新的硬件加速自适应EWA(椭圆加权平均)Splatting算法框架,可同时适用于三维体数据和点模型.算法将高斯重建核与低通图像滤波核结合,得到反走样、无模糊的高质量图像.提出一种高效的白适应滤波方法,减少了高质量EWA Splatting的计算量.提出了自适应体EWA Splatting的3种数据存储模式和一系列高级特性,其中包括交互式分类、体一面混合绘制策略和自适应浮点累加.展示了如何在可编程图形处理单元(GPU)中计算体数据和点模型数据的EWA Splat基元.实验表明,文中的方法在一台普通微机上每秒可绘制1500万～2000万个基元,达到较高的图像质量与交互的绘制速度.     

一般点模型的交互式布尔运算

提出了一个适用于一般点模型的交互式布尔运算算法,此算法由4个步骤组成．首先将点模型表示为自适应的三色八叉树,然后利用自适应八叉树结构加速内外测试．对于局部采样密度不一致的相交区域或曲率太大容易导致较大求交误差的地方,实行了自适应细分加密采样;重采样相交的部分以获得更精确的求交结果．与已有的点模型布尔运算方法相比,该算法适用于一般的实测点云数据,包括少量噪声的点模型、非均匀采样以及不同分辨率点模型之间的交互式布尔运算。     

Detail-generating geometry completion for point-sampled geometry

In this paper, we present a novel method for detail-generating geometry completion over point-sampled geometry. The main idea consists of converting the context-based geometry completion into the detail-based texture completion on the surface. According to the influence region of boundary points surrounding a hole, a smooth patch covering the hole is first constructed using radial base functions. By applying region-growing clustering to the patch, the patching units for further completion with geometry details is then produced, and using the trilateral filtering operator formulated by us, the geometry-detail texture of each sample point on the input geometry is determined. The geometry details on the smooth completed patch are finally generated by optimizing a constrained global texture energy function on the point-sampled surfaces. Experimental results demonstrate that the method can achieve efficient completed patches that not only conform with their boundaries, but also contain the plausible 3D surface details.