Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

This paper presents a method for multi-scale segmentation of surface data using scale-adaptive region growing. The proposed segmentation algorithm is initiated by an unsupervised learning of optimal seed positions through the surface attribute clustering with a two-criterion score function. The seeds are selected as consecutive local maxima of the clustering map, which is computed by an aggregation of the local isotropic contrast and local variance maps. The proposed method avoids typical segmentation errors caused by an inappropriate choice of seed points and thresholds used in the region-growing algorithm. The scale-adaptive threshold estimate is based on the image local statistics in the neighborhoods of seed points. The performance of this method was evaluated on LiDAR surface images.     

Online Segmentation and Clustering From Continuous Observation of Whole Body Motions

This paper describes a novel approach for incremental learning of human motion pattern primitives through online observation of human motion. The observed time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments are then abstracted into a stochastic model representation and automatically clustered and organized. As new motion patterns are observed, they are incrementally grouped together into a tree structure, based on their relative distance in the model space. The tree leaves, which represent the most specialized learned motion primitives, are then passed back to the segmentation algorithm so that as the number of known motion primitives increases, the accuracy of the segmentation can also be improved. The combined algorithm is tested on a sequence of continuous human motion data that are obtained through motion capture, and demonstrates the performance of the proposed approach.     

Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking

Appearance modeling is very important for background modeling and object tracking. Subspace learning-based algorithms have been used to model the appearances of objects or scenes. Current vector subspace-based algorithms cannot effectively represent spatial correlations between pixel values. Current tensor subspace-based algorithms construct an offline representation of image ensembles, and current online tensor subspace learning algorithms cannot be applied to background modeling and object tracking. In this paper, we propose an online tensor subspace learning algorithm which models appearance changes by incrementally learning a tensor subspace representation through adaptively updating the sample mean and an eigenbasis for each unfolding matrix of the tensor. The proposed incremental tensor subspace learning algorithm is applied to foreground segmentation and object tracking for grayscale and color image sequences. The new background models capture the intrinsic spatiotemporal characteristics of scenes. The new tracking algorithm captures the appearance characteristics of an object during tracking and uses a particle filter to estimate the optimal object state. Experimental evaluations against state-of-the-art algorithms demonstrate the promise and effectiveness of the proposed incremental tensor subspace learning algorithm, and its applications to foreground segmentation and object tracking.     

Fast segmentation of range images into planar regions by scan line grouping

A novel technique is presented for rapid partitioning of surfaces in range images into planar patches. The method extends and improves Pavlidis' algorithm (1976), proposed for segmenting images from electron microscopes. The new method is based on region growing where the segmentation primitives are scan line grouping features instead of individual pixels. We use a noise variance estimation to automatically set thresholds so that the algorithm can adapt to the noise conditions of different range images. The proposed algorithm has been tested on real range images acquired by two different range sensors. Experimental results show that the proposed algorithm is fast and robust.     

自适应区域生长算法在医学图像分割中的应用

提出一种通过计算种子点附近邻域统计信息,自适应改变生长标准参数用于医学图像分割的算法．在切片图像预处理过程中,考虑到体数据相邻切片之间高度的相关性,在相邻层之间采取高斯核滤波去除噪声,并通过各向异性滤波算法对该层切片进行滤波．实验结果表明,该算法可有效地提取出图像区域,具有较好的鲁棒性．     

OBCD-HH: an object-based change detection approach using multi-feature non-seed-based region growing segmentation

There is an increasing need to get updated information regarding the changes on earth’s surface. The information obtained can be used in a wide range of applications including disaster management, land-use investigation etc. The high-resolution remote sensing images obtained from satellites provide us with an opportunity to detect changes on earth’s surface between various time intervals. In this paper, an unsupervised object-based change detection (OBCD) method is proposed to detect changes in high resolution bi-temporal satellite images. To detect changes, a novel multi-feature non-seed-based region growing (MF-NSRG) algorithm is proposed for image segmentation based on heterogeneity minimization that uses textural heterogeneity along with spectral and spatial heterogeneity during region growing. The performance of MF-NSRG algorithm is further improved by using Harris Hawk, a recently proposed metaheuristic algorithm, which is used to obtain optimal values of segmentation parameters. Finally, the feature maps extracted from the pre-change and post-change segmented images are analysed using histogram trend similarity (HTS) approach to detect changes. The proposed approach is known as object-based change detection using Harris Hawk (OBCD-HH). The proposed OBCD-HH approach is applied on two datasets: xBD and Onera Satellite Change Detection (OSCD) dataset. Its performance is compared with existing state-of-the-art algorithms and results show the superiority of the proposed approach.

     

An integrated approach for range image segmentation and representation

This paper proposes an efficient method for the segmentation and representation of 3D rigid, solid objects from its range images using differential invariants derived from classical differential geometry. An efficient algorithm for derivation of surface curvatures, which are affine invariants, at smooth surface patches is proposed. The surface is approximated by Bezier and Beta-splines to compare qualitatively the proposed segmentation scheme. This scheme leads to derivation of surface features, which provides a very robust surface segmentation. An integrated approach represents the surface in terms of plane, quadric and superquadric surface.Experiments show excellent performance and together with the inherent parallelism make the scheme a promising one. Present experiments were conducted on some real range images where most of the parts of the object are planar.     

Coupled grouping and matching for sign and gesture recognition

Matching an image sequence to a model is a core problem in gesture or sign recognition. In this paper, we consider such a matching problem, without requiring a perfect segmentation of the scene. Instead of requiring that low- and mid-level processes produce near-perfect segmentation, we take into account that such processes can only produce uncertain information and use an intermediate grouping module to generate multiple candidates. From the set of low-level image primitives, such as constant color region patches found in each image, a ranked set of salient, overlapping, groups of these primitives are formed, based on low-level cues such as region shape, proximity, or color. These groups corresponds to underlying object parts of interest, such as the hands. The sequence of these frame-wise group hypotheses are then matched to a model by casting it into a minimization problem. We show the coupling of these hypotheses with both non-statistical matching (match to sample-based modeling of signs) and statistical matching (match to HMM models) are possible. Our algorithm not only produces a matching score, but also selects the best group in each image frame, i.e. recognition and final segmentation of the scene are coupled. In addition, there is no need for tracking of features across sequences, which is known to be a hard task. We demonstrate our method using data from sign language recognition and gesture recognition, we compare our results with the ground truth hand groups, and achieved less than 5% performance loss for both two models. We also tested our algorithm on a sports video dataset that has moving background.     

Sketching Clothoid Splines Using Shortest Paths

Clothoid splines are gaining popularity as a curve representation due to their intrinsically pleasing curvature, which varies piecewise linearly over arc length. However, constructing them from hand‐drawn strokes remains difficult. Building on recent results, we describe a novel algorithm for approximating a sketched stroke with a fair (i.e., visually pleasing) clothoid spline. Fairness depends on proper segmentation of the stroke into curve primitives — lines, arcs, and clothoids. Our main idea is to cast the segmentation as a shortest path problem on a carefully constructed weighted graph. The nodes in our graph correspond to a vastly overcomplete set of curve primitives that are fit to every subsegment of the sketch, and edges correspond to transitions of a specified degree of continuity between curve primitives. The shortest path in the graph corresponds to a desirable segmentation of the input curve. Once the segmentation is found, the primitives are fit to the curve using non‐linear constrained optimization. We demonstrate that the curves produced by our method have good curvature profiles, while staying close to the user sketch.     

工业CT数据场面绘制和体绘制改进算法研究

工业CT图像的重建速度和精度是工业CT产品的两个重要指标。针对面绘制的MC算法提出了一种基于相似性区域分割的三维工业图像表面重建算法,实现了准确分割,并利用分割结果精确地提取等值面,显著提高了检测效率;针对体绘制的光线投射算法提出了一种基于二维最大熵阈值的分割预处理方法,利用二维直方图熵最大化寻找阈值的最佳组合,能有效减少重建体数据量,实测数据表明体绘制速度明显提高。     

SAR图像的最优分割方法

根据SAR图像的概率密度函数获得图像的拟然函数,然后将似然函数和边界约束方程结合起来,提出适合于SAR图像分割的代价函数,其中边界约束方程引入邻域结构信息来保证区域边界的规则性,通过使代价函数最小来获得图像的最优分割。算法首先将原图分割成一定大小的块状区域作为初始分割,每一区域代表一个类别;然后随机调整相邻两个区域之间的像素,通过比较代价函数的变化,利用模拟退火算法确定接受该调整的概率。模拟退火是一种求解全局最优的算法,当温度趋向于0时,它可以获得使代价函数最小的SAR图像的分割。最后,利用基于相似性的融合方法对分割进行后期处理,将相似的较小的区域融合成较大的区域,使得分割更合理。我们将该算法应用到一些SAR测试图像上,获得了比较满意的结果。     

基于高斯混合模型的视频运动对象自动分割算法

提出的算法首先采用高斯混合模型依据空间属性对当前帧进行聚类分割,可以克服一般聚类算法对数据集中的噪声无法建模以及聚类数目难以确定的问题.然后依据时序属性,分割出当前帧的运动对象的初步轮廓区域.最后将初步轮廓区域和聚类分割的区域进行匹配,提取出视频运动对象.通过实验验证,算法具有较好的准确性和抗干扰性,在运动微小的情况下也能取得比较好的效果.     

离散ADMM方法下像素与对象基元协同优化的遥感影像无监督语义分割

语义分割是遥感影像分析中的重要技术之一。现有的方法（如基于深度卷积神经网络的方法等）虽然在语义分割中取得了显著进展,但往往需要大量训练数据。基于图模型的马尔可夫随机场模型（Markov random field model,MRF）提出了一种不依赖训练数据的无监督语义分割思路,可以有效地刻画地物空间关系,并对地物空间分布的统计规律进行建模。但现有的MRF模型方法通常建立在基于像素或对象的单一粒度基元上,难以充分利用影像信息,语义分割效果不佳。针对上述问题,引入交替方向乘子法 （alternative direction method of multiplier,ADMM）并将其离散化,提出了一种像素与对象基元协同的MRF模型无监督语义分割方法（MRF-ADMM）。首先构建像素基元和对象基元两个概率图,其中像素基元概率图用于刻画影像的细节信息,保持语义分割的边界;对象基元概率图用于描述较大范围的空间关系,以应对遥感影像地物内部的高异质性,使分割结果中地物内部具有良好的区域完整性。在模型求解过程中,针对像素和对象基元的特点,提出了一种离散化的ADMM方法,并将其用于两种基元类别标记的传递与更新,实现像素基元细节信息和对象基元区域信息的协同优化。高分二号和航拍影像等不同数据库不同类型遥感影像的语义分割实验结果表明,相较于现有的MRF模型,提出的MRF-ADMM方法能有效地协同不同粒度基元的优点,优化语义分割结果。     

Determination of the optimal number of clusters using a spectral clustering optimization

In this paper, we present a new method, called Spectral Global Silhouette method (GS), to calculate the optimal number of clusters in a dataset using a Spectral Clustering algorithm. It combines both a Silhouette Validity Index and the concept of Local Scaling. First, the GS algorithm has first been tested using synthetic data. Then, it is applied on real data for image segmentation task. In addition, three new methods for image segmentation and two new ways to calculate the optimal number of groups in an image are proposed. Our experiments have shown a promising performance of the proposed algorithms.     

Range Scan Registration Using Reduced Deformable Models

We present an unsupervised method for registering range scans of deforming, articulated shapes. The key idea is to model the motion of the underlying object using a reduced deformable model. We use a linear skinning model for its simplicity and represent the weight functions on a regular grid localized to the surface geometry. This decouples the deformation model from the surface representation and allows us to deal with the severe occlusion and missing data that is inherent in range scan data. We formulate the registration problem using an objective function that enforces close alignment of the 3D data and includes an intuitive notion of joints. This leads to an optimization problem that we solve using an efficient EM-type algorithm. With our algorithm we obtain smooth deformations that accurately register pairs of range scans with significant motion and occlusion. The main advantages of our approach are that it does not require user specified markers, a template, nor manual segmentation of the surface geometry into rigid parts.     

Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols

An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to use as training data for the HMMs, this framework requires a manual segmentation of human motions. Essentially, a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations. For this use, manual segmentation is cumbersome and impractical for humanoid robots. In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases. In the first phase, short human movements are encoded into feature HMMs. Seamless human motion can be converted to a sequence of these feature HMMs. In the second phase, the causality between the feature HMMs is extracted. The causality data make it possible to predict movement from observation. In the third phase, movements having a large prediction uncertainty are designated as the boundaries of motion primitives. In this way, human whole-body motion can be segmented into a sequence of motion primitives. This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives (AUS). Each derived motion primitive is classified into an HMM for a motion symbol, and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning. The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives. We tested the RUS and AUS frameworks on captured human whole-body motions and demonstrated the validity of the proposed framework.     

基于微分不变量和区域增长法的深度图分割

用深度图(点云)直接对曲面物体进行识别,将会加大曲面物体的数学表示的难度。该文提出一种对深度图进行自动分割的方法,基于微分不变量进行初始分割,得到初始的核区域,用区域增长法进行曲面片增长,将深度图分割成多个区域。该算法原理简单、易于理解和编程。通过一个米老鼠头部深度图实例证明了该方法的有效性。     

一种基于互补投票的高效光流计算方法

描述了一个基于互补投票的高效、带有信赖度的光流计算方法,简称CMV方法.为了计算一个感兴趣区域的光流, 我们首先分割这个区域为若干个子区域,然后利用一个匹配策略计算每一个子区域的相似度分布.这些相似度分布被用来抽取两种类型的投票角色:正投票和负投票.随后,这两种投票角色在一个准则的控制下被用来获取一个最优的投票结果, 这个投票结果将决定光流及其信赖度. 为了削减CMV的计算复杂度,我们提出了一个基于正投票的(PV-based)负投票策略.实验结果显示,CMV方法能够有效计算低质量图像序列的光流,并且这个新的负投票策略在几乎没有影响性能的情况下极大地削减了算法的计算复杂度.     

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。     

Creating and Rendering Convolution Surfaces

Implicit surfaces obtained by convolution of multi-dimensional primitives with some potential function, are a generalisation of popular implicit surface models: blobs, metaballs and soft objects. These models differ in their choice of potential function but agree upon the use of underlying modelling primitives, namely, points. In this paper a method is described for modelling and rendering implicit surfaces built upon an expanded set of skeletal primitives: points, line segments, polygons, arcs and planes. An analytical solution to the convolution is described. This solution offers a more accurate and robust representation of the resultant implicit surface than previous methods. An algorithm for ray-tracing the surfaces formed through convolution of any combination of these primitives is also outlined.