A new method for automated detection and identification of neurons in microscopic images of brain slices

A new combined mathematical method is proposed, implemented, and experimentally tested for extracting information necessary for modeling and, in future, predicting Parkinson??s disease (PD) from microscopic images of brain slices of experimental animals. The method allows one to detect and identify as neurons a set of small informative extended objects with well distinguished (by brightness) oval inclusions. The result is a binary image of the contours of detected objects and their inclusions and a list of characteristics calculated for each detected object. The method is based on the joint application of image processing methods, methods of mathematical morphology, methods of segmentation, and the methods of classification of microscopic images.     

Appearance characterization of linear Lambertian objects, generalized photometric stereo, and illumination-invariant face recognition

Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented     

Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem

This paper presents an algebraic approach to polynomial spectral factorization, an important mathematical tool in signal processing and control. The approach exploits an intriguing relationship between the theory of Gröbner bases and polynomial spectral factorization which can be observed through the sum of roots, and allows us to perform polynomial spectral factorization in the presence of real parameters. It is discussed that parametric polynomial spectral factorization enables us to express quantities such as the optimal cost in terms of parameters and the sum of roots. Furthermore an optimization method over parameters is suggested that makes use of the results from parametric polynomial spectral factorization and also employs two quantifier elimination techniques. This proposed approach is demonstrated in a numerical example of a particular control problem.     

Combined new nonnegative matrix factorization algorithms with two-dimensional nonnegative matrix factorization for image processing

In recent years, nonnegative matrix factorization (NMF) has attracted significant amount of attentions in image processing, text mining, speech processing and related fields. Although NMF has been applied in several application successfully, its simple application on image processing has a few caveats. For example, NMF costs considerable computational resources when performing on large databases. In this paper, we propose two enhanced NMF algorithms for image processing to save the computational costs. One is modified rank-one residue iteration (MRRI) algorithm , the other is element-wisely residue iteration (ERI) algorithm. Here we combine CAPG (a NMF algorithm proposed by Lin), MRRI and ERI with two-dimensional nonnegative matrix factorization (2DNMF) for image processing. The main difference between NMF and 2DNMF is that the former first aligns images into one-dimensional (1D) vectors and then represents them with a set of 1D bases, while the latter regards images as 2D matrices and represents them with a set of 2D bases. The three combined algorithms are named CAPG-2DNMF, MRRI-2DNMF and ERI-2DNMF. The computational complexity and convergence analyses of proposed algorithms are also presented in this paper. Three public databases are used to test the three NMF algorithms and the three combinations, the results of which show the enhancement performance of our proposed algorithms (MRRI and ERI algorithms) over the CAPG algorithm. MRRI and ERI have similar performance. The three combined algorithms have better image reconstruction quality and less running time than their corresponding 1DNMF algorithms under the same compression ratio. We also do some experiments on a real-captured image database and get similar conclusions.     

基于图像块分类器和条件随机场的显微图像分割

针对花粉显微图像处理提出了一种自动分割方法,将有助于花粉识别系统的开发。使用归一化颜色分量训练图像块分类器,并且结合条件随机场和图割进行建模和优化,利用最大化后验概率(MAP)的方法实现花粉显微图像中花粉区域的分割。对于实验中的133幅图像,自动分割同人工分割的结果相比较,统计得到距离误差均值为7.3像素,准确率的平均值为87%。实验结果表明,使用图像块分类器和条件随机场模型可以用于花粉图像的分割。     

基于概念格因子分解的零件三维CAD模型检索

针对影响概念格应用的重要问题—即使是一个小规模数据集也会产生大量的形式概念,文中提出了可以满足关系覆盖的用对象（属性）概念分解形式背景对应的布尔矩阵的新方法.用这种方法原对象属性间的二元关系可以用数量在对象（属性）概念个数以内的概念表达出来,成为概念格因子.文中给出了概念格因子生成的基本原理及其算法.通过分析三维CAD零件模型功能表面间的关系构建零件工程图结构模型,并将其映射为形式背景,从而完成概念格因子到零件关键结构的应用.最后,实例演示了概念格因子在基于零件工程图结构模型的零件CAD模型检索中的运用.     

A new synaptic plasticity rule for networks of spiking neurons

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.     

Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy

Optical flow approaches calculate vector fields which determine the apparent velocities of objects in time-varying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABA_BR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160?nm. Displacement of agglomerated GABA_BR1 receptor subunits can be predicted correctly for maximum displacements of 640?nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.     

基于非局部张量火车分解的彩色图像修补

数据在采集和转换的过程中通常存在部分数据丢失的问题,丢失数据的补全直接影响后续的识别、跟踪等高层任务的结果.自然图像中经常存在许多具有重复特性的相似结构,利用该类冗余信息,文中提出基于非局部张量火车分解的张量补全方法.利用图像的非局部相似性,挖掘其中蕴含的低秩特性,并通过张量火车分解模型进行建模及升阶,将低阶张量转化为高阶以进行低秩信息的进一步挖掘利用,从而进行图像中缺失数据的修补.实验验证文中方法在图像修补上的有效性.     

一种基于线性判别空间增量更新的跟踪方法

为了在视频中稳定地跟踪目标物,提出一种基于增量型线性判别分析的目标跟踪方法。该方法利用一组仿射参数描述目标物在视频中的空间位置及姿态,根据状态转移模型预测得到下一帧视频中目标物的候选图像样本集合。计算各样本在线性判别空间中为目标图像的似然度,以具有最大似然度的样本作为目标图像区域。最后由类间散度矩阵与类内散度矩阵的充分生成集作旋转变换完成投影矩阵的增量更新,以保持判别空间的判别能力。实验结果表明,该方法对目标物及其周围背景的外观变化具有较强的自适应性,能够有效地对运动目标进行仿射不变的跟踪。     

显微图象中目标对象的识别

研究了显微图象中目标对象的识别问题。首先比较了显微图象和宏观图象之间的区别,总结了大部分显微图象具有的一般特征,讨论了宏观图象处理方法用于显微图象时存在的缺陷。然后提出了一种目标对象识别的新方法,该方法利用最优颜色通道识别显微图象中的目标对象,动态地调节识别阈值,减少了误识别象素的数量。对残存的误识别象素和脏点象素采用面积滤波的方式去除,进一步提高识别的效果。该文通过实验,识别了几种显微图象中的目标对象,实验发现,使用这一方法不仅能够提取背景中的目标对象,还可以大量减少误识别象素和脏点象素,说明该方法用于显微图象中目标对象的识别具有一定的优点。     

应用于灰度图像分割的基于流体静力学原理活动轮廓模型

提出一种新的活动轮廓模型,应用于灰度图像分割。此模型建立在流体 静力学理论之上, 运用流体静力学理论直接驱动连续曲线,逼近被包围的目标。该模型能够 分割多重目标、能够分割嵌套的目标、能够有效地控制过分割现象。     

基于全卷积网络的砂石图像粒径检测

为了准确分割开紧密粘连的砂石目标,并获得砂石目标粒径大小,提出一种基于两阶段深度学习的砂石图像粒径检测方法。该方法利用图像处理技术对砂石图像进行预处理,然后通过第一阶段的网络分割模型对砂石目标进行目标分割。对分割目标进行形态学处理后,很多砂石目标紧密粘连在一起,再通过第二阶段的网络分离模型将粘连的砂石目标分离开来,得到分割且分离的结果图。最后计算砂石目标最长径,求均值后得到砂石图像的平均粒径大小。通过实验验证该算法可以快速、准确地将紧密粘连的砂石目标分割开来,提高了砂石目标粒径大小计算精度。     

Intent-aware image cloning

Currently, gradient domain methods are popular for producing seamless cloning of a source image patch into a target image. However, structure conflicts between the source image patch and the target image may generate artifacts, preventing the general practices. In this paper, we tackle the challenge by incorporating the users’ intent in outlining the source patch, where the boundary drawn generally has different appearances from the objects of interest. We first reveal that artifacts exist in the over-included region, the region outside the objects of interest in the source patch. Then we use the diversity from the boundary to approximately distinguish the objects from the over-included region, and design a new algorithm to make the target image adaptively take effects in blending. So the structure conflicts can be efficiently suppressed to remove the artifacts around the objects of interest in the composite result. Moreover, we develop an interpolation measure to composite the final image rather than solving a Poisson equation, and speed up the interpolation by treating pixels in clusters and using hierarchical sampling techniques. Our method is simple to use for instant and high-quality image cloning, in which users only need to outline a region of interested objects to process. Our experimental results have demonstrated the effectiveness of our cloning method.     

Quasi-perspective Projection Model: Theory and Application to Structure and Motion Factorization from Uncalibrated Image Sequences

This paper addresses the problem of factorization-based 3D reconstruction from uncalibrated image sequences. Previous studies on structure and motion factorization are either based on simplified affine assumption or general perspective projection. The affine approximation is widely adopted due to its simplicity, whereas the extension to perspective model suffers from recovering projective depths. To fill the gap between simplicity of affine and accuracy of perspective model, we propose a quasi-perspective projection model for structure and motion recovery of rigid and nonrigid objects based on factorization framework. The novelty and contribution of this paper are as follows. Firstly, under the assumption that the camera is far away from the object with small lateral rotations, we prove that the imaging process can be modeled by quasi-perspective projection, which is more accurate than affine model from both geometrical error analysis and experimental studies. Secondly, we apply the model to establish a framework of rigid and nonrigid factorization under quasi-perspective assumption. Finally, we propose an Extended Cholesky Decomposition to recover the rotation part of the Euclidean upgrading matrix. We also prove that the last column of the upgrading matrix corresponds to a global scale and translation of the camera thus may be set freely. The proposed method is validated and evaluated extensively on synthetic and real image sequences and improved results over existing schemes are observed.     

基于深度学习的虚拟边界检测方法

针对传统边缘检测方法无法对材料微观图像中不同区域间存在的"虚拟边界"（VB）进行准确检测的问题,提出一种基于卷积神经网络（CNN）的虚拟边界检测模型,称之为"虚拟边界网络"（VBN）。该模型对VGGNet深度学习模型进行了简化,并在模型训练过程中采用了dropout以及Adam算法等优化策略。VBN以图像中每个像素为中心所取的图像块作为输入,然后输出该图像块所属的类别并据此判断中心像素是否属于虚拟边界。在对两类材料图像进行虚拟边界检测的实验中,VBN的平均检测精度到达92.5%,平均召回率达到89.5%,证明该模型能够准确、有效地对图像中的虚拟边界进行检测,是一种替代低效率人工分析方法的有效手段。     

双层非负矩阵分解的分形图像压缩算法

分形图像压缩作为一种基于结构的图像压缩技术,在许多图像处理中得到了应用.但是分形图像压缩的编码阶段非常耗时,且重建图像的质量效果不佳.针对这些问题,提出了一种基于双层非负矩阵分解的分形图像压缩编码算法.在传统的非负矩阵分解理论上,将投影非负矩阵分解与L3/2范数约束相结合,可以在较短的时间内提取具有代表性的图像特征.算...     

Incorporating non-motion cues into 3D motion segmentation

We address the problem of segmenting an image sequence into rigidly moving 3D objects. An elegant solution to this problem in the case of orthographic projection is the multibody factorization approach in which the measurement matrix is factored into lower rank matrices. Despite progress in factorization algorithms, their performance is still far from satisfactory and in scenes with missing data and noise, most existing algorithms fail.In this paper we propose a method for incorporating 2D non-motion cues (such as spatial coherence) into multibody factorization. We show the similarity of the problem to constrained factor analysis and use the EM algorithm to find the segmentation. We show that adding these cues improves performance in real and synthetic sequences.