Segmentation-recognition algorithm for zip code field recognition

This paper describes a recognition algorithm for zip code field recognition. The algorithm consists of an initial character segmentation algorithm and a connected-numeral splitting algorithm. The initial character segmentation algorithm employs connected component analysis with component merge technique based on proximity. The numeral splitting algorithm consists of a slant splitting algorithm based on discriminant analysis and two postprocessing algorithms based on local shape analysis. The splitting algorithm is integrated with a statistical classifier to form a segmentation-recognition algorithm to resolve the ambiguity of connected numeral splitting. The performance is tested by recognition experiments on zip code fields collected from real USPS mail envelopes.     

A rule-based approach for robust clump splitting

This paper presents a robust rule-based approach for the splitting of binary clumps that are formed by objects of diverse shapes and sizes. First, the deepest boundary pixels, i.e., the concavity pixels in a clump, are detected using a fast and accurate scheme. Next, concavity-based rules are applied to generate the candidate split lines that join pairs of concavity pixels. A figure of merit is used to determine the best split line from the set of candidate lines. Experimental results show that the proposed approach is robust and accurate.     

Parallel median splitting and k-splitting with applicationto merging and sorting

Multiple-instruction multiple-data (MIMD) algorithms that use multiple processors to do median splitting, k-splitting and parallel splitting into t equal sections are presented. Both concurrent read, exclusive write (CREW) and exclusive read, exclusive write (EREW) versions of the algorithms are given. It is shown that a k-splitting problem can be easily converted into a median-splitting problem. Methods for finding multiple split points quickly and application of k-splitting to merging and sorting are discussed     

Mumford-Shah on the Move: Region-Based Segmentation on Deforming Manifolds with Application to 3-D Reconstruction of Shape and Appearance from Multi-View Images

We address the problem of estimating the shape and appearance of a scene made of smooth Lambertian surfaces with piecewise smooth albedo. We allow the scene to have self-occlusions and multiple connected components. This class of surfaces is often used as an approximation of scenes populated by man-made objects. We assume we are given a number of images taken from different vantage points. Mathematically this problem can be posed as an extension of Mumford and Shah’s approach to static image segmentation to the segmentation of a function defined on a deforming surface. We propose an iterative procedure to minimize a global cost functional that combines geometric priors on both the shape of the scene and the boundary between smooth albedo regions. We carry out the numerical implementation in the level set framework.     

Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.     

Propagation algorithms for framing rectangle construction

Given a binary image containing a connected component, parallel propagation algorithms are presented for constructing upright and 45°-tilted framing rectangles around the component in time proportional to the sum of the dimensions of these rectangles. The algorithms make use of properties of geodesics connecting pairs of points in the component to iteratively fill in the region to the desired shape.     

一种基于灰度级连通性的红外图像分割方法

本文提出一种新的基于灰度级连通性的红外图像分割方法。灰度级连通性认为在某个灰度级以下的所有级集合是连通的,则灰度图像是连通的。提出的图像分割方法使用k级特征开运算将图像中包含目标的k级以上的连通成分保留下来,结合图像弱小目标的特征进行k级连通成分分解运算,提取出包含目标的k级连通成分实现图像简化和目标提取,最后结合简单的二值化处理就能够准确地分割出目标。通过仿真结果的比较,证明在红外图像中这种方法可以实现提高信噪比、提取目标和分割图像的目的。     

Influence of image fusion approaches on classification accuracy: a case study

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes.

This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM+ image was used, where six multispectral bands (30 m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue‐saturation‐value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolution image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object‐based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue‐saturation‐value image fusion performed poorly and cannot be recommended for classification of fused imagery.     

基于水动力学、分数阶微分及Steger算法的线状目标提取

目的 线状目标的检测具有非常广泛的应用领域,如车道线、道路及裂缝的检测等,而裂缝是其中最难检测的线状目标。为避免直接提取线状目标时图像分割难的问题,以裂缝和车道线为例,提出了一种新的跟踪线状目标中线的算法。方法 对图像进行高斯平滑,用一种新的分数阶微分模板增强图像中的模糊及微细线状目标;基于Steger算法提出一种提取线状目标中心线特征点的算法,避免了提取整体目标的困难;根据水动力学思想将裂隙看成溪流,通过最大熵阈值处理后,先进行特征点的连接,再基于线段之间的距离及夹角进行线段之间的连接（溪流之间的融合）。结果 对300幅裂缝图像及4种类别的其他线状目标图像进行试验,并与距离变换、最大熵阈值法+细线化Otsu阈值分割+细线化、谷底边界检测等类似算法进行比较分析,本文算法检测出的线状目标的连续性好、漏检（大间隙少）和误检（毛刺及多余线段少）率均较低。结论 本文算法能够在复杂的线状目标图像中准确快速地提取目标的中心线,一定程度上改善了复杂线状目标图像分割难的问题。     

Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds

This paper presents an extension of previously published theory and algorithms for object definition based on fuzzy connectedness. In this approach, a strength of connectedness is assigned between every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image and is determined considering how close the elements are in space and in intensity features. A fuzzy connected object containing a particular seed element specified within the object is computed via dynamic programming. In all reported works so far, the minimum of affinities has been considered to define path strength and the maximum of path strengths has been used to define fuzzy connectedness. The question thus remained all along as to whether there are other valid formulations for fuzzy connectedness. One of the main contributions of this paper is a theoretical investigation under reasonable axioms to establish that maximum of path strengths of minimum of affinities along each path is indeed the one and only valid choice for defining fuzzy connectedness. In the past, a single fuzzy connected object was specified through a single seed element indicated interactively within the object region. When many objects are to be identified in a single image, interactive seed specification becomes cumbersome. Further, selecting exactly one element in each region automatically is more difficult than identifying a set of elements. Hence the theory and algorithms for the single-element case need to be generalized to multiple elements for more effective practical use. This is the second main contribution of this paper. The importance of multiple seeded fuzzy connected object definition from the considerations of both practicality and automation in image segmentation are described and illustrated with examples taken from several real medical applications.     

Multi-object reconstruction from dynamic scenes: An object-centered approach

In this paper, we present a new framework for three-dimensional (3D) reconstruction of multiple rigid objects from dynamic scenes. Conventional 3D reconstruction from multiple views is applicable to static scenes, in which the configuration of objects is fixed while the images are taken. In our framework, we aim to reconstruct the 3D models of multiple objects in a more general setting where the configuration of the objects varies among views. We solve this problem by object-centered decomposition of the dynamic scenes using unsupervised co-recognition approach. Unlike conventional motion segmentation algorithms that require small motion assumption between consecutive views, co-recognition method provides reliable accurate correspondences of a same object among unordered and wide-baseline views. In order to segment each object region, we benefit from the 3D sparse points obtained from the structure-from-motion. These points are reliable and serve as automatic seed points for a seeded-segmentation algorithm. Experiments on various real challenging image sequences demonstrate the effectiveness of our approach, especially in the presence of abrupt independent motions of objects.     

Operator Splittings,Bregman Methods and Frame Shrinkage in Image Processing

We examine the underlying structure of popular algorithms for variational methods used in image processing. We focus here on operator splittings and Bregman methods based on a unified approach via fixed point iterations and averaged operators. In particular, the recently proposed alternating split Bregman method can be interpreted from different points of view—as a Bregman, as an augmented Lagrangian and as a Douglas-Rachford splitting algorithm which is a classical operator splitting method. We also study similarities between this method and the forward-backward splitting method when applied to two frequently used models for image denoising which employ a Besov-norm and a total variation regularization term, respectively. In the first setting, we show that for a discretization based on Parseval frames the gradient descent reprojection and the alternating split Bregman algorithm are equivalent and turn out to be a frame shrinkage method. For the total variation regularizer, we also present a numerical comparison with multistep methods.     

一种求取物体凹点的算法研究

在图像识别的应用中,物体凹凸情况的分析是非常重要的,它既是描述物体形状的一个特征参数,也是一些重叠物体分离算法的前提。目前,求取物体边界凹点的算法已有多种,但都存在一些不足之处,为此,本文介绍了一种简便算法,该算法在我们开发的微生物细胞图像自动判读系统中已得到应用。     

基于Hough变换和连通体分析的混合圆形体检测算法

从复杂图像中准确可靠地检测圆形体是计算机视觉和智能化图像理解的关键技术之一. 现存算法存在检测精度低, 对噪声及复杂背景敏感等缺点. 本文提出一种新的混合算法. 首先采用一种改进 Hough 变换获取参数空间并生成其横切面图像; 然后对该横切面图像进行连通体分析, 检测出圆形体的尺寸和中心位置. 改进 Hough 变换重新定义了掩模及积分算子; 连通体分析则采用一种改进圆形测度. 大量实验表明所提算法具有更高检测率、检测精度和鲁棒性.     

ASOD: Arbitrary shape object detection

Arbitrary shape object detection, which is mostly related to computer vision and image processing, deals with detecting objects from an image. In this paper, we consider the problem of detecting arbitrary shape objects as a clustering application by decomposing images into representative data points, and then performing clustering on these points. Our method for arbitrary shape object detection is based on COMUSA which is an efficient algorithm for combining multiple clusterings. Extensive experimental evaluations on real and synthetically generated data sets demonstrate that our method is very accurate and efficient.     

基于集成分类算法的自动图像标注

基于语义的图像检索技术中,按照图像的语义进行自动标注是一个具有挑战性的工作. 本文把图像的自动标注过程转化为图像分类的过程,通过有监督学习对每个图像区域分类并得到相应关键字,实现标注. 采用一种快速随机森林(Fast random forest, FRF)集成分类算法,它可以对大量的训练数据进行有效的分类和标注. 在基于Corel数据集的实验中,相比经典算法, FRF改善了运算速度,并且分类精度保持稳定. 在图像标注方面有很好的应用.     

Handprinted word recognition on a NIST data set

An approach to handprinted word recognition is described. The approach is based on the use of generating multiple possible segmentations of a word image into characters and matching these segmentations to a lexicon of candidate strings. The segmentation process uses a combination of connected component analysis and distance transform-based, connected character splitting. Neural networks are used to assign character confidence values to potential character within word images. Experimental results are provided for both character and word recognition modules on data extracted from the NIST handprinted character database.     

Separation of Reflection Components Using Color and Polarization

Specular reflections and interreflections produce strong highlights in brightness images. These highlights can cause vision algorithms for segmentation, shape from shading, binocular stereo, and motion estimation to produce erroneous results. A technique is developed for separating the specular and diffuse components of reflection from images. The approach is to use color and polarization information, simultaneously, to obtain constraints on the reflection components at each image point. Polarization yields local and independent estimates of the color of specular reflection. The result is a linear subspace in color space in which the local diffuse component must lie. This subspace constraint is applied to neighboring image points to determine the diffuse component. In contrast to previous separation algorithms, the proposed method can handle highlights on surfaces with substantial texture, smoothly varying diffuse reflectance, and varying material properties. The separation algorithm is applied to several complex scenes with textured objects and strong interreflections. The separation results are then used to solve three problems pertinent to visual perception; determining illumination color, estimating illumination direction, and shape recovery.     

Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object. We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per-pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach.     

A pairwise decision tree framework for hyperspectral classification

A novel pairwise decision tree (PDT) framework is proposed for hyperspectral classification, where no partitions and clustering are needed and the original C‐class problem is divided into a set of two‐class problems. The top of the tree includes all original classes. Each internal node consists of either a set of class pairs or a set of class pairs and a single class. The pairs are selected by the proposed sequential forward selection (SFS) or sequential backward selection (SBS) algorithms. The current node is divided into next‐stage nodes by excluding either class of each selected pair. In the classification, an unlabelled pixel is recursively classified into the next node, by excluding the less similar class of each node pair until the classification result is obtained. Compared to the single‐stage classifier approach, the pairwise classifier framework and the binary hierarchical classifier (BHC), experiments on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a nine‐class problem demonstrated the effectiveness of the proposed framework.