Unified framework for automated iris segmentation using distantly acquired face images

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.     

Selecting salient frames for spatiotemporal video modeling and segmentation.

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.     

基于梯度的极化SAR图像超像素分割

超像素分割在图像分割领域以其优异的性能表现被广泛应用,准确性和高效性是评价分割性能的重要指标.简单线性迭代聚类（simple linear iterative clustering,SLIC）方法在光学图像上表现出了优异的性能,在极化合成孔径雷达（synthetic aperture radar,SAR）图像中也被广泛应用,然而SLIC方法中的初始化步骤不能准确地定位类中心,需要多次的迭代纠正误差.改进的分水岭方法（spatial constrained watershed,SCoW）是一种基于梯度阈值区分的简单且高效的分割方法,但是不能直接用于极化SAR图像.本文受SCoW的启发,提出一种对SLIC进行预处理的分割方法,通过横虚警（constant false alarm rate,CFAR）边缘检测器计算得到极化SAR图像的梯度信息,并将梯度信息用于初始化分割.基于两幅实测极化SAR图像,将本文提出方法与其他三种方法对比.实验表明本文方法可以减少整个算法的迭代次数,得到更加符合图像信息、贴合图像边界的分割结果.     

Reverse-Range-Doppler method for automated geocoding SAR images

Range-Doppler (RD) method and Reverse-Range-Doppler (RRD) method are combined together to achieve automatic geocoding of Synthetic Aperture Radar (SAR) image quickly and accurately in the paper. The RD method is firstly used to locate the four corners of the image, then the other pixels of the image can be located by Reverse-Range-Doppler (RRD) method. Resampling is performed at last. The approach has an advantage over previous techniques in that it does not require ground control points and is independent of spacecraft attitude knowledge or control. It can compensate the shift due to the assumed Doppler frequency in SAR image preprocessing. RRD simplifies the process of RD, therefore speeds up the computation. The experimental results show that a SAR image can be automated geocoded in 30 s using the single CPU (3 GHz) with 1 G memory and an accuracy of 10 m is attainable with this method.     

Level set analysis for leukocyte detection and tracking

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.     

Knowledge-based segmentation of Landsat images

A knowledge-based approach for Landsat image segmentation is proposed. The image segmentation problem is solved by extracting kernel information from the input image to provide an initial interpretation of the image and by using a knowledge-based hierarchical classifier to discriminate between major land-cover types in the study area. The proposed method is designed in such a way that a Landsat image can be segmented and interpreted without any prior image-dependent information. The general spectral land-cover knowledge is constructed from the training land-cover data, and the road information of an image is obtained through a road-detection program     

Intensity-based segmentation of microarray images

The underlying principle in microarray image analysis is that the spot intensity is a measure of the gene expression. This implicitly assumes the gene expression of a spot to be governed entirely by the distribution of the pixel intensities. Thus, a segmentation technique based on the distribution of the pixel intensities is appropriate for the current problem. In this paper, clustering-based segmentation is described to extract the target intensity of the spots. The approximate boundaries of the spots in the microarray are determined by manual adjustment of rectilinear grids. The distribution of the pixel intensity in a grid containing a spot is assumed to be the superposition of the foreground and the local background. The k-means clustering technique and the partitioning around medoids (PAM) were used to generate a binary partition of the pixel intensity distribution. The median (k-means) and the medoid (PAM) of the cluster members are chosen as the cluster representatives. The effectiveness of the clustering-based segmentation techniques was tested on publicly available arrays generated in a lipid metabolism experiment (Callow et al., 2000). The results are compared against those obtained using the region-growing approach (SPOT) (Yang et al., 2001). The effect of additive white Gaussian noise is also investigated.     

Optimizing block-thresholding segmentation for multilayercompression of compound images

Compound document images contain graphic or textual content along with pictures. They are a very common form of documents, found in magazines, brochures, Web sites, etc. We focus our attention on the mixed raster content (MRC) multilayer approach for compound image compression. We study block thresholding as a means to segment an image for MRC. An attempt is made to optimize the block threshold in a rate-distortion sense. Also, a fast algorithm is presented to approximate the optimized method. Extensive results are presented including rate-distortion curves, segmentation masks and reconstructed images, showing the performance of the proposed algorithm.     

Unsupervised approach for segmentation of textured cytologic images

A procedure is introduced for the segmentation of textured cytologic cell images. The cell region is determined by a set of coefficients that are gradually adjusted while minimising the defined cost function. Experiments have demonstrated the promising segmentation performance of the procedure for textured cytologic cell images     

A semisupervised segmentation model for collections of images

In this paper, we consider the problem of segmentation of large collections of images. We propose a semisupervised optimization model that determines an efficient segmentation of many input images. The advantages of the model are twofold. First, the segmentation is highly controllable by the user so that the user can easily specify what he/she wants. This is done by allowing the user to provide, either offline or interactively, some (fully or partially) labeled pixels in images as strong priors for the model. Second, the model requires only minimal tuning of model parameters during the initial stage. Once initial tuning is done, the setup can be used to automatically segment a large collection of images that are distinct but share similar features. We will show the mathematical properties of the model such as existence and uniqueness of solution and establish a maximum/minimum principle for the solution of the model. Extensive experiments on various collections of biological images suggest that the proposed model is effective for segmentation and is computationally efficient.     

Bayesian decision feedback for segmentation of binary images

We present real-time algorithms for the segmentation of binary images modeled by Markov mesh random fields (MMRFs) and corrupted by independent noise. The goal is to find a recursive algorithm to compute the maximum a posteriori (MAP) estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. First, this MAP fixed-lag estimation problem is set up and the corresponding optimal recursive (but computationally complex) estimator is derived. Then, both hard and soft (conditional) decision feedbacks are introduced at appropriate stages of the optimal estimator to reduce the complexity. The algorithm is applied to several synthetic and real images. The results demonstrate the viability of the algorithm both complexity-wise and performance-wise, and show its subjective relevance to the image segmentation problem.     

Optimal segmentation of cell images

An optimal segmentation algorithm for light microscopic cell images is presented. The image segmentation is performed by thresholding a parametric image approximating the original image. Using the mean squared error between the original and the constructed image as the cost function, the segmentation problem is transformed into an optimisation process where parametric parameters are determined that minimise the defined cost function. The cost function is iteratively minimised using an unsupervised learning rule to adjust the parameters, and a parametric image is constructed at each iteration, based on the obtained parameters. The cell region is extracted by thresholding the final parametric image, where the threshold is one of the image parameters. Application results to real cervical images are provided to show the performance of the proposed segmentation approach. Experimental segmentation results are presented for the proposed optimal algorithm for synthetic cell images corrupted by variant levels of noise; these results are compared with the K-means clustering method and Bayes classifier in terms of classification errors     

Performance measures for video object segmentation and tracking

We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.     

Automatic video segmentation and tracking for content-based applications

Advanced multimedia applications have to provide content-related functionalities such as search and retrieval of meaningful objects, detection and analysis of events, and understanding of scenes, which allow the user to access and manipulate the multimedia content with greater flexibility. This greatly depends on automatic techniques for extracting such objects from multimedia data. In this article we intend to provide a tutorial on the state-of-the-art in video segmentation and tracking technology with particular attention paid to the recent developments in attention-based object extraction. Performance results are included to highlight this emerging technology     

一种视觉表格图像全局阈值分割算法

针对复杂环境因素影响下的视觉表格图像,提出了一种有效的图像分割方法。首先利用图像的闭运算,估计出图像的背景变化,进而利用定义的图像对比度实现了图像的背景补偿。然后利用背景估计图像的均值作为补偿图像的全局分割阈值,成功实现了表格图像分割。实验仿真表明,本文方法的分割结果体现了较好的视觉效果,而且与基于补偿图像的OSTU方法和基于局部阈值的Niblack方法相比,它在图像单元格区域的过分割与欠分割之间实现了较好的均衡。     

A parametric fitting algorithm for segmentation of cell images

This paper presents a parametric fitting algorithm for segmentation of cervical and breast cell images from cytology smears. A parametric elliptical model for cells is introduced and the parameters adjusted to fit the cell shapes while minimizing a cost function. Segmentation results of noisy human cervical cell and textured breast cell images demonstrate that the proposed parametric fitting algorithm is very successful in segmentation of images of both nonoverlapped and overlapped elliptically shaped cells     

Image segmentation for compression of images and image sequences

Region-based image coding schemes, the so-called second generation techniques, have gained much favour in recent years. For still picture coding, they can increase the compression ratio obtained by transform coding by an order of magnitude, while maintaining adequate image representation. The success of these techniques relies on the ability to describe regions in an image succinctly by their shape and size. The algorithms presented describe methods for segmenting images. Unlike most other region based algorithms, these algorithms incorporate knowledge of the border coding process in deciding how to partition the image. The extension from single image compression to sequential image compression is also considered. A new, efficient segmentation scheme is proposed that exploits temporal redundancies between successive images, and reduces some problems associated with error accumulation in error images     

Rule-based automatic segmentation of color images

A rule-based segmentation algorithm for color images has been presented in this paper. The proposed strategy is similar to region growing algorithm where the seed points are automatically selected and grown. The similarity percents of neighboring pixels are calculated by means of fuzzy reasoning rules, and the merging of the pixels with regions is performed by comparing the similarity percent with the similarity threshold value. The algorithm does not require any prior knowledge of the number of regions existing in the image and decreases the computational load required for the fuzzy c-means (FCM). Several computer simulations have been performed and the results have been discussed. The simulation results indicate that the proposed algorithm yields segmented color image of perfect accuracy.     

Multiscale morphological segmentation of gray-scale images

In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.     

Precise segmentation of multimodal images.

We propose new techniques for unsupervised segmentation of multimodal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of grey levels. We follow the most conventional approaches in that initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. However, our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. We modify an expectation-maximization (EM) algorithm to deal with the LCGs and also propose a novel EM-based sequential technique to get a close initial LCG approximation with which the modified EM algorithm should start. The proposed technique identifies individual LCG models in a mixed empirical distribution, including the number of positive and negative Gaussians. Initial segmentation based on the LCG models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments show that the developed techniques segment different types of complex multimodal medical images more accurately than other known algorithms.