Iris segmentation using variational level set method

Continuous efforts have been made to process degraded iris images for enhancement of the iris recognition performance in unconstrained situations. Recently, many researchers have focused on developing the iris segmentation techniques, which can deal with iris images in a non-cooperative environment where the probability of acquiring unideal iris images is very high due to gaze deviation, noise, blurring, and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection of iris images captured in a closely controlled environment. The novelty of this research effort is that we propose to apply a variational level set-based curve evolution scheme that uses a significantly larger time step to numerically solve the evolution partial differential equation (PDE) for segmentation of an unideal iris image accurately, and thereby, speeding up the curve evolution process drastically. The iris boundary represented by the variational level set may break and merge naturally during evolution, and thus, the topological changes are handled automatically. The proposed variational model is also robust against poor localization and weak iris/sclera boundaries. In order to solve the size irregularities occurring due to arbitrary shapes of the extracted iris/pupil regions, a simple method is applied based on connection of adjacent contour points. Furthermore, to reduce the noise effect, we apply a pixel-wise adaptive 2D Wiener filter. The verification and identification performance of the proposed scheme is validated on three challenging iris image datasets, namely, the ICE 2005, the WVU Unideal, and the UBIRIS Version 1.     

基于窄带多区域水平集方法的遥感图像分割

由于遥感图像存在边缘混叠等问题,经典的C-V模型会产生大量的冗余轮廓,而且无法分割多个同质区域的目标.为此,提出了基于C-V模型的窄带多区域水平集图像分割方法,采用N-1个水平集函数将图像分割成N(N＞1)个区域,每个水平集函数表达一个区域.该方法一方面通过建立独立多区域水平集模型可以消除多余的轮廓,避免分割区域的重叠...     

Fast image enhancement using multi-scale saliency extraction in infrared imagery

We propose a multi-scale saliency extraction based fast infrared image enhancement approach. A local frequency-tuned based saliency extraction technique is designed for highlighting the salient regions, firstly. Then, multi-scale saliency extraction is demonstrated, introducing multi-scale local windows with different sizes to extract regions of interest at different scales. Finally, the original image is enhanced with combining multi-scale salient image regions into one image. The experimental results prove the proposed approach is robust and efficient for infrared image enhancement.     

基于C-V模型的改进快速水平集图像分割法

针对水平集方法计算复杂度高,无法满足实时系统要求的缺陷,提出一种改进的快速水平集算法。该算法对快速水平集算法进行简化,采用单链表表示轮廓曲线。利用C-V模型的二值拟合项来设计曲线演化的速度函数,保留了C-V模型的全局优化特性。还给出了一个基于单链表中轮廓点个数变化的水平集演化终止准则。该算法不仅明显提高了分割速度,且对噪声图像也能实现高效的分割。     

A new level set model for cell image segmentation

In this paper we first determine three phases of cell images:background,cytoplasm and nucleolus according to the general physical characteristics of cell images,and then develop a variational model,based on these characteristics,to segment nucleolus and cytoplasm from their relatively complicated backgrounds.In the meantime,the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model,which can speed up the segmentation and present satisfactory results in cell image processing.     

A hybrid level set method for modelling detonation and combustion problems in complex geometries

We present an accurate and fast wave tracking method that uses parametric representations of tracked fronts, combined with modifications of level set methods that use narrow bands. Our strategy generates accurate computations of the front curvature and other geometric properties of the front. We introduce data structures that can store discrete representations of the location of the moving fronts and boundaries, as well as the corresponding level set fields, that are designed to reduce computational overhead and memory storage. We present an algorithm we call stack sweeping to efficiently sort and store data that is used to represent orientable fronts. Our implementation features two reciprocal procedures, a forward ‘front parameterization’ that constructs a parameterization of a front given a level set field and a backward ‘field construction’ that constructs an approximation of the signed normal distance to the front, given a parameterized representation of the front. These reciprocal procedures are used to achieve and maintain high spatial accuracy. Close to the front, precise computation of the normal distance is carried out by requiring that displacement vectors from grid points to the front be along a normal direction. For front curves in two dimensions, a cubic interpolation scheme is used, and G ¹ surface parameterization based on triangular patches is used for the three-dimensional implementation to compute the distances from grid points near the front. To demonstrate this new, high accuracy method we present validations and show examples of combustion-like applications that include detonation shock dynamics, material interface motions in a compressible multi-material simulation and the Stephan problem associated with dendrite solidification.     

A conservative level set method for contact line dynamics

A new model for simulating contact line dynamics is proposed. We apply the idea of driving contact-line movement by enforcing the equilibrium contact angle at the boundary, to the conservative level set method for incompressible two-phase flow [E. Olsson, G. Kreiss, A conservative level set method for two phase flow, J. Comput. Phys. 210 (2005) 225–246]. A modified reinitialization procedure provides a diffusive mechanism for contact-line movement, and results in a smooth transition of the interface near the contact line without explicit reconstruction of the interface. We are able to capture contact-line movement without loosing the conservation. Numerical simulations of capillary dominated flows in two space dimensions demonstrate that the model is able to capture contact line dynamics qualitatively correct.     

A robust level set method based on local statistical information for noisy image segmentation

The paper presents an improved local region-based active contour model for image segmentation, which is robust to noise. A data fitting energy functional is defined in terms of curves and the energy terms which are based on the differences between the local average intensity and the global intensity means. Then the energy is incorporated into a level set variational formulation, from which a curve evolution equation is derived for energy minimization. And then the level set function is regularized by Gaussian filter to keep smooth and eliminate the re-initialization. By using the local statistical information, the proposed model can handle with noisy images. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase one. Experimental results show desirable performances of the proposed model for both noisy synthetic and real images, especially with high level noise.     

红外图像的疲劳状态识别方法

目的：为了解决光照变化对疲劳检测系统造成的准确性不高的问题,提出了一种近红外环境下判断人眼状态的方法,即针对红外光补图像的人眼状态判断。方法：首先,利用Adaboost算法对人眼区域进行定位,在网格法标记人眼瞳孔部分的基础上,进行Retinex红外图像增强。接着,对二值化与边缘检测后的红外图像分别进行网格法闭合度计算,得到人眼闭合度。最后,根据闭合度计算结果设定双阈值并结合PERCLOS来判断眼睛状态。结果：在DM642硬件平台上进行疲劳检测试验,实验结果表明,人眼状态识别率达到了90%以上,且平均每秒能处理21帧图片。结论：证明了该方法不仅能有效解决光照变化带来的问题,而且满足疲劳状态检测系统的快速性、可靠性和有效性等要求。     

基于局部和全局信息的自适应水平集图像分割

针对仅采用局部或全局信息无法快速准确分割灰度不均匀图像的问题,提出了一种基于局部和全局信息的自适应水平集图像分割模型。首先,利用图像局部信息和全局信息建立局部能量项和全局能量项,并且利用演化曲线轮廓内外小邻域的灰度均值差作为自变量,建立了权重函数模型,实现了局部能量项和全局能量项之间权重的自适应调整,提高了模型分割灰度不均匀图像的效率和准确性。其次,提出了一种新的能量惩罚项,避免了水平集函数的重新初始化,增强了数值计算的稳定性。最后,为验证模型的优越性,将模型与CV模型、LBF模型和LGIF模型进行了对比,并通过分割时间、迭代次数以及相似度等指标对分割结果进行了客观、定量分析。最终结果表明:该模型不但对初始轮廓具有较高鲁棒性,而且对灰度不均匀图像具有较高的分割准确性与分割效率。     

Variational piecewise constant level set methods for shape optimization of a two-density drum

We apply the piecewise constant level set method to a class of eigenvalue related two-phase shape optimization problems. Based on the augmented Lagrangian method and the Lagrange multiplier approach, we propose three effective variational methods for the constrained optimization problem. The corresponding gradient-type algorithms are detailed. The first Uzawa-type algorithm having applied to shape optimization in the literature is proven to be effective for our model, but it lacks stability and accuracy in satisfying the geometry constraint during the iteration. The two other novel algorithms we propose can overcome this limitation and satisfy the geometry constraint very accurately at each iteration. Moreover, they are both highly initial independent and more robust than the first algorithm. Without penalty parameters, the last projection Lagrangian algorithm has less severe restriction on the time step than the first two algorithms. Numerical results for various instances are presented and compared with those obtained by level set methods. The comparisons show effectiveness, efficiency and robustness of our methods. We expect our promising algorithms to be applied to other shape optimization and multiphase problems.     

Adaptive variational curve smoothing based on level set method

This paper presents an adaptive method for variational curve smoothing based on level set implementation. A suitable cost functional is minimized via solving the derived Euler–Lagrangian equation, of which the discretization is conducted on unstructured triangular meshes by employing a simple and effective finite volume scheme. Through adaptive refinement of the mesh, the geometry features of the given curve can be well resolved in a cost-effective way. Various numerical experiments demonstrate the effectiveness and efficiency of the proposed approach.     

A multi-phase level set framework for source reconstruction in bioluminescence tomography

We propose a novel multi-phase level set algorithm for solving the inverse problem of bioluminescence tomography. The distribution of unknown interior source is considered as piecewise constant and represented by using multiple level set functions. The localization of interior bioluminescence source is implemented by tracing the evolution of level set function. An alternate search scheme is incorporated to ensure the global optimal of reconstruction. Both numerical and physical experiments are performed to evaluate the developed level set reconstruction method. Reconstruction results show that the proposed method can stably resolve the interior source of bioluminescence tomography.     

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.     

Background subtraction based level sets for human segmentation in thermal infrared surveillance systems

Based on the technique of background subtraction, two level set based active contour models (LSACs) named as RT-BSLSAC and EA-BSLSAC are proposed for human segmentation in thermal infrared surveillance systems. The energy functional of RT-BSLSAC is initially formulated with the spatial–temporal information extracted from the background-subtracted images that correspond to the current frame and its adjacent frames. Then, minimization of such functional is conducted by a real-time numeric scheme evolving a binary level set function (BLSF). When the BLSF converges, the moving humans in current frame are detected with relatively complete interiors and enclosed, smooth contours. EA-BSLSAC makes two improvements to RT-BSLSAC. First, the formulation of energy functional not only depends on spatial–temporal information but also the boundary information resulting from an edge detector. Second, the functional is minimized by a convex numeric scheme featured by initialization-invariance. As a result, EA-BSLSAC presents higher segmentation accuracy but at more computational cost in comparison with RT-BSLSAC. Experimental results from segmenting the real-world infrared surveillance clips validate the advantages of the proposed methods in accuracy, efficiency, and the coordination with other algorithmic components of an infrared surveillance system due to the cancellation of post-processing meaning to reach complete human interiors and exact silhouettes.     

Improved CE/SE scheme with particle level set method for numerical simulation of spall fracture due to high-velocity impact

In the present paper, an Eulerian scheme combined with the hybrid particle level set method for numerical simulation of spall fracture due to high-velocity impact is proposed. An axisymmetric framework is established, based on an improved CE/SE scheme, to solve the high-velocity impact problems with large deformations, high strain rates and spall fractures. The hybrid particle level set method is adopted for tracking material interfaces and describing the formation and propagation of a crack. A novel representation of crack by level set is proposed. Numerical simulations are carried out and compared to the corresponding experimental results. The numerical results are in good agreement with the experimental data. The edge effects are reproduced and the decrease of scab thickness with increase in impact velocity is observed owing to the numerical analysis. It is proved that our computational technique is feasible and reliable for analyzing the spall fracture.     

A conservative level set method suitable for variable-order approximations and unstructured meshes

This paper presents a formulation for free-surface computations capable of handling complex phenomena, such as wave breaking, without excessive mass loss or smearing of the interface. The formulation is suitable for discretizations using finite elements of any topology and order, or other approaches such as isogeometric and finite volume methods. Furthermore, the approach builds on standard level set tools and can therefore be used to augment existing implementations of level set methods with discrete conservation properties. Implementations of the method are tested on several difficult two- and three-dimensional problems, including two incompressible air/water flow problems with available experimental results. Linear and quadratic approximations on unstructured tetrahedral and trilinear approximations on hexahedral meshes were tested. Global conservation and agreement with experiments as well as computations by other researchers are obtained.     

A novel compression algorithm for infrared thermal image sequence based on K-means method

High resolution in space and time is becoming the new trend of thermographic inspection of equipments, therefore, the development of a fast and precise processing and data store technique of high resolution thermal image should be well studied. This article will propose a novel global compression algorithm, which will provide an effective way to improve the precision and processing speed of thermal image data. This new algorithm is based on the decay of the temperature of thermograph and the feature of thermal image morphology. Firstly, it will sort the data in space according to K-means method. Then it will employ classic fitting calculation to fit all the typical temperature decay curves. At last, it will use the fitting parameters of the curves as the parameters for compression and reconstruction of thermal image sequence to achieve the method for which the thermal image sequence can be compressed in space and time simultaneously. To validate the proposed new algorithm, the authors used two embedded defective specimens made of different materials to do the experiment. The results show that the proposed infrared thermal image sequence compression processing algorithm is an effective solution with high speed and high precision. Compared to the conventional method, the global compression algorithm is not only noise resistant but also can improve the computing speed in hundreds of times.     

An ensemble template algorithm for extracting targets from blurred infrared images

In this paper, an ensemble template algorithm is proposed to extract targets from blurred infrared images. First, the image pixels are divided according to their gray values into three pixel sets, a target set, a background set and the third set without class label. Second, the neighborhood statistical characteristics for each pixel are calculated as its template features. Third, ensemble detectors are designed using target pixels and background pixels based on their template features, and these ensemble detectors are used to detect the third pixel set. To evaluate the performance of the proposed extraction algorithm, this paper compares the ensemble template with other extraction algorithms using blurred infrared image of hand trace. Experimental results show that the ensemble template algorithm proposed in this paper exhibits better extraction performance.