Relevant bibliographies by topics / Geodesic active contour

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Geodesic active contour'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Geodesic active contour"

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Hua, Cui, and Gao Liqun. "Geodesic active contour, inertia and initial speed." Pattern Recognition Letters 29, no. 16 (2008): 2197–205. http://dx.doi.org/10.1016/j.patrec.2008.08.006.

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Zhu, Guopu. "Directional geodesic active contour for image segmentation." Journal of Electronic Imaging 16, no. 3 (2007): 030501. http://dx.doi.org/10.1117/1.2753836.

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Mustaffa, Maizatul Nadirah, Norma Alias, and Faridah Mustapha. "Some numerical methods for solving geodesic active contour model on image segmentation process." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-1 (2017): 408–11. http://dx.doi.org/10.11113/mjfas.v13n4-1.849.

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In this paper, we present an edge-based image segmentation technique using modified geodesic active contour model to detect the desired objects from an image. The stopping function of the proposed model has been modified from the usual geodesic active contour model. The modified geodesic active contour model is discretized using finite difference method based on the central difference formula. Then, some numerical methods such as RBGS and Jacobi methods are used for solving the linear system of equation. The accuracy and effectiveness of the proposed algorithm have been illustrated by applied to different images and some numerical methods.
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Feng, Rui Jie, and Hui Yan Jiang. "Edge Based Level Set with Gaussian Filtering Regularized and its Application in Liver Segmentation." Advanced Materials Research 121-122 (June 2010): 222–27. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.222.

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A novel edge-based active contour model (ACM) is proposed in this paper. Our edge-based active contour model has many advantages over the conventional active contour models. Firstly, the proposed model can get much smoother contour and needs much less iterations to evolution by being implemented with a special processing named Selectively Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method. Secondly, we introduce Bilateral Gaussian Filter which can preserve edges to smooth images. So we make weak edges more clear than traditional Gaussian Filter. Thirdly, the level set function can be easily initialized with binary function, which is more efficient to construct than the widely used signed distance function (SDF) because of the special processing. Experiments on synthetic image and segmenting liver from abdominal CT images demonstrate the advantages of the proposed method over geodesic active contours (GAC) in term of both efficiency and accuracy.
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Gout, Christian. "Viscosity solutions for geodesic active contour under geometrical conditions." International Journal of Computer Mathematics 85, no. 9 (2008): 1375–95. http://dx.doi.org/10.1080/00207160701482914.

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Sowmiya, D., and P. Anandhakumar. "Human Detection and Segmentation Using Automatic Geodesic Active Contours for Vision Based Activity Recognition Applications." Journal of Computational and Theoretical Nanoscience 15, no. 2 (2018): 409–16. http://dx.doi.org/10.1166/jctn.2018.7103.

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Vision-based activity recognition applications are in need of Automatic human detection and segmentation for surveillance purposes. Though there are many state-of-art methods in the literature, there still exist many challenges such as self-occlusion, illumination variations and sensitive to light conditions, appearance, and variations due to clothing. In this paper, a new novel framework for automatic detection and segmentation of the human region in a video sequence using Automatic Geodesic Active Contours is proposed. Normally geodesic active contours have static Region of coincidence but in this work, a dynamic Region of coincidence is proposed to draw the initial contours on the human region. To detect the human region the histogram of oriented gradients are computed and trained using SVM classifier. Once the human region is detected, the contour is drawn on the human region alone to segment the human region from the background. The proposed algorithm achieves an accuracy rate of 98%, 99% and 98% for KTH, Weizmann and U t-interaction dataset respectively.
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Ananth, K. R., and S. Pannirselvam. "A Geodesic Active Contour Level Set Method for Image Segmentation." International Journal of Image, Graphics and Signal Processing 4, no. 5 (2012): 31–37. http://dx.doi.org/10.5815/ijigsp.2012.05.04.

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Liu Bin, 刘斌, and 胡春海 Hu Chunhai. "Visual Attention-Driven Geodesic Active Contour Model and Its Application." Acta Optica Sinica 30, no. 10 (2010): 2800–2805. http://dx.doi.org/10.3788/aos20103010.2800.

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Le Guyader, Carole, and Christian Gout. "Geodesic active contour under geometrical conditions: theory and 3D applications." Numerical Algorithms 48, no. 1-3 (2008): 105–33. http://dx.doi.org/10.1007/s11075-008-9174-y.

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Xu, Hai Yong, and Ming Hua Liu. "Active Contour Driven by Edge and Region Image Fitting Energy." Advanced Materials Research 532-533 (June 2012): 892–96. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.892.

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In this paper, we propose a novel edge and region-based active contour model. We consider geodesic curve and region-based model, and evolve a contour based on global information. Moreover, we add to the level set regularization term in the energy functional to ensure accurate computation and avoids expensive re-initialization of the level set function. Experiments on synthetic and real images show desirable performances of our method.
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Dissertations / Theses on the topic "Geodesic active contour"

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Kasimoglu, Ismail Hakki. "Estimation of a Coronary Vessel Wall Deformation with High-Frequency Ultrasound Elastography." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19762.

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Elastography, which is based on applying pressure and estimating the resulting deformation, involves the forward problem to obtain the strain distributions and inverse problem to construct the elastic distributions consistent with the obtained strains on observation points. This thesis focuses on the former problem whose solution is used as an input to the latter problem. The aim is to provide the inverse problem community with accurate strain estimates of a coronary artery vessel wall. In doing so, a new ultrasonic image-based elastography approach is developed. Because the accuracy and quality of the estimated strain fields depend on the resolution level of the ultrasound image and to date best resolution levels obtained in the literature are not enough to clearly see all boundaries of the artery, one of the main goals is to acquire high-resolution coronary vessel wall ultrasound images at different pressures. For this purpose, first an experimental setup is designed to collect radio frequency (RF) signals, and then image formation algorithm is developed to obtain ultrasound images from the collected signals. To segment the noisy ultrasound images formed, a geodesic active contour-based segmentation algorithm with a novel stopping function that includes local phase of the image is developed. Then, region-based information is added to make the segmentation more robust to noise. Finally, elliptical deformable template is applied so that a priori information regarding the shape of the arteries could be taken into account, resulting in more stable and accurate results. The use of this template also implicitly provides boundary point correspondences from which high-resolution, size-independent, non-rigid and local strain fields of the coronary vessel wall are obtained.
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Appia, Vikram VijayanBabu. "Non-local active contours." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44739.

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This thesis deals with image segmentation problems that arise in various computer vision related fields such as medical imaging, satellite imaging, video surveillance, recognition and robotic vision. More specifically, this thesis deals with a special class of image segmentation technique called Snakes or Active Contour Models. In active contour models, image segmentation is posed as an energy minimization problem, where an objective energy function (based on certain image related features) is defined on the segmenting curve (contour). Typically, a gradient descent energy minimization approach is used to drive the initial contour towards a minimum for the defined energy. The drawback associated with this approach is that the contour has a tendency to get stuck at undesired local minima caused by subtle and undesired image features/edges. Thus, active contour based curve evolution approaches are very sensitive to initialization and noise. The central theme of this thesis is to develop techniques that can make active contour models robust against certain classes of local minima by incorporating global information in energy minimization. These techniques lead to energy minimization with global considerations; we call these models -- 'Non-local active contours'. In this thesis, we consider three widely used active contour models: 1) Edge- and region-based segmentation model, 2) Prior shape knowledge based segmentation model, and 3) Motion segmentation model. We analyze the traditional techniques used for these models and establish the need for robust models that avoid local minima. We address the local minima problem for each model by adding global image considerations.
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Niu, Xutong. "Highway extraction from high resolution aerial photography using a geometric active contour model." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1101833084.

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Facciolo, Furlan Gabriele. "Irregularly sampled image resortation and interpolation." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/22714.

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The generation of urban digital elevation models from satellite images using stereo reconstruction techniques poses several challenges due to its precision requirements. In this thesis we study three problems related to the reconstruction of urban models using stereo images in a low baseline disposition. They were motivated by the MISS project, launched by the CNES (Centre National d'Etudes Spatiales), in order to develop a low baseline acquisition model. The first problem is the restoration of irregularly sampled images and image fusion using a band limited interpolation model. A novel restoration algorithm is proposed, which incorporates the image formation model as a set of local constraints, and uses of a family of regularizers that allow to control the spectral behavior of the solution. Secondly, the problem of interpolating sparsely sampled images is addressed using a self-similarity prior. The related problem of image inpainting is also considered, and a novel framework for exemplar-based image inpainting is proposed. This framework is then extended to consider the interpolation of sparsely sampled images. The third problem is the regularization and interpolation of digital elevation models imposing geometric restrictions. The geometric restrictions come from a reference image. For this problem three different regularization models are studied: an anisotropic minimal surface regularizer, the anisotropic total variation and a new piecewise affine interpolation algorithm.<br>La generación de modelos urbanos de elevación a partir de imágenes de satélite mediante técnicas de reconstrucción estereoscópica presenta varios retos debido a sus requisitos de precisión. En esta tesis se estudian tres problemas vinculados a la generación de estos modelos partiendo de pares estereoscópicos adquiridos por satélites en una configuración con baseline pequeño. Estos problemas fueron motivados por el proyecto MISS, lanzado por el CNES (Centre National d'Etudes Spatiales) con el objetivo de desarrollar las técnicas de reconstrucción para imágenes adquiridas con baseline pequeños. El primer problema es la restauración de imágenes muestreadas irregularmente y la fusión de imágenes usando un modelo de interpolación de banda limitada. Se propone un nuevo método de restauración, el cual usa una familia de regularizadores que permite controlar el decaimiento espectral de la solución e incorpora el modelo de formación de imagen como un conjunto de restricciones locales. El segundo problema es la interpolación de imágenes muestreadas en forma dispersa usando un prior de auto similitud, se considera también el problema relacionado de inpainting de imágenes. Se propone un nuevo framework para inpainting basado en ejemplares, el cual luego es extendido a la interpolación de imágenes muestreadas en forma dispersa. El tercer problema es la regularización e interpolación de modelos digitales de elevación imponiendo restricciones geométricas las cuales se extraen de una imagen de referencia. Para este problema se estudian tres modelos de regularización: un regularizador anisótropo de superficie mínima, la variación total anisótropa y un nuevo algoritmo de interpolación afín a trozos.
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Chen, Da. "Nouveaux modèles de chemins minimaux pour l'extraction de structures tubulaires et la segmentation d'images." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED037/document.

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Dans les domaines de l’imagerie médicale et de la vision par ordinateur, la segmentation joue un rôle crucial dans le but d’extraire les composantes intéressantes d’une image ou d’une séquence d’images. Elle est à l’intermédiaire entre le traitement d’images de bas niveau et les applications cliniques et celles de la vision par ordinateur de haut niveau. Ces applications de haut niveau peuvent inclure le diagnostic, la planification de la thérapie, la détection et la reconnaissance d'objet, etc. Parmi les méthodes de segmentation existantes, les courbes géodésiques minimales possèdent des avantages théoriques et pratiques importants tels que le minimum global de l’énergie géodésique et la méthode bien connue de Fast Marching pour obtenir une solution numérique. Dans cette thèse, nous nous concentrons sur les méthodes géodésiques basées sur l’équation aux dérivées partielles, l’équation Eikonale, afin d’étudier des méthodes précises, rapides et robustes, pour l’extraction de structures tubulaires et la segmentation d’image, en développant diverses métriques géodésiques locales pour des applications cliniques et la segmentation d’images en général<br>In the fields of medical imaging and computer vision, segmentation plays a crucial role with the goal of separating the interesting components from one image or a sequence of image frames. It bridges the gaps between the low-level image processing and high level clinical and computer vision applications. Among the existing segmentation methods, minimal geodesics have important theoretical and practical advantages such as the global minimum of the geodesic energy and the well-established fast marching method for numerical solution. In this thesis, we focus on the Eikonal partial differential equation based geodesic methods to investigate accurate, fast and robust tubular structure extraction and image segmentation methods, by developing various local geodesic metrics for types of clinical and segmentation tasks. This thesis aims to applying different geodesic metrics based on the Eikonal framework to solve different image segmentation problems especially for tubularity segmentation and region-based active contours models, by making use of more information from the image feature and prior clinical knowledges. The designed geodesic metrics basically take advantages of the geodesic orientation or anisotropy, the image feature consistency, the geodesic curvature and the geodesic asymmetry property to deal with various difficulties suffered by the classical minimal geodesic models and the active contours models. The main contributions of this thesis lie at the deep study of the various geodesic metrics and their applications in medical imaging and image segmentation. Experiments on medical images and nature images show the effectiveness of the presented contributions
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Lorigo, Liana M., Olivier Faugeras, W. E. L. Grimson, Renaud Keriven, Ron Kikinis, and Carl-Fredrik Westin. "Co-dimension 2 Geodesic Active Contours for MRA Segmentation." 1999. http://hdl.handle.net/1721.1/5928.

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Automatic and semi-automatic magnetic resonance angiography (MRA)s segmentation techniques can potentially save radiologists larges amounts of time required for manual segmentation and cans facilitate further data analysis. The proposed MRAs segmentation method uses a mathematical modeling technique whichs is well-suited to the complicated curve-like structure of bloods vessels. We define the segmentation task as ans energy minimization over all 3D curves and use a level set methods to search for a solution. Ours approach is an extension of previous level set segmentations techniques to higher co-dimension.
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"Multiscale geodesic active contours and local phase information in ultrasonic applications: 多尺度測地主動輪廓線和局部相位信息在超聲應用中的使用". 2014. http://library.cuhk.edu.hk/record=b6115456.

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在各种临床应用广泛使用的诊断和治疗工具中，超声成像是其中的一个。与其他成像模式相比，比如计算机断层照相法和磁共振成像，超声波检查法有许多优点:没有辐射风险，设备价格低以及能够实时获取图像。很多超声应用的第一步通常是对感兴趣组织和结构的检测和定位。然而，超声图像存在一些特有的伪影，比如高噪声，低信噪比和灰度不均，这些伪影使得检测任务变得困难。此外，感兴趣区域之间的低对比度也使得这一任务变得更加复杂。在这篇论文里，我们深入研究这些图像伪影并提出新的方法来促进临床中的超声应用。<br>首先，我们提出一个多尺度的框架来进行超声图像的分割，这个框架是基于各向异性去噪扩散和测地主动轮廓线的。各向异性去噪扩散是对边缘敏感且专门用于斑点噪声图像的扩散过程，这里它被用来去除超声图像的斑点噪声，我们对每幅输入图像构造一个多尺度的表示方法，随着尺度的增加，噪声被逐渐地消除。之后，多尺度测地主动轮廓线从粗到细渐进地应用到这些尺度来提取物体的边界线。为了避免在低对比度区域出现边界泄漏的情况，我们把不同尺度之间的边界形状相似性结合到传统的测地主动轮廓线模型里作为一个外部约束来指导轮廓线的演化。在合成和临床图像的实验结果证明了我们的方法的优越性。<br>其次，我们提出一个基于相位的方法来检查和测量超声图像里的胎儿腹部轮廓线。我们定义了一个基于局部相位的度量来检测胎儿腹部的边界线，这个度量称为多尺度特征非对称性，它与图像的亮度无关，并且能为图像里特征的重要程度提供一个绝对的测量。为了估计与腹部轮廓线相吻合的椭圆，我们使用一个迭代随机霍夫变换来排除内腹部边界线的影响，从而使得估计的椭圆逐渐收敛到外边界线。在临床超声图像里进行腹部周长测量的实验结果验证了我们的方法与手工的方法有很高的一致性，这也表明我们的方法可以作为一个可靠的工具来进行产科的护理和诊断。<br>Ultrasound imaging is one of the most widely used diagnostic and therapeutic tools for a variety of clinical applications. Compared with other imaging modalities, such as computed tomography and magnetic resonance imaging, ultrasonography has a lot of advantages: free of radiation risk, low cost of acquisition and images are available in real-time. The first step in many ultrasonic applications is usually the detection and localization of interested tissues and structures. However, there are a number of characteristic artifacts in ultrasound images that make the task difficult such as high speckle noise, low signal-to-noise ratio and intensity inhomogeneity. Besides, the low contrast between regions of interest further complicates the processing. In this thesis, we deeply investigate these image artifacts and propose new techniques to facilitate ultrasonic applications in clinic.<br>First, we propose a multiscale framework for ultrasound image segmentation based on speckle reducing anisotropic diffusion(SRAD) and geodesic active contours (GAC). SRAD is an edge-sensitive diffusion tailored for speckled images, and it is adopted here to reduce speckle noise by constructing a multiscale representation for each input image, where the noise is gradually removed as the scale increases. Multiscale geodesic active contours are then applied along the scales in a coarse-to-fine manner to capture the object boundaries progressively. To avoid boundary leakages in low contrast regions, traditional GAC model is modified by incorporating the boundary shape similarity between different scales as an external constraint to guide the contour evolution. Experimental results in both synthetic and clinical images demonstrate the superiority of the proposed approach.<br>Second, we propose a phase-based approach for fetal abdominal contour detection and measurement in ultrasound images.We define a local phase-based measure, called multiscale feature asymmetry (MSFA), from the monogenic signal to detect the boundaries of fetal abdomen. The MSFA measure is intensity invariant and provides an absolute measurement for the significance of features in the image. In order to estimate the ellipse that fits to the abdominal contour, we employ an iterative randomized Hough transform to exclude the interferences of the inner boundaries of the abdomen, after which the estimated ellipse gradually converges to the outer boundaries. Experimental results in clinical ultrasound images validate the high agreement between our approach and manual approach in the measurement of abdominal circumference, indicating that the proposed approach can be used as a reliable tool for obstetric care and diagnosis.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Detailed summary in vernacular field only.<br>Wang, Weiming .<br>Thesis (Ph.D.) Chinese University of Hong Kong, 2014.<br>Includes bibliographical references (leaves 68-84).<br>Abstracts also in Chinese.<br>Wang, Weiming .
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Book chapters on the topic "Geodesic active contour"

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Chang, Yuan-Tsung, Chih-Wen Ou, Timothy K. Shih, and Yung-Hui Li. "Performing Iris Segmentation by Using Geodesic Active Contour (GAC)." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7398-4_3.

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Doğan, Günay. "An Efficient Lagrangian Algorithm for an Anisotropic Geodesic Active Contour Model." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58771-4_33.

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Guo, Chunsheng, and Zhiyu Wang. "Graph-Cut Optimization for Video Moving Objects Detection with Geodesic Active Contour." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33478-8_55.

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Dasgupta, Avijit, Sudipta Mukhopadhyay, Shrikant A. Mehre, and Parthasarathi Bhattacharyya. "Morphological Geodesic Active Contour Based Automatic Aorta Segmentation in Thoracic CT Images." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2107-7_17.

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Suzuki, Kenji. "Computerized Segmentation of Organs by Means of Geodesic Active-Contour Level-Set Algorithm." In Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-8195-0_4.

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Shang, Yanfeng, Xin Yang, Ming Zhu, Biao Jin, and Ming Liu. "Region and Shape Prior Based Geodesic Active Contour and Application in Cardiac Valve Segmentation." In Computational Science and Its Applications – ICCSA 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424925_115.

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Barizon, Gustavo Canavaci, Antonio Carlos da Silva Senra Filho, André Schmidt, and Luiz Otávio Murta Junior. "Evaluation of Left Ventricle Myocardium Detection by a Fully Automatic Segmentation Using Geodesic Active Contour." In XXVI Brazilian Congress on Biomedical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2517-5_32.

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Won, Chul-Ho, Seung-Ik Lee, Dong-Hun Kim, and Jin-Ho Cho. "Lung Detection by Using Geodesic Active Contour Model Based on Characteristics of Lung Parenchyma Region." In Advances in Multimedia Information Processing - PCM 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11581772_78.

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Medeiros, Aldísio G., Lucas de O. Santos, Roger Moura Sarmento, Elizângela de Souza Rebouças, and Pedro P. Rebouças Filho. "New Adaptive Morphological Geodesic Active Contour Method for Segmentation of Hemorrhagic Stroke in Computed Tomography Image." In Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61380-8_41.

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Huynh, Hieu Trung, Ibrahim Karademir, Aytekin Oto, and Kenji Suzuki. "Liver Volumetry in MRI by Using Fast Marching Algorithm Coupled with 3D Geodesic Active Contour Segmentation." In Computational Intelligence in Biomedical Imaging. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7245-2_6.

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Conference papers on the topic "Geodesic active contour"

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Zhang, Bo, Yongli Su, Yongfeng Xu, and Shuling Zhang. "An adaptive Geodesic Active Contour model." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5584107.

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Gang Xu and Lei Shi. "Using Geodesic Active Contours for motion-blurred images contour detection." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620929.

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Lou, Qiong, and Dexing Kong. "A novel non-local geodesic active contour model." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469962.

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Van-Truong Pham, Thi-Thao Tran, Yun-Jen Chiu, and Kuo-Kai Shyu. "Region-aided Geodesic Active Contour model for image segmentation." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5563948.

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Xie, X., and M. Mirmehdi. "Geodesic Colour Active Contour Resistent to Weak Edges and Noise." In British Machine Vision Conference 2003. British Machine Vision Association, 2003. http://dx.doi.org/10.5244/c.17.41.

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Yong, Tan. "Segmentation of Blood Cell Micrograph Based on Geodesic Active Contour." In 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2013. http://dx.doi.org/10.1109/ihmsc.2013.216.

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Liu, Changzheng, Junlin Ma, and Guiyun Ye. "Medical Image Segmentation by Geodesic Active Contour Incorporating Region Statistical Information." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.393.

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Wang, Kaibin, and Bianzhang Yu. "Texture Image Segmentation Using Without Re-initialization Geodesic Active Contour Model." In International Conference on Intelligent Systems and Knowledge Engineering 2007. Atlantis Press, 2007. http://dx.doi.org/10.2991/iske.2007.68.

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Gai, Jiading, and Robert L. Stevenson. "Contour tracking based on a synergistic approach of geodesic active contours and conditional random fields." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651053.

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Prakash, Surya. "Multiple Textured Objects Segmentation Using DWT Based Texture Features in Geodesic Active Contour." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.17.

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