Bibliografie tematiche / Unsupervised image segmentation

Letteratura scientifica selezionata sul tema "Unsupervised image segmentation"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Unsupervised image segmentation":

Demir, Önder, e Buket Doğan. "Unsupervised Image Segmentation Using Textural Features". International Journal of Signal Processing Systems 5, n. 3 (settembre 2017): 112–15. http://dx.doi.org/10.18178/ijsps.5.3.112-115.

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K., Sundeep Kumar, Jacob C. F. e Eswara Reddy B. "UNSUPERVISED WOUND IMAGE SEGMENTATION". ICTACT Journal on Image and Video Processing 04, n. 03 (1 febbraio 2014): 737–47. http://dx.doi.org/10.21917/ijivp.2014.0107.

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Peng, Wei Fu, Shu Du e Fu Xiang Li. "Unsupervised Image Segmentation via Affinity Propagation". Applied Mechanics and Materials 610 (agosto 2014): 464–70. http://dx.doi.org/10.4028/www.scientific.net/amm.610.464.

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Abstract (sommario):

Image segmentation is an important research subject in the area of image processing. Most of the existing image segmentation methods partition the image based on the single cue of the image, the color, which brings a serious limitation when the complex scenes involve in the natural images. In this paper, we introduce a novel unsupervised image segmentation method via affinity propagation which takes into local texture and color features with superpixel map. The new method fuses color and texture information as local feature of each superpixel. The experimental results show that the proposed method performs better and steadier when partitioning various complex nature images, comparing to the existing methods.

Panić, Branislav, Marko Nagode, Jernej Klemenc e Simon Oman. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks". Mathematics 10, n. 22 (16 novembre 2022): 4301. http://dx.doi.org/10.3390/math10224301.

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Abstract (sommario):

Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.

Ikokou, Guy Blanchard, e Kate Miranda Malale. "Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications". Geomatics 4, n. 2 (12 maggio 2024): 149–72. http://dx.doi.org/10.3390/geomatics4020009.

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Abstract (sommario):

Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process.

Yu, Sheng-yang, Yan Zhang, Yong-gang Wang e Jie Yang. "Unsupervised color-texture image segmentation". Journal of Shanghai Jiaotong University (Science) 13, n. 1 (febbraio 2008): 71–75. http://dx.doi.org/10.1007/s12204-008-0071-2.

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Arasy, Muhammad Hariz, Suyanto Suyanto e Kurniawan Nur Ramadhani. "Aerial Image Segmentation with Clustering Using Fireworks Algorithm". Indonesian Journal on Computing (Indo-JC) 4, n. 1 (22 marzo 2019): 19. http://dx.doi.org/10.21108/indojc.2019.4.1.245.

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Abstract (sommario):

Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

Mohapatra, Subrajeet, Dipti Patra e Kundan Kumar. "Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering". ISRN Artificial Intelligence 2012 (1 marzo 2012): 1–12. http://dx.doi.org/10.5402/2012/923946.

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Abstract (sommario):

The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

Ortiz, A., J. M. Gorriz, J. Ramirez e D. Salas-Gonzalez. "Unsupervised Neural Techniques Applied to MR Brain Image Segmentation". Advances in Artificial Neural Systems 2012 (7 giugno 2012): 1–7. http://dx.doi.org/10.1155/2012/457590.

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Abstract (sommario):

The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD). Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM) as the core of the algorithms. The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes. Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR) outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid. Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain).

Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez e M. A. Ibarra-Manzano. "Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior". Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/909625.

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This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support.

Più fonti

Tesi sul tema "Unsupervised image segmentation":

Horne, Caspar. "Unsupervised image segmentation /". Lausanne : EPFL, 1991. http://library.epfl.ch/theses/?nr=905.

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Baumann, Oliver Nicholas. "Connected operators for unsupervised image segmentation". Thesis, University of Southampton, 2004. https://eprints.soton.ac.uk/66319/.

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Abstract (sommario):

Image segmentation forms the first stage in many image analysis procedures including image sequence re-timing and the emerging field of content based retrieval. By dividing the image into a set of disjoint connected regions, each of which is homogeneous with respect to some measure of the image content, the scene can be analysed and metadata extracted more efficiently, and in many cases more effectively, than on a pixel by pixel basis. Though a great number of segmentation techniques exist (and continue to be developed,) many of them fall short of the requirements of these applications. This thesis first defines these requirements and reviews established segmentation methods describing their qualities and shortfalls. Selecting the watershed transform and connected operators from those techniques reviewed a number of novel adaptations are introduced, developed and shown to produce pleasing results both in terms of a new evaluation metric and subjective appraisal. Finally, the use of the image segmentation is shown to improve established methods of image noise removal using the discrete wavelet transform.

Barker, S. A. "Unsupervised image segmentation using Markov Random Field models". Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596368.

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Abstract (sommario):

The development of a fully unsupervised algorithm to achieve image segmentation is the central theme of this dissertation. Existing literature falls short of such a goal providing many algorithms capable of solving a subset of this highly challenging problem. Unsupervised segmentation is the process of identifying and locating the constituent regions of an observed image, while having no prior knowledge of the number of regions. The problem can be formulated in a Bayesian framework and through the use of an assumed model unsupervised segmentation can be posed as a problem of optimisation. This is the approach pursued throughout this dissertation. Throughout the literature, the commonly adopted model is an hierarchical image model whose underlying components are various forms of Markov Random Fields. Gaussian Markov Random Field models are used to model the textural content of the observed image's regions, while a Potts model provides a regularisation function for the segmentation. The optimisation of such highly complicated models is a topic that has challenged researchers for several decades. The contribution of this thesis is the introduction of new techniques allowing unsupervised segmentation to be carried using a single optimisation process. It is hoped that these algorithms will facilitate the future study of hierarchical image models and in particular the discovery of further models capable of more closely fitting real world data. The extensive literature surrounding Markov Random Field models and their optimisation is reviewed early in this dissertation, as is the literature concerning the selection of features to identify the textural content of an observed image. In the light of these reviews new algorithms are proposed that achieve a fusion between concepts originating in both these areas. Algorithms previously applied in statistical mechanics form an important part of this work. The use of various Markov Chain Monte Carlo algorithms is prevalent and in particular, the reversible jump sampling algorithm is of great significance. It is the combination of several of these algorithms to form a single optimisation framework that lies at the heart of the most successful algorithms presented here.

Kam, Alvin Harvey Siew Wah. "A general multiscale scheme for unsupervised image segmentation". Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621969.

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Islam, Mofakharul University of Ballarat. "Unsupervised Color Image Segmentation Using Markov Random Fields Model". University of Ballarat, 2008. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12827.

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Abstract (sommario):

We propose a novel approach to investigate and implement unsupervised segmentation of color images particularly natural color images. The aim is to devise a robust unsu- pervised segmentation approach that can segment a color textured image accurately. Here, the color and texture information of each individual pixel along with the pixel's spatial relationship within its neighborhood have been considered for producing precise segmentation of color images. Precise segmentation of images has tremendous potential in various application domains like bioinformatics, forensics, security and surveillance, the mining and material industry and medical imaging where subtle information related to color and texture is required to analyze an image accurately. We intend to implement a robust unsupervised segmentation approach for color im- ages using a newly developed multidimensional spatially variant ¯nite mixture model (MSVFMM) using a Markov Random Fields (MRF) model for improving the over- all accuracy in segmentation and Haar wavelet transform for increasing the texture sensitivity of the proposed approach. [...]
Master of Computing

Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.

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Abstract (sommario):

Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images automatically. However, these methods heavily rely on prior knowledge, which limits their generalization ability on various biomedical images. With the recent advance of the deep learning technique, convolutional neural network (CNN) based methods have achieved state-of-the-art performance on various nature and biomedical image segmentation tasks. The great success of the CNN based segmentation methods results from the ability to learn contextual and local information from the high dimensional feature space. However, the biomedical image segmentation tasks are particularly challenging, due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries. To this end, it is necessary to establish automated deep learning-based segmentation paradigms, which are capable of processing the complicated semantic and morphological relationships in various biomedical images. In this thesis, we propose novel deep learning-based methods for fully supervised and unsupervised biomedical image segmentation tasks. For the first part of the thesis, we introduce fully supervised deep learning-based segmentation methods on various biomedical image analysis scenarios. First, we design a panoptic structure paradigm for nuclei instance segmentation in the histopathology images, and cell instance segmentation in the fluorescence microscopy images. Traditional proposal-based and proposal-free instance segmentation methods are only capable to leverage either global contextual or local instance information. However, our panoptic paradigm integrates both of them and therefore achieves better performance. Second, we propose a multi-level feature fusion architecture for semantic neuron membrane segmentation in the electron microscopy (EM) images. Third, we propose a 3D anisotropic paradigm for brain tumor segmentation in magnetic resonance images, which enlarges the model receptive field while maintaining the memory efficiency. Although our fully supervised methods achieve competitive performance on several biomedical image segmentation tasks, they heavily rely on the annotations of the training images. However, labeling pixel-level segmentation ground truth for biomedical images is expensive and labor-intensive. Subsequently, exploring unsupervised segmentation methods without accessing annotations is an important topic for biomedical image analysis. In the second part of the thesis, we focus on the unsupervised biomedical image segmentation methods. First, we proposed a panoptic feature alignment paradigm for unsupervised nuclei instance segmentation in the histopathology images, and mitochondria instance segmentation in EM images. To the best of our knowledge, we are for the first time to design an unsupervised deep learning-based method for various biomedical image instance segmentation tasks. Second, we design a feature disentanglement architecture for unsupervised object recognition. In addition to the unsupervised instance segmentation for the biomedical images, our method also achieves state-of-the-art performance on the unsupervised object detection for natural images, which further demonstrates its effectiveness and high generalization ability.

Zhang, Xinwen. "Multi-modality Medical Image Segmentation with Unsupervised Domain Adaptation". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29776.

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Abstract (sommario):

Advances in medical imaging have greatly aided in providing accurate and fast medical diagnosis, followed by recent deep learning developments enabling the efficient and cost-effective analysis of medical images. Among different image processing tasks, medical segmentation is one of the most crucial aspects because it provides the class, location, size, and shape of the subject of interest, which is invaluable and essential for diagnostics. Nevertheless, acquiring annotations for training data usually requires expensive manpower and specialised expertise, making supervised training difficult. To overcome these problems, unsupervised domain adaptation (UDA) has been adopted to bridge knowledge between different domains. Despite the appearance dissimilarities of different modalities such as MRI and CT, researchers have concluded that structural features of the same anatomy are universal across modalities, which unfolded the study of multi-modality image segmentation with UDA methods. The traditional UDA research tackled the domain shift problem by minimising the distance of the source and target distributions in latent spaces with the help of advanced mathematics. However, with the recent development of the generative adversarial network (GAN), the adversarial UDA methods have shown outstanding performance by producing synthetic images to mitigate the domain gap in training a segmentation network for the target domain. Most existing studies focus on modifying the network architecture, but few investigate the generative adversarial training strategy. Inspired by the recent success of state-of-the-art data augmentation techniques in classification tasks, we designed a novel mix-up strategy to assist GAN training for the better synthesis of structural details, consequently leading to better segmentation results. In this thesis, we propose SynthMix, an add-on module with a natural yet effective training policy that can promote synthetic quality without altering the network architecture. SynthMix is a mix-up synthesis scheme designed for integration with the adversarial logic of GAN networks. Traditional GAN approaches judge an image as a whole which could be easily dominated by discriminative features, resulting in little improvement of delicate structures in synthesis. In contrast, SynthMix uses the data augmentation technique to reinforce detail transformation at local regions. Specifically, it coherently mixes up aligned images of real and synthetic samples at local regions to stimulate the generation of fine-grained features examined by an associated inspector for domain-specific details. We evaluated our method on two segmentation benchmarks among three publicly available datasets. Our method showed a significant performance gain compared with existing state-of-the-art approaches.

Islam, Mofakharul. "Unsupervised color image segmentation using Markov Random Fields Model". Thesis, University of Ballarat, 2008. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/53709.

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Abstract (sommario):

Islam, Mofakharul. "Unsupervised color image segmentation using Markov Random Fields Model". University of Ballarat, 2008. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15694.

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Abstract (sommario):

Zheng, Hongwei. "Bayesian learning and regularization for unsupervised image restoration and segmentation". [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1623.

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Libri sul tema "Unsupervised image segmentation":

Rajakumar, P. S., S. Geetha e T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the intricacies of digital imaging systems, pixel connectivity, color models, and file formats are dissected. Following this, image enhancement techniques, including spatial and frequency domain methods and histogram processing, are elaborated upon. The book then addresses image restoration, discussing degradation models, noise modeling, and blur, and offers insights into the compelling world of multi-resolution analysis with in-depth discussions on wavelets and image pyramids. Segmentation processes, especially edge operators, boundary detections, and thresholding techniques, are detailed in subsequent chapters. The text culminates by diving deep into the applications of image processing, exploring supervised and unsupervised learning, clustering algorithms, and various classifiers. Throughout the discourse, practical examples, real-world applications, and intuitive diagrams are integrated to facilitate an enriched learning experience. This book stands as an essential guide for both novices aiming to grasp the basics and experts looking to hone their knowledge in image processing. Keywords: Digital Imaging Systems, Image Enhancement, Image Restoration, Multi-resolution Analysis, Wavelets, Image Segmentation, Feature Extraction, SIFT, SURF, Image Classifiers, Supervised Learning, Clustering Algorithms.

Capitoli di libri sul tema "Unsupervised image segmentation":

Ouali, Yassine, Céline Hudelot e Myriam Tami. "Autoregressive Unsupervised Image Segmentation". In Computer Vision – ECCV 2020, 142–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58571-6_9.

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Bandyopadhyay, Sanghamitra, e Sriparna Saha. "A Validity Index Based on Symmetry: Application to Satellite Image Segmentation". In Unsupervised Classification, 125–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32451-2_6.

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Ojala, Timo, e Matti Pietikäinen. "Unsupervised texture segmentation using feature distributions". In Image Analysis and Processing, 311–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63507-6_216.

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Jodoin, Pierre-Marc, Jean-François St-Amour e Max Mignotte. "Unsupervised Markovian Segmentation on Graphics Hardware". In Pattern Recognition and Image Analysis, 444–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_50.

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Saha, Sudipan, Swathikiran Sudhakaran, Biplab Banerjee e Sumedh Pendurkar. "Semantic Guided Deep Unsupervised Image Segmentation". In Lecture Notes in Computer Science, 499–510. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30645-8_46.

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Zheng, Yuanjie, Jie Yang e Yue Zhou. "Unsupervised Image Segmentation with Fuzzy Connectedness". In PRICAI 2004: Trends in Artificial Intelligence, 961–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_114.

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Mouna, Zitouni, Zribi Mourad e Masmoudi Afif. "Unsupervised Image Segmentation Using THMRF Model". In Hybrid Intelligent Systems, 41–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_5.

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Zribi, M., e F. Ghorbel. "An unsupervised and non-parametric Bayesian Image segmentation". In Image Analysis and Processing, 423–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_292.

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Battistone, Francesco, Alfredo Petrosino e Gabriella Sanniti di Baja. "GRUNTS: Graph Representation for UNsupervised Temporal Segmentation". In Image Analysis and Processing — ICIAP 2015, 225–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_21.

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Han, Liang, e Zhaozheng Yin. "Unsupervised Network Learning for Cell Segmentation". In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 282–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87193-2_27.

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Atti di convegni sul tema "Unsupervised image segmentation":

Haindl, Michal, e Stanislav Mikes. "Unsupervised Image Segmentation Contest". In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.264.

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Gregoriou, G. K., e O. J. Tretiak. "Unsupervised textured image segmentation". In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.226273.

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Kanezaki, Asako. "Unsupervised Image Segmentation by Backpropagation". In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462533.

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Yang, Junhuan, Yi Sheng, Yuzhou Zhang, Weiwen Jiang e Lei Yang. "On-Device Unsupervised Image Segmentation". In 2023 60th ACM/IEEE Design Automation Conference (DAC). IEEE, 2023. http://dx.doi.org/10.1109/dac56929.2023.10247959.

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Alqahtani, Hussain, Naif Alqahtani, Ryan T. Armstrong e Peyman Mostaghimi. "Segmentation of X-Ray Images of Rocks Using Supervoxels Over-Segmentation". In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22131-ms.

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Abstract (sommario):

Abstract Digital core analysis has gained the interest of many scientific communities because of its impact on our understanding of flow in porous media. A typical workflow in digital core analysis includes scanning, reconstruction, denoising, segmentation, and modeling. Image analysis and modeling highly depend on the quality of the segmentation step. In this regard, conventional image segmentation methods often require user input/interference. This results in user bias and may produce a range of possible segmentation outcomes. To address this, we propose an unsupervised machine learning framework that offers multiple functionalities including improved mineral and micro-porosity identification. Superpixel (2D) and (3D) work by over-segmenting greyscale images using a family of over-segmentation algorithms. Simple Linear Iterative Clustering (SLIC) is one of these algorithms that is recognized for its speed and memory efficiency. The proposed framework utilizes SLIC and unsupervised clustering methods for segmenting greyscale images. SLIC divides the 2D and 3D images into segments having pixels (or voxels) with similar features (i.e., intensity range). Statistical features of each segment are computed and used for identifying the segment label through unsupervised clustering techniques. The unsupervised voting clustering implements a majority voting policy from multiple clustering algorithms including Hierarchical clustering and k-means clustering. A North Sea sandstone 2D X-ray image along with its SEM image were used to validate this framework. Different metrics were used to measure the accuracy of the X-ray segmentation with SEM segmentation. Our results show a mean Jaccard index of 70% and a mean Dice index of 81%. The same workflow is applied using supervoxels on a high-resolution 3D Indiana Limestone image and the results show similar accuracy margins compared to watershed segmentation. Comparison with other segmentation methods shows an average Jaccard score of 74% and an average Dice index score of 83%. To the best of our knowledge, this is the first application of superpixels over-segmentation algorithms in semantic segmentation of X-ray micro-CT images of porous media. The findings of this study highlighted the advantage of these algorithms in detecting sub-resolution porosity regions in greyscale images and obtaining accurate multi-label segmentation.

van Nierop, Wessel L., Jan-Niklas Schneider, Peter H. N. de With e Fons van der Sommen. "Unsupervised cell segmentation by invariant information clustering". In Image Processing, a cura di Olivier Colliot e Jhimli Mitra. SPIE, 2024. http://dx.doi.org/10.1117/12.3001442.

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Eliasof, Moshe, Nir Ben Zikri e Eran Treister. "Rethinking Unsupervised Neural Superpixel Segmentation". In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897484.

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Morales-González, Annette, Edel García-Reyes e Luis Enrique Sucar. "Unsupervised Segmentation Evaluation for Image Annotation". In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005314201480155.

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Gao, Li, Jie Xia, Junli Liang e Shuyuan Yang. "Improved Techniques for Unsupervised Image Segmentation". In 2006 International Conference on Communications, Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icccas.2006.284608.

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Wang, Fan, Qixing Huang, Maks Ovsjanikov e Leonidas J. Guibas. "Unsupervised Multi-class Joint Image Segmentation". In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.402.

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