Journal articles: 'Unsupervised image segmentation'

1

Demir, Önder, and Buket Doğan. "Unsupervised Image Segmentation Using Textural Features." International Journal of Signal Processing Systems 5, no. 3 (September 2017): 112–15. http://dx.doi.org/10.18178/ijsps.5.3.112-115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

K., Sundeep Kumar, Jacob C. F., and Eswara Reddy B. "UNSUPERVISED WOUND IMAGE SEGMENTATION." ICTACT Journal on Image and Video Processing 04, no. 03 (February 1, 2014): 737–47. http://dx.doi.org/10.21917/ijivp.2014.0107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

4

Panić, Branislav, Marko Nagode, Jernej Klemenc, and Simon Oman. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks." Mathematics 10, no. 22 (November 16, 2022): 4301. http://dx.doi.org/10.3390/math10224301.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

5

Ikokou, Guy Blanchard, and Kate Miranda Malale. "Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications." Geomatics 4, no. 2 (May 12, 2024): 149–72. http://dx.doi.org/10.3390/geomatics4020009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

6

Yu, Sheng-yang, Yan Zhang, Yong-gang Wang, and Jie Yang. "Unsupervised color-texture image segmentation." Journal of Shanghai Jiaotong University (Science) 13, no. 1 (February 2008): 71–75. http://dx.doi.org/10.1007/s12204-008-0071-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Arasy, Muhammad Hariz, Suyanto Suyanto, and Kurniawan Nur Ramadhani. "Aerial Image Segmentation with Clustering Using Fireworks Algorithm." Indonesian Journal on Computing (Indo-JC) 4, no. 1 (March 22, 2019): 19. http://dx.doi.org/10.21108/indojc.2019.4.1.245.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

8

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

9

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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).

10

Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, and 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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

11

SHIRVAIKAR, MUKUL V., and MOHAN M. TRIVEDI. "TEXTURE SEGMENTATION: AN UNSUPERVISED APPROACH." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 04 (December 1995): 431–49. http://dx.doi.org/10.1142/s0218488595000220.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The segmentation of scenes into perceptually meaningful partitions has been a basic problem in image understanding, especially when unsupervised methodology has been desired. A novel unsupervised segmentation approach based on texture is developed. The texture model is based on sets of gray level cooccurence (GLC) matrices rather than measures extracted from them. The algorithmic constituents for the segmentation scheme: choice of seed regions, normalized match distances between texture models, region homogeneity, and aggregation criteria are systematically developed. The unsupervised algorithm works so that “seed” regions are discovered by an image search process. Initial estimates of the texture model prototypes are automatically computed for each “seed” region, and classification thresholds are based on the variance of the model over the “seed” region. An aggregation process then results in regions being successively classified and segmented “out” of the image. This recursive process of segmentation is continued until all pixels are classified. The segmentation strategy was tested successfully on natural texture mosaics. The results are analytically presented. These experiments demonstrate that the unsupervised process can correctly identify the perceptual constituents of the image based on texture.

12

CHEN Zhi-gang, 陈志刚, 陈爱华 CHEN Ai-hua, 崔跃利 CUI Yue-li, and 项美晶 XIANG Mei-jing. "Multi-scale Unsupervised Color Image Segmentation." ACTA PHOTONICA SINICA 40, no. 10 (2011): 1553–59. http://dx.doi.org/10.3788/gzxb20114010.1553.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Won, Chee Sun. "Block-based unsupervised natural image segmentation." Optical Engineering 39, no. 12 (December 1, 2000): 3146. http://dx.doi.org/10.1117/1.1321198.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Choy, Siu Kai, Tsz Ching Ng, and Carisa Yu. "Unsupervised fuzzy model-based image segmentation." Signal Processing 171 (June 2020): 107483. http://dx.doi.org/10.1016/j.sigpro.2020.107483.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Jeon, Byoung-Ki, Yun-Beom Jung, and Ki-Sang Hong. "Image segmentation by unsupervised sparse clustering." Pattern Recognition Letters 27, no. 14 (October 2006): 1650–64. http://dx.doi.org/10.1016/j.patrec.2006.03.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Ohkura, Keiko, Hidekazu Nishizawa, Takashi Obi, Akira Hasegawa, Masahiro Yamaguchi, and Nagaaki Ohyama. "Unsupervised Image Segmentation Using Hierarchical Clustering." Optical Review 7, no. 3 (May 2000): 193–98. http://dx.doi.org/10.1007/s10043-000-0193-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Meuleman, J., and C. van Kaam. "UNSUPERVISED IMAGE SEGMENTATION WITH NEURAL NETWORKS." Acta Horticulturae, no. 562 (November 2001): 101–8. http://dx.doi.org/10.17660/actahortic.2001.562.10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

O'Callaghan, R. J., and D. R. Bull. "Combined morphological-spectral unsupervised image segmentation." IEEE Transactions on Image Processing 14, no. 1 (January 2005): 49–62. http://dx.doi.org/10.1109/tip.2004.838695.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Cohen, Fernand S., and Zhigang Fan. "Maximum likelihood unsupervised textured image segmentation." CVGIP: Graphical Models and Image Processing 54, no. 3 (May 1992): 239–51. http://dx.doi.org/10.1016/1049-9652(92)90054-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Lin, Jian, Bo Peng, and Tianrui Li. "A Learning-Based Framework for Supervised and Unsupervised Image Segmentation Evaluation." International Journal of Image and Graphics 14, no. 03 (July 2014): 1450014. http://dx.doi.org/10.1142/s0219467814500144.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Image segmentation is a fundamental task in automatic image analysis. However, there is still no generally accepted effectiveness measure which is suitable for evaluating the segmentation quality in every application. In this paper, we propose an evaluation framework which benefits from multiple stand-alone measures. To this end, different segmentation evaluation measures are chosen to evaluate segmentation separately, and the results are effectively combined using machine learning methods. We train and implement this framework in our brand-new segmentation dataset which contains images of different contents with segmentation ground truth and Weizmann segmentation database (WSD). In addition, we provide human evaluation of image segmentation pairs to benchmark the evaluation results of the measures. Experimental results show a better performance than the stand-alone methods.

21

Zhou, Lei, and Weiyufeng Wei. "DIC: Deep Image Clustering for Unsupervised Image Segmentation." IEEE Access 8 (2020): 34481–91. http://dx.doi.org/10.1109/access.2020.2974496.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Liu, Xian, Fang Yang, and Wei Guo. "Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN." Applied Sciences 14, no. 11 (May 23, 2024): 4420. http://dx.doi.org/10.3390/app14114420.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Scene image text segmentation is an important task in computer vision, but the complexity and diversity of backgrounds make it challenging. All supervised image segmentation tasks require paired semantic label data to ensure the accuracy of segmentation, but semantic labels are often difficult to obtain. To solve this problem, we propose an unsupervised scene image text segmentation model based on the image style transfer model cyclic uniform Generation Adversarial network (CycleGAN), which is trained by partial unpaired label data. Text segmentation is achieved by converting a complex background to a simple background. Since the images generated by CycleGAN cannot retain the details of the text content, we also introduced the Atrous spatial Pyramid pool module (ASPP) to obtain the features of the text from multiple scales. The resulting image quality is improved. The proposed method is verified by experiments on a synthetic data set, the IIIT 5k word data set and the MACT data set, which effectively segments the text and preserves the details of the text content.

23

Wang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang, and Jinming Duan. "Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/614613.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

24

Choi, Hyun-Tae, and Byung-Woo Hong. "Unsupervised Object Segmentation Based on Bi-Partitioning Image Model Integrated with Classification." Electronics 10, no. 18 (September 18, 2021): 2296. http://dx.doi.org/10.3390/electronics10182296.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this paper, we propose a weakly supervised model for image segmentation based on the segmentation models (Mumford–Shah model and Chan–Vese model) and classification. The segmentation model (i.e., Mumford–Shah model or Chan–Vese model) is to find a base image mask for classification, and the classification network uses the mask from the segmentation models. With the classifcation network, the output mask of the segmentation model changes in the direction of increasing the performance of the classification network. In addition, the mask can distinguish the foreground and background of images naturally. Our experiment shows that our segmentation model, integrated with a classifier, can segment the input image to the foreground and the background only with the image’s class label, which is the image-level label.

25

Wang, Hong-Yuan, and Fuhua Chen. "Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/8508329.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter. In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced. Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed. The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation. The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions. The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model. The framework is then applied to MRI brain image segmentation. Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation. The new method can produce segmentation as precise as required.

26

Wang, Xi Jie, and Xiao Fan Zhao. "Texture Image Segmentation Based on MRMRF in Contourlet Domain." Advanced Materials Research 532-533 (June 2012): 732–37. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.732.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

27

Li, Lina, Zhi Liu, and Jian Zhang. "Unsupervised image co-segmentation via guidance of simple images." Neurocomputing 275 (January 2018): 1650–61. http://dx.doi.org/10.1016/j.neucom.2017.10.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Li, Yu, Xiang Juan Li, Ya Sen Zhang, Xian Sun, and Hong Qi Wang. "Multi-Scale Semantic Model for Unsupervised Object Segmentation." Advanced Materials Research 532-533 (June 2012): 859–64. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.859.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

It is difficult to segment instances of object classes accurately unsupervised in images, because of the complexity of structures, inter-class differences, background interference and so on. A multi-scale semantic model method is proposed to overcome the disadvantages existing in most of the relative methods. This method uses generative model to deal with the objects obtained by multi-scale segmentations instead of whole image, and calculates kinds of visual features to mine the topic information of every object. In the segmentation process, a semantic correlative function of every segment block based on KL divergence is built up and minimized to select the object correct regions. Experimental results demonstrate the effectiveness of the proposed method.

29

Chen, Ding-Jie, Jui-Ting Chien, Hwann-Tzong Chen, and Tyng-Luh Liu. "Unsupervised Meta-Learning of Figure-Ground Segmentation via Imitating Visual Effects." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8159–66. http://dx.doi.org/10.1609/aaai.v33i01.33018159.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This paper presents a “learning to learn” approach to figureground image segmentation. By exploring webly-abundant images of specific visual effects, our method can effectively learn the visual-effect internal representations in an unsupervised manner and uses this knowledge to differentiate the figure from the ground in an image. Specifically, we formulate the meta-learning process as a compositional image editing task that learns to imitate a certain visual effect and derive the corresponding internal representation. Such a generative process can help instantiate the underlying figure-ground notion and enables the system to accomplish the intended image segmentation. Whereas existing generative methods are mostly tailored to image synthesis or style transfer, our approach offers a flexible learning mechanism to model a general concept of figure-ground segmentation from unorganized images that have no explicit pixel-level annotations. We validate our approach via extensive experiments on six datasets to demonstrate that the proposed model can be end-to-end trained without ground-truth pixel labeling yet outperforms the existing methods of unsupervised segmentation tasks.

30

Andresen, Julia, Timo Kepp, Jan Ehrhardt, Claus von der Burchard, Johann Roider, and Heinz Handels. "Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies." International Journal of Computer Assisted Radiology and Surgery 17, no. 4 (March 3, 2022): 699–710. http://dx.doi.org/10.1007/s11548-022-02577-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Purpose The registration of medical images often suffers from missing correspondences due to inter-patient variations, pathologies and their progression leading to implausible deformations that cause misregistrations and might eliminate valuable information. Detecting non-corresponding regions simultaneously with the registration process helps generating better deformations and has been investigated thoroughly with classical iterative frameworks but rarely with deep learning-based methods. Methods We present the joint non-correspondence segmentation and image registration network (NCR-Net), a convolutional neural network (CNN) trained on a Mumford–Shah-like functional, transferring the classical approach to the field of deep learning. NCR-Net consists of one encoding and two decoding parts allowing the network to simultaneously generate diffeomorphic deformations and segment non-correspondences. The loss function is composed of a masked image distance measure and regularization of deformation field and segmentation output. Additionally, anatomical labels are used for weak supervision of the registration task. No manual segmentations of non-correspondences are required. Results The proposed network is evaluated on the publicly available LPBA40 dataset with artificially added stroke lesions and a longitudinal optical coherence tomography (OCT) dataset of patients with age-related macular degeneration. The LPBA40 data are used to quantitatively assess the segmentation performance of the network, and it is shown qualitatively that NCR-Net can be used for the unsupervised segmentation of pathologies in OCT images. Furthermore, NCR-Net is compared to a registration-only network and state-of-the-art registration algorithms showing that NCR-Net achieves competitive performance and superior robustness to non-correspondences. Conclusion NCR-Net, a CNN for simultaneous image registration and unsupervised non-correspondence segmentation, is presented. Experimental results show the network’s ability to segment non-correspondence regions in an unsupervised manner and its robust registration performance even in the presence of large pathologies.

31

E. A. Armya Armya, Revella, and Adnan Mohsin Abdulazeez. "Medical Images Segmentation Based on Unsupervised Algorithms: A Review." Qubahan Academic Journal 1, no. 2 (April 28, 2021): 71–80. http://dx.doi.org/10.48161/qaj.v1n2a51.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

32

Khan, Khan Bahadar, Muhammad Shahbaz Siddique, Muhammad Ahmad, and Manuel Mazzara. "A Hybrid Unsupervised Approach for Retinal Vessel Segmentation." BioMed Research International 2020 (December 10, 2020): 1–20. http://dx.doi.org/10.1155/2020/8365783.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Retinal vessel segmentation (RVS) is a significant source of useful information for monitoring, identification, initial medication, and surgical development of ophthalmic disorders. Most common disorders, i.e., stroke, diabetic retinopathy (DR), and cardiac diseases, often change the normal structure of the retinal vascular network. A lot of research has been committed to building an automatic RVS system. But, it is still an open issue. In this article, a framework is recommended for RVS with fast execution and competing outcomes. An initial binary image is obtained by the application of the MISODATA on the preprocessed image. For vessel structure enhancement, B-COSFIRE filters are utilized along with thresholding to obtain another binary image. These two binary images are combined by logical AND-type operation. Then, it is fused with the enhanced image of B-COSFIRE filters followed by thresholding to obtain the vessel location map (VLM). The methodology is verified on four different datasets: DRIVE, STARE, HRF, and CHASE_DB1, which are publicly accessible for benchmarking and validation. The obtained results are compared with the existing competing methods.

33

Lisenko, O. M., and A. YU Varfolomєєv. "Comparative analysis of modern automated algorithms image segmentation." Electronics and Communications 16, no. 5 (May 29, 2012): 37–47. http://dx.doi.org/10.20535/2312-1807.2011.16.5.247555.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Unsupervised image segmentation algorithms based on-mean clustering, expectation-maximization, mean-shift, normalized graph cut, weighted aggregation, statistical region merging, JSEG, HGS and ROI-SEG are considered. The results of segmentation obtained by mentioned algorithms on textural, satellite and natural images are presented. The analysis of quality and segmentation speed of each algorithm realization is performed

34

YAKUT, Cem, and Sezer ULUKAYA. "PARAMETER OPTIMIZATION FOR UNSUPERVISED RETINAL VESSEL SEGMENTATION WITH IMAGE FILTERING." Mühendislik Bilimleri ve Tasarım Dergisi 10, no. 3 (September 30, 2022): 844–55. http://dx.doi.org/10.21923/jesd.1033339.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

For the detection and evaluation of eye disorders, retinal pictures are obtained in a digital environment with a customized camera system called the fundus. Due to various noises and unsharp contrast, it is difficult to detect the vessels in the eye by specialists, and this can make it difficult for specialists to diagnose. In this study, unsupervised image processing-based mathematical morphology and Coye filtering, and connected component analysis approaches were used to increase the success of retinal vascular segmentation from fundus images. In addition, retinal images are preprocessed for noise reduction and increased contrast. Parameter optimization was performed to increase the success of unsupervised image processing-based approaches. In the contrast limited adaptive histogram equalization (CLAHE) method, which is frequently used in image processing, the most appropriate upper limit value for contrast on color retinal images was investigated. The presented approach tested on the DRIVE and STARE datasets available to researchers. Compared to previous unsupervised learning studies, some metrics were at par with the literature and some metrics were more successful.

35

CHEN, Fan, Takafumi AOKI, and Tsuyoshi HORIGUCHI. "Unsupervised Image Segmentation Based on Bethe Approximation." Interdisciplinary Information Sciences 11, no. 2 (2005): 127–39. http://dx.doi.org/10.4036/iis.2005.127.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Haindl, Michal, and Stanislav Mikeš. "A competition in unsupervised color image segmentation." Pattern Recognition 57 (September 2016): 136–51. http://dx.doi.org/10.1016/j.patcog.2016.03.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Zribi, Mourad. "Unsupervised Bayesian image segmentation using orthogonal series." Journal of Visual Communication and Image Representation 18, no. 6 (December 2007): 496–503. http://dx.doi.org/10.1016/j.jvcir.2007.05.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Liu, Juncheng, Paul L. Rosin, Xianfang Sun, Jianguo Xiao, and Zhouhui Lian. "Image-driven unsupervised 3D model co-segmentation." Visual Computer 35, no. 6-8 (May 7, 2019): 909–20. http://dx.doi.org/10.1007/s00371-019-01679-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Benboudjema, Dalila, and Wojciech Pieczynski. "Unsupervised image segmentation using triplet Markov fields." Computer Vision and Image Understanding 99, no. 3 (September 2005): 476–98. http://dx.doi.org/10.1016/j.cviu.2005.04.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Al-Nuaimy, W., Y. Huang, A. Eriksen, and V. T. Nguyen. "Automatic feature selection for unsupervised image segmentation." Applied Physics Letters 77, no. 8 (August 21, 2000): 1230–32. http://dx.doi.org/10.1063/1.1289267.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Caillol, H., A. Hillion, and W. Pieczynski. "Fuzzy random fields and unsupervised image segmentation." IEEE Transactions on Geoscience and Remote Sensing 31, no. 4 (July 1993): 801–10. http://dx.doi.org/10.1109/36.239902.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Wang, Jiajing, Shuhong Jiao, Lianyang Shen, Zhenyu Sun, and Lin Tang. "Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/354704.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Unsupervised synthetic aperture radar (SAR) image segmentation is a fundamental preliminary processing step required for sea area detection in military applications. The purpose of this step is to classify large image areas into different segments to assist with identification of the sea area and the ship target within the image. The recently proposed triplet Markov field (TMF) model has been successfully used for segmentation of nonstationary SAR images. This letter presents a hierarchical TMF model in the discrete wavelet domain of unsupervised SAR image segmentation for sea area detection, which we have named the wavelet hierarchical TMF (WHTMF) model. The WHTMF model can precisely capture the global and local image characteristics in the two-pass computation of posterior distribution. The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (X,U). To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. The effectiveness of the proposed model for SAR image segmentation is evaluated using synthesized and real SAR data.

43

Zhao, Dong, Baoqing Ding, Yulin Wu, Lei Chen, and Hongchao Zhou. "Unsupervised Learning from Videos for Object Discovery in Single Images." Symmetry 13, no. 1 (December 29, 2020): 38. http://dx.doi.org/10.3390/sym13010038.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This paper proposes a method for discovering the primary objects in single images by learning from videos in a purely unsupervised manner—the learning process is based on videos, but the generated network is able to discover objects from a single input image. The rough idea is that an image typically consists of multiple object instances (like the foreground and background) that have spatial transformations across video frames and they can be sparsely represented. By exploring the sparsity representation of a video with a neural network, one may learn the features of each object instance without any labels, which can be used to discover, recognize, or distinguish object instances from a single image. In this paper, we consider a relatively simple scenario, where each image roughly consists of a foreground and a background. Our proposed method is based on encoder-decoder structures to sparsely represent the foreground, background, and segmentation mask, which further reconstruct the original images. We apply the feed-forward network trained from videos for object discovery in single images, which is different from the previous co-segmentation methods that require videos or collections of images as the input for inference. The experimental results on various object segmentation benchmarks demonstrate that the proposed method extracts primary objects accurately and robustly, which suggests that unsupervised image learning tasks can benefit from the sparsity of images and the inter-frame structure of videos.

44

Dai, Songmin, Xiaoqiang Li, Lu Wang, Pin Wu, Weiqin Tong, and Yimin Chen. "Learning Segmentation Masks with the Independence Prior." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3429–36. http://dx.doi.org/10.1609/aaai.v33i01.33013429.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances’ poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.

45

Song, Yinglei, Benjamin Adobah, Junfeng Qu, and Chunmei Liu. "Segmentation of Ordinary Images and Medical Images with an Adaptive Hidden Markov Model and Viterbi Algorithm." Current Signal Transduction Therapy 15, no. 2 (December 1, 2020): 109–23. http://dx.doi.org/10.2174/1574362413666181109113834.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Background: Image segmentation is an important problem in both image processing and computer vision. Given an image, the goal of image segmentation is to label each pixel in the image such that the pixels with the same label collectively represent an object. Materials and Methods: Due to the inherent complexity and noise that may exist in images, developing an algorithm that can generate excellent segmentation results for an arbitrary image is still a challenging problem. In this paper, a new adaptive Hidden Markov Model is developed to describe the spatial and semantic relationships among pixels in an image. Based on this statistical model, image segmentation can be efficiently performed with an adaptive Viterbi algorithm in linear time. Results: The algorithm is unsupervised and does not require being used along with any other approach in image segmentation. Testing results on synthetic and real images show that this algorithm is able to achieve excellent segmentation results in both ordinary images and medical images. Conclusion: An implementation of this algorithm in MATLAB is freely available upon request.

46

Ju, Yan Wei, and Yan Zhang. "SVMMAP Modeling of SAR Imagery for Unsupervised Segmentation with Bootstrap Sampling." Applied Mechanics and Materials 614 (September 2014): 393–96. http://dx.doi.org/10.4028/www.scientific.net/amm.614.393.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

A spatially variant mixture multiscale autoregressive prediction (SVMMAP) model is present, which was applied to segmentation of SAR imagery. General process is as follow: at first, by Bootstrap sampling technique a small representative set of pixels is selected; then, expectation maximization (EM) algorithm and least square (LS) estimation were used to estimate the model, and minimum description length (MDL) rule was employed to choose classification number; at last, Bayes classifier was used to segment image. For a simulated image of size 256×256, a segmentation accuracy of 99.76% was achieved. Besides, quantitative assessment was also presented about segmentation quality of images.

47

Panić, Branislav, Marko Nagode, Jernej Klemenc, and Simon Oman. "Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering." Mathematics 11, no. 23 (November 28, 2023): 4800. http://dx.doi.org/10.3390/math11234800.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive.

48

Droby, Ahmad, Berat Kurar Barakat, Raid Saabni, Reem Alaasam, Boraq Madi, and Jihad El-Sana. "Understanding Unsupervised Deep Learning for Text Line Segmentation." Applied Sciences 12, no. 19 (September 22, 2022): 9528. http://dx.doi.org/10.3390/app12199528.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

We propose an unsupervised feature learning approach for segmenting text lines of handwritten document images with no labelling effort. Humans can easily group local text line features to global coarse patterns. We leverage this coherent visual perception of text lines as a supervising signal by formulating the feature learning as a global pattern differentiation task. The machine is trained to detect whether a document patch contains a similar global text line pattern with its identity or neighbours, and a different global text line pattern with its 90-degree-rotated identity or neighbours. Clustering the central windows of document image patches using their extracted features, forms blob lines which strike through the text lines. The blob lines guide an energy minimization function for extracting text lines in a binary image and guide a seam carving function for detecting baselines in a colour image. In identifying the aspect of the input patch that supports the actual prediction and clustering, we contribute toward the understanding of input patch functionality. We evaluate the method on several variants of text line segmentation datasets to demonstrate its effectiveness, visualize what it has learned, and enable it to comprehend its clustering strategy from a human perspective.

49

Gómez, Jose L., Gabriel Villalonga, and Antonio M. López. "Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models." Sensors 23, no. 2 (January 5, 2023): 621. http://dx.doi.org/10.3390/s23020621.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies addressing an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It performs iterations where the (unlabeled) real-world training images are labeled by intermediate deep models trained with both the (labeled) synthetic images and the real-world ones labeled in previous iterations. More specifically, a self-training stage provides two domain-adapted models and a model collaboration loop allows the mutual improvement of these two models. The final semantic segmentation labels (pseudo-labels) for the real-world images are provided by these two models. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for onboard semantic segmentation. Our procedure shows improvements ranging from approximately 13 to 31 mIoU points over baselines.

50

Wang, Yi, and Lihong Xu. "Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation." PeerJ 6 (June 28, 2018): e5036. http://dx.doi.org/10.7717/peerj.5036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Agricultural greenhouse plant images with complicated scenes are difficult to precisely manually label. The appearance of leaf disease spots and mosses increases the difficulty in plant segmentation. Considering these problems, this paper proposed a statistical image segmentation algorithm MSBS-LDA (Mean-shift Bandwidths Searching Latent Dirichlet Allocation), which can perform unsupervised segmentation of greenhouse plants. The main idea of the algorithm is to take advantage of the language model LDA (Latent Dirichlet Allocation) to deal with image segmentation based on the design of spatial documents. The maximum points of probability density function in image space are mapped as documents and Mean-shift is utilized to fulfill the word-document assignment. The proportion of the first major word in word frequency statistics determines the coordinate space bandwidth, and the spatial LDA segmentation procedure iteratively searches for optimal color space bandwidth in the light of the LUV distances between classes. In view of the fruits in plant segmentation result and the ever-changing illumination condition in greenhouses, an improved leaf segmentation method based on watershed is proposed to further segment the leaves. Experiment results show that the proposed methods can segment greenhouse plants and leaves in an unsupervised way and obtain a high segmentation accuracy together with an effective extraction of the fruit part.

Journal articles on the topic 'Unsupervised image segmentation'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles