Soft image segmentation model
01 Dec 2012-pp 90-93
TL;DR: A soft segmentation model aiming to describe the uncertainties in the segmentation procedure was developed based on the multi-resolution segmentation, which is a bottom-up approach and follows a pair-wise object merging process.
Abstract: With the spatial resolution of remote sensing data increases, the information of spatial features become more and more important for interpretation of remote sensing images Therefore, object based image analysis methods receive more and more attentions Image segmentation is the most important step for object based methods Many segmentation algorithms have been developed, however, there are always uncertainties or errors in image segmentation Unfortunately, such uncertainty information was largely neglected in previous studies In this paper, a soft segmentation model aiming to describe the uncertainties in the segmentation procedure was developed based on the multi-resolution segmentation, which is a bottom-up approach and follows a pair-wise object merging process At each merging step, the soft segmentation model calculates probabilities of several adjacent sub-objects merged into super-objects on the next level By combining the probability at each merging step, the final probability of each pixel merged into the super-objects on the top level can be acquired A case study of an IKONOS image was conducted for validating the effectiveness of the proposed model The result shows that the soft image segmentation model is able to represent the reliability of the hard segmentation and the existence of mixed pixels on the borders of the adjacent objects The uncertainty information of segmentation may help to further understand the segmented result
Citations
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TL;DR: An extensive state-of-the-art survey on OBIA techniques is conducted, discussed different segmentation techniques and their applicability to OBIB, and selected optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects.
Abstract: Image segmentation is a critical and important step in (GEographic) Object-Based Image Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly dependent on the quality of image segmentation. Segmentation has been used in remote sensing image processing since the advent of the Landsat-1 satellite. However, after the launch of the high-resolution IKONOS satellite in 1999, the paradigm of image analysis moved from pixel-based to object-based. As a result, the purpose of segmentation has been changed from helping pixel labeling to object identification. Although several articles have reviewed segmentation algorithms, it is unclear if some segmentation algorithms are generally more suited for (GE)OBIA than others. This article has conducted an extensive state-of-the-art survey on OBIA techniques, discussed different segmentation techniques and their applicability to OBIA. Conceptual details of those techniques are explained along with the strengths and weaknesses. The available tools and software packages for segmentation are also summarized. The key challenge in image segmentation is to select optimal parameters and algorithms that can general image objects matching with the meaningful geographic objects. Recent research indicates an apparent movement towards the improvement of segmentation algorithms, aiming at more accurate, automated, and computationally efficient techniques.
325 citations
Cites methods from "Soft image segmentation model"
...Chen et al. (2012b) prescribed a soft image segmentation model based on multiresolution and probability of pixel merging at the top level....
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TL;DR: The experimental results show that the presented segmentation method for the images with complicated background, dim targets, and low-contrast grayscale between targets and background performs better and has better rationality and robustness to noise.
9 citations
03 Mar 2016
TL;DR: A method that clusters pixels into four regions based on their intensities using the process of thresholding with two local thresholds and one global threshold is put forward.
Abstract: In image processing, segmentation is the process of partitioning an image on the basis of intensity values of pixels, which makes it easier to analyze the image and distinguish objects and boundaries. The paper puts forward a method that clusters pixels into four regions based on their intensities. The method uses the process of thresholding with two local thresholds and one global threshold. The novelty of this process lies in the automated generation of three thresholds based on intrinsic characteristics of the image. The global threshold is obtained through neighborhood comparison of localized regions spanning through the image. The global threshold forms two sets of pixels, from which the local thresholds are obtained.
1 citations
01 Jan 2014
TL;DR: This research reports the development of Mobi Image Processing Suite (MIPS) which is an executable android application, that takes two images at run time and a single merged image is produced as a result by discarding the overlapping information.
Abstract: This research reports the development of Mobi Image Processing Suite (MIPS) which is an executable android application, that takes two images at run time and a single merged image is produced as a result by discarding the overlapping information. This application is especially designed to accommodate missing persons in a group photo. A complete group image can be constructed by merging two images captured back to back under same environmental and physical conditions and manipulating the missing human information. Enhancement techniques sharpening, converting into gray scale inverting, darkening the image has included. Face detection functionality is also integrated in the application. Besides entertainment photography, the application is highly accepted for real world engineering applications for inspection and fault analysis of dynamic complex machines where machine elements are partially hidden. For example a gear box and crankcase, a single image can be made by capturing two back to back images at rotation of 1800 for easy analysis. However, for better understanding of wide range audience,
References
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TL;DR: This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way.
Abstract: Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.
3,809 citations
01 Sep 1984-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: There are several image segmentation techniques, some considered general purpose and some designed for specific classes of images as discussed by the authors, some of which can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid link growing scheme, centroid region growing scheme and split-and-merge scheme.
Abstract: There are now a wide Abstract There are now a wide variety of image segmentation techniques, some considered general purpose and some designed for specific classes of images. These techniques can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid linkage region growing schemes, centroid linkage region growing schemes, spatial clustering schemes, and split-and-merge schemes. In this paper, we define each of the major classes of image segmentation techniques and describe several specific examples of each class of algorithm. We illustrate some of the techniques with examples of segmentations performed on real images.
2,009 citations
01 Jan 2000
TL;DR: In this paper, a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques is proposed for object oriented image processing, which aims for an universal high-quality solution applicable and adaptable to many problems and data types.
Abstract: A necessary prerequisite for object oriented image processing is successful image segmentation. The approach presented in this paper aims for an universal high-quality solution applicable and adaptable to many problems and data types. As each image analysis problem deals with structures of a certain spatial scale, the average image objects size must be free adaptable to the scale of interest. This is achieved by a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques. A scale parameter is used to control the average image object size. Different homogeneity criteria for image objects based on spectral and/or spatial information are developed and compared.
1,672 citations
01 Jan 2010
TL;DR: Different image segmentation techniques applied on optical remote sensing images are reviewed and conclusions are drawn summarizing commonly used techniques and their complexities in application.
Abstract: With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques applied on optical remote sensing images are reviewed. The selection of papers include sources from image processing journals, conferences, books, dissertations and thesis out of more than 3000 journals, books and online research databases available at UNB. The conceptual details of the techniques are explained and mathematical details are avoided for simplicity. Both broad and detailed categorisations of reviewed segmentation techniques are provided. The state of art research on each category is provided with emphasis on developed technologies and image properties used by them. The categories defined are not always mutually independent. Hence, their interrelationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application
224 citations
Additional excerpts
...Till now, many image segmentation algorithms have been developed [2] [3]....
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Journal Article•
01 Jan 2002-ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
TL;DR: The state-of-the-art of respective segmentation methods are summarized by describing the underlying concepts which are rather complex in the case of processing remotely sensed data, demonstrating various applications (automatical object recognition, signalbased fusion as support for visual interpretation, and estimation of the terrain surface from Digital Surface Models), and identifying yet existing problems and further research and development needs.
Abstract: Segmentation algorithms have already been recognized as a valuable and complementary approach that similar to human operators perform a region-based rather than a point-based evaluation of high-resolution and multi-source remotely sensed data. Goal of this paper is to summarize the state-of-the-art of respective segmentation methods by describing the underlying concepts which are rather complex in the case of processing remotely sensed data, demonstrating various applications (automatical object recognition, signalbased fusion as support for visual interpretation, and estimation of the terrain surface from Digital Surface Models), and identifying yet existing problems and further research and development needs.
165 citations