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15th RIVF International Conference on Computing and Communication Technologies (RIVF)

作者： Xuan Huy Manh Hai Vu Xuan Dung Nguyen Linh Hoang Pham Tu Hang Dao Viet Phuc Binh Nguyen Manh Hung Nguyen Hanoi Univ Sci & Technol HUST Int Res Inst MICA Hanoi Vietnam HUST Sch Elect & Telecommun Hanoi Vietnam Inst Gastroenterol & Hepatol Hanoi Vietnam

ISBN: (纸本)9781665404358

Z-line is a junction between esophageal and gastric mucosa which is an important landmark in exploring esophageal diseases such as Gastroesophageal Reflux Diseases (GERD). This paper describes an effective interactive segmentation tool for Z-line annotation from Upper Gastrointestinal Endoscopy (UGIE) images. To this end, we propose a method containing of two main steps: firstly, a coarse scheme is designed to roughly segment boundary regions of Z-line. Thanks to recent advances of deep neural networks in biomedical imaging such as U-net segmentation, Z-line annotation is automatically achieved with acceptable results. However, the U-net's segmentation is not accurate enough due to gastric mucosa complexity. We then propose a fine-tuning scheme, which aims to prune the U-net's results. The proposed method is based on binary partition tree (BPT) algorithms, which BPT is built-in into a Graphic User Interface. Objective of the proposed framework is to help endoscopy doctors achieve the best segmentation results with lowest efforts of interactions via the GUI. The experiment was setup to evaluate effectiveness of the proposed method by comparing performances of four different segmentation schemes. They are manual segmentation by hand, fully automation by U-net, the interactive segmentation via BPT only, and the proposed scheme (U-net+BPT). The results confirmed that the proposed method converged faster to ideal regions than the other three. It took the lowest time costs and users' efforts but achieved the best accuracy. The proposed method also suggest a feasible solution for segmenting abnormal regions in UGIE images.

关键词： Z-line Gastro-Esophageal Reflux Disease (GERD) U-net binary partition tree

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binary partition tree construction from multiple features for image segmentation

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PATTERN RECOGNITION 2018年 84卷 237-250页

作者： Randrianasoa, Jimmy Francky Kurtz, Camille Desjardin, Eric Passat, Nicolas Univ Strasbourg CNRS ICube Strasbourg France Univ Reims CReSTIC Reims France Univ Paris 05 LIPADE Paris France

In the context of image analysis, the binary partition tree (BPT) is a classical data structure for the hierarchical modelling of images at different scales. BPTs belong both to the families of graph-based models and morphological hierarchies. They constitute an efficient way to define sets of nested partitions of image support, that further provide knowledge-guided reduced research spaces for optimization-based segmentation procedures. Basically, a BPT is built in a mono-feature way, i.e. for one given image, and one given metric, by merging pairs of connected image regions that are similar in the induced feature space. Our goal is to design a new family of BPTs, dealing with the need to directly manage multiple features within its building process, Then, we propose a generalization of the BPT construction framework, allowing one to embed multiple features. The cornerstone of our approach relies on a collaborative strategy used to establish a consensus between different metrics, thus enabling to obtain a unified hierarchical segmentation space. In particular, this provides alternatives to the complex issue of metric construction from several -possibly non-comparable- features. To reach that goal, we first revisit the BPT construction algorithm to describe it in a graph-based-formalism. Then, we present the structural and algorithmic evolutions and impacts when embedding multiple features in BPT construction. Final experiments illustrate how this multi-feature framework can be used to build BPTs from multiple metrics computed through the (potentially multiple) image content(s). (C) 2018 Elsevier Ltd. All rights reserved.

关键词： binary partition tree Morphological hierarchies Multiple features Graph-based image processing Image segmentation

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Region-based urban road extraction from VHR satellite images using binary partition tree

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 44卷 217-225页

作者： Li, Mengmeng Stein, Alfred Bijker, Wietske Zhan, Qingming Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands Wuhan Univ Sch Urban Design Wuhan 430072 Peoples R China Wuhan Univ Res Ctr Digital City Wuhan 430072 Peoples R China

This paper provides a hierarchical method for urban road extraction. It consists of (1) obtaining the road region of interest from a VHR image, (2) hierarchically representing this road region of interest in a binary partition tree (BPT), and extracting the roads based on this BPT at hierarchical levels. Besides using two existing geometrical features (i.e. compactness and elongation), we define two other structural features based on orientation histograms and morphological profiles to guide the region merging of BPT. The morphological profiles are constructed using a series of path openings, which facilitate modeling linear or curved structures. The proposed method was applied to two types of VHR images with different urban settings, corresponding to a Pleiades-B image of Wuhan, China, and a Quickbird image of Enschede, the Netherlands. Experimental results show that the proposed method was able to group adjacent small segments that have high spectral heterogeneity and low road-like geometrical properties to form more meaningful roads sections, and performed superior to the existing methods. Furthermore, we compared the proposed method with two other existing methods in the literature. We conclude that the proposed method can provide an effective means for extracting roads over densely populated urban areas from VHR satellite images. (C) 2015 Elsevier B.V. All rights reserved.

关键词： binary partition tree Fuzzy aggregation Morphological profiles Road extraction Very high resolution images

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Object recognition in hyperspectral images using binary partition tree representation

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PATTERN RECOGNITION LETTERS 2015年 56卷 45-51页

作者： Valero, Silvia Salembier, Philippe Chanussot, Jocelyn Univ Toulouse CESBIO CNES CNRS UMR 5126 IRD Toulouse France Tech Univ Catalonia UPC Barcelona Catalonia Spain Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland Grenoble Inst Technolo4 GIPSA Lab Signal & Image Dept Grenoble France

In this work, an image representation based on binary partition tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Object based image analysis binary partition tree Hyperspectral images Object recognition

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On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images

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REMOTE SENSING OF ENVIRONMENT 2015年 159卷 318-331页

作者： Tochon, G. Feret, J. B. Valero, S. Martin, R. E. Knapp, D. E. Salembier, P. Chanussot, J. Asner, G. P. GIPSA Lab F-38400 St Martin Dheres France Carnegie Inst Sci Dept Global Ecol Stanford CA 94305 USA CESBIO F-31400 Toulouse France Tech Univ Catalonia UPC Barcelona 08034 Spain Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland

The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications, such as forest inventories or conservation and management of ecosystems, for which species classification techniques and estimation of the number of individuals are highly valuable inputs. In this paper, we propose a method for hyperspectral image segmentation, based on the binary partition tree (BPT) algorithm, and we apply it to two sites located in Hawaiian and Panamean tropical rainforests. Different strategies combining spatial and spectral dimensionality reduction are compared prior to the construction of the BPT. Various superpixel generation methods including watershed transformation and mean shift clustering are applied to decrease spatial dimensionality and provide an initial segmentation map. Principal component analysis is performed to reduce the spectral dimensionality and different combinations of principal components are compared. A non-parametric region model based on histograms, combined with the diffusion distance to merge regions, is used to build the BPT. An adapted pruning strategy based on the size discontinuity of the merging regions is proposed and compared with an already existing pruning strategy. Finally, a set of criteria to assess the quality of the tree segmentation is introduced. The proposed method correctly segmented up to 68% of the tree crowns and produced reasonable patterns of the segmented landscapes. (C) 2015 Elsevier Inc. All rights reserved.

关键词： binary partition tree Carnegie Airborne Observatory Hyperspectral imagery Segmentation tree crown delineation Tropical forest

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Use of binary partition tree and energy minimization for object-based classification of urban land cover

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年第Apr.期102卷 48-61页

作者： Li, Mengmeng Bijker, Wietske Stein, Alfred Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a binary partition tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pleiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Urban land cover binary partition tree Energy minimization Fuzzy landscapes Directional relationship Segmentation

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Incremental watershed cuts: Interactive segmentation algorithm with parallel strategy

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PATTERN RECOGNITION LETTERS 2025年 189卷 256-263页

作者： Lebon, Quentin Lefevre, Josselin Cousty, Jean Perret, Benjamin Univ Gustave Eiffel LIGM CNRS F-77454 Marne La Vallee France Thermo Fisher Sci Bordeaux France

In this article, we design an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut. Additionally, we leverage properties of minimum spanning forests to introduce a parallel method for labeling a connected component. We show that those algorithms fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in linear time with respect to the number of affected pixels. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods with a significant speedup ranging from 10 to 60 on both 2D and 3D images, thus improving the user experience on large images.

关键词： Interactive segmentation Watershed binary partition tree Minimum spanning tree Parallel labeling

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Distributed Scatterer Processing Based on binary partition trees with Multi-Baseline PolInSAR Data

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REMOTE SENSING 2022年第21期14卷 5367-5367页

作者： Wang, Guanya Deng, Kailiang Chen, Qi Li, Zhiwei Gao, Han Hu, Jun Xiang, Deliang Cent South Univ Sch Geosci & Infophys Changsha 410083 Peoples R China Hunan Key Lab Remote Sensing Ecol Environm Dongti Changsha 410014 Peoples R China Naval Res Inst Tianjin 300061 Peoples R China China Ctr Resources Satellite Data & Applicat Beijing 100094 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Beijing Univ Chem Technol Beijing Adv Innovat Ctr Soft Matter Sci & Engn Interdisciplinary Res Ctr Artificial Intelligence Beijing 100029 Peoples R China

Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm.

关键词： distributed scatterer MT-InSAR PIHP binary partition tree multi-baseline PolInSAR

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Interactive Segmentation with Incremental Watershed Cuts 26th

Interactive Segmentation with Incremental Watershed Cuts

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26th Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Lebon, Quentin Lefevre, Josselin Cousty, Jean Perret, Benjamin Univ Gustave Eiffel CNRS LIGM F-77454 Marne La Vallee France Thermo Fisher Sci Bordeaux France

ISBN: (纸本)9783031490170;9783031490187

In this article, we propose an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut. We show that this algorithm fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in time linear with respect to the number of affected pixels. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods achieving significant speedup from 15 to 90, thus improving the user experience on large images.

关键词： Interactive segmentation watershed binary partition tree minimum spanning tree

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Platform for Temporal Analysis of binary partition tree

Platform for Temporal Analysis of Binary Partition Tree

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Conference on Signal Processing - Algorithms, Architectures, Arrangements, and Applications (SPA)

作者： Setyanto, Arief Wood, John Charles Ghanbari, Mohammed Univ Essex Colchester CO4 3SQ Essex England

ISBN: (纸本)9788362065172

Video contains rich information in both the spatial and temporal domains. A single video consists of a number of frames, which are composed of a set of regions. The regions may have changed in location, scale or orientation. A salient region is defined as any region which is highly distinct from its surroundings and therefore easily found in a binary partition tree (BPT). Salient regions which are consistently observed during the sequence can be classed as temporally salient. By rank ordering saliency and then rank ordering the ease with which regions are identified in the temporal domain, a powerful tool for higher level cognition results.

关键词： Spatial saliency temporal saliency binary partition tree region based video analysis temporal correlation

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