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Multi-layered tree crown extraction from LiDAR data using graph-based segmentation

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 170卷 105213-105213页

作者： Dong, Tianyang Zhang, Xinpeng Ding, Zhanfeng Fan, Jing Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

With the development of Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) technology, extracting tree crowns from LiDAR data and infering their geometrical features are becoming more available for everyone. Although the forest has a significant stratification phenomenon in the vertical direction, the existing individual tree detection methods generally aimed at solving overstory trees detection. As a result, the understory trees cannot be effectively extracted. To effectively detect individual tree from multi-layer forests, a multi-layered tree extraction method using a graph-based segmentation algorithm was proposed in this study. First, using the graph-based segmentation algorithm delineates the canopy of the overstory tree on the canopy height model (CHM) generated by LiDAR data. Then, using the sliding window detection method extracts the LiDAR data of understory trees. Finally, the information of understory trees is extracted by the graph-based segmentation algorithm. To verify the performance of the proposed method, this study selected six experimental plots from two research areas. According to the result of our method, the highest matching score and average score for overstory trees reached 91.3 and 86.3;the highest matching score and average score for understory trees reached 78.1 and 63.2. Compared with other methods, our method has better detection results. The experimental results show that the proposed method can extract the understory and overstory trees effectively, thus improving the accuracy of individual tree extraction in multi-layered forests.

关键词： LiDAR Individual tree detection graph-based segmentation Tree structure

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graph-based segmentation of abnormal nuclei in cervical cytology

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COMPUTERIZED MEDICAL IMAGING AND graphICS 2017年 56卷 38-48页

作者： Zhang, Ling Kong, Hui Liu, Shaoxiong Wang, Tianfu Chen, Siping Sonka, Milan Shenzhen Univ Guangdong Key Lab Biomed Measurements & Ultrasoun Sch Med Dept Biomed Engn Shenzhen 518060 Peoples R China Univ Iowa Iowa Inst Biomed Imaging Iowa City IA 52242 USA Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Peoples Hosp Nanshan Dist Dept Pathol Shenzhen 518052 Peoples R China

A general method is reported for improving the segmentation of abnormal cell nuclei in cervical cytology images. In automation-assisted reading of cervical cytology, one of the essential steps is the segmentation of nuclei. Despite some progress, there is a need to improve the sensitivity, particularly the segmentation of abnormal nuclei. Our method starts with pre-segmenting the nucleus to define the coarse center and size of nucleus, which is used to construct a graph by image unfolding that maps ellipse-like border in the Cartesian coordinate system to lines in the polar coordinate system. The cost function jointly reflects properties of nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are utilized to modify the local cost functions. The globally optimal path in the constructed graph is then identified by dynamic programming with an iterative approach ensuring an optimal closed contour. Validation of our method was performed on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained manual liquid-based cytology (HEMLBC). Compared with five state-of-the-art approaches, our graph-search based method shows superior performance. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Image cytometry Cervical cells Abnormal nuclei segmentation graph-based segmentation

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Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年第Apr.期126卷 245-260页

作者： Alshehhi, Rasha Marpu, Prashanth Reddy Masdar Inst Sci & Technol Inst Ctr Smart & Sustainable Syst Abu Dhabi U Arab Emirates

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Road networks graph-based segmentation Gabor Morphological filtering Hierarchical merging and splitting

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A Hog & graph based Human segmentation from Video Sequences

A Hog & Graph Based Human Segmentation from Video Sequences

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Donmez, Emrah Kocamaz, Adnan Fatih Inonu Univ Dept Comp Engn Malatya Turkey

ISBN: (纸本)9781538668788

Human segmentation from video is a significant problem to recognize a specific person which is desired to find. There are a remarkable number of studies discussing segmentation process in videos. Almost all study is examined how well a human segmentation process could be created by considering their position, clothing and face. Unfortunately, it has been assumed that the background is static by approximately all of these works. Main challenges in this research area are;non-static background derived from dynamic structure of videos, human body and face position, shadows and clothing changing after a period of time in videos. In this script, we have proposed a multi-model (Histogram of the Gradients - HOG and graph-based) human segmentation technique that has worked with respect to HOG features and detected low-level and mid-level features of human clothing by supposing their positions in video. The offered technique is designed to demonstrate robustness against such challenges emphasized above. In this study, a well-known video series have been used. The video scenes can reach 15 similar to 25 fps and have the size about 640x480px. To compare graph-based method robustness a well-known segmentation method Watershed is also experimented and both methods are simply compared. Eventually, we determined that the proposed technique can produce satisfactory quality segmentation mentioned and detailed in following sections.

关键词： Human segmentation HOG Watershed graph-based segmentation

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graph-based Semantic segmentation for 3D Digital Images 31

Graph-Based Semantic Segmentation for 3D Digital Images

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31st IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Burdescu, Dumitru Dan Brezovan, Marius Stanescu, Liana Spahiu, Cosmin Stoica Ebanca, Daniel Costin Univ Craiova Fac Automat Computers & Elect Comp & Informat Technol Dept Craiova Romania

ISBN: (纸本)9781509062317

graph-based segmentation is gaining popularity among the many approaches in performing image segmentation, primarily due to its ability in reflecting global image properties. The most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. We developed a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The most important characteristic of using a virtual tree-hexagonal network over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

关键词： volumetric segmentation graph-based segmentation graph algorithms dissimilarity

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Effects of Color Spaces and Distance Norms on graph-based Image segmentation 3

Effects of Color Spaces and Distance Norms on Graph-Based Im...

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3rd International Conference on Frontiers of Signal Processing (ICFSP)

作者： Saglam, Ali Baykan, Nurdan Akhan Selcuk Univ Engn Fac Dept Comp Engn Konya Turkey

ISBN: (纸本)9781538610381

Use of the graph theory tools in image processing field is growing up with each passing day. graph theory makes the operations easier for image processing applications, and can represent digital image components completely. In image segmentation processes, the graph theory tools are also used widely. These kinds of image segmentation processes are called graph-based image segmentation. In many image processing applications, it seems as a problem that which color space the color values of pixels should be considered according to and which distance norm should be used to measure the difference between two points in the space. In this work, a graph-based image segmentation algorithm is tested on several color spaces with different distance norms. The test is carried out on 100 real world images that take part in a general-purposed image segmentation dataset. The average segmentation results are given as F-measure in this work with regard to both color spaces and distance norms. The results show that L*a*b* and L*u*v* color spaces are more appropriate than RGB color space, in general. The squared Euclidean distance norm gives more accurate results than the Euclidean distance norm, used in the source paper, if the Gaussian smoothing is not used as pre-processing.

关键词： color spaces digital image processing distance norms graph-based segmentation

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graph segmentation-based Pseudo-Labeling for Semi-Supervised Pathology Image Classification

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IEEE ACCESS 2022年 10卷 93960-93970页

作者： Shin, Hong-Kyu Uhmn, Kwang-Hyun Choi, Kyuyeon Xu, Zhixin Jung, Seung-Won Ko, Sung-Jea Korea Univ Dept Elect Engn Seoul 02841 South Korea

Pathology image classification is an important step in cancer diagnosis and precision treatment. Training a pathology image classification model in a fully supervised manner requires exhaustive pixel-level manual annotations from pathologists, which may not be practical in real applications. Semi-supervised learning (SSL) has been widely used to exploit large amounts of unlabeled data to facilitate model training with a small set of labeled data. However, due to the limited annotations, it still suffers from the issue of inaccurate pseudo-labels of unlabeled data. In this paper, we propose a novel framework for semi-supervised pathology image classification, which incorporates graph-based segmentation to refine initial pseudo-labels of tissue regions by considering local and global contextual relationships of patches in whole-slide images (WSIs). Moreover, we define a new energy function for graph construction that allows the graph to take into account the uncertainty of network predictions on unlabeled data. Extensive experiments on two different pathology image datasets demonstrate the effectiveness of our method compared with state-of-the-art SSL baselines. In particular, when using 5% labeled data, our approach outperforms a strong baseline by 2.81% AUC.

关键词： Pathology Training data Annotations Labeling Predictive models Image segmentation Data models Computational modeling Semi-supervised learning graph-based segmentation pathology pseudo-labeling semi-supervised learning

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Threshold of graph-based Volumetric segmentation 7

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7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

作者： Burdescu, Dumitru Dan Stanescu, Liana Brezovan, Marius Spahiu, Cosmin Stoica Slabu, Florin Univ Craiova Comp & Informat Technol Dept Fac Automat Comp & Elect Craiova 200440 Dolj Romania Univ Craiova Dept Comp Sci Craiova 200585 Dolj Romania

ISBN: (纸本)9783319193908

Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the spatial extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new and efficient method as complexity to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for original volumetric segmentation that uses a tree-hexagonal structure defined on the set of the voxels. The advantage of using a tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

关键词： Volumetric segmentation graph-based segmentation Dissimilarity Threshold

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A visual object segmentation algorithm with spatial and temporal coherence inspired by the architecture of the visual cortex

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COGNITIVE PROCESSING 2022年第1期23卷 27-40页

作者： Ramirez-Quintana, Juan A. Rangel-Gonzalez, Raul Chacon-Murguia, Mario, I Ramirez-Alonso, Graciela Tecnol Nacl Mexico IT Chihuahua Grad & Res Dept Av Tecnol 2909 Chihuahua 31310 Mexico Intel Tecnol Mexico Guadalajara Jalisco Mexico Univ Autonoma Chihuahua Fac Ingn Chihuahua Chihuahua Mexico

Scene analysis in video sequences is a complex task for a computer vision system. Several schemes have been addressed in this analysis, such as deep learning networks or traditional image processing methods. However, these methods require thorough training or manual adjustment of parameters to achieve accurate results. Therefore, it is necessary to develop novel methods to analyze the scenario information in video sequences. For this reason, this paper proposes a method for object segmentation in video sequences inspired by the structural layers of the visual cortex. The method is called Neuro-Inspired Object segmentation, SegNI. SegNI has a hierarchical architecture that analyzes object features such as edges, color, and motion to generate regions that represent the objects in the scenario. The results obtained with the Video segmentation Benchmark VSB100 dataset demonstrate that SegNI can adapt automatically to videos with scenarios that have different nature, composition, and different types of objects. Also, SegNI adapts its processing to new scenario conditions without training, which is a significant advantage over deep learning networks.

关键词： Video object segmentation Neuro-inspired algorithms Visual cortex graph-based segmentation

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graph-based RGB-D IMAGE segmentation USING COLOR-DIRECTIONAL-REGION MERGING 44

GRAPH-BASED RGB-D IMAGE SEGMENTATION USING COLOR-DIRECTIONAL...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Pan, Xiong Zhang, Zejun Liu, Yizhang Yang, Changcai Chen, Qiufeng Cheng, Li Lin, Jiaxiang Chen, Riqing Fujian Agr & Forestry Univ Coll Comp & Informat Sci Digital Fujian Inst Big Data Agr & Forestry Fuzhou Fujian Peoples R China Fujian Agr & Forestry Univ Jinshan Colledge Fuzhou Fujian Peoples R China

ISBN: (纸本)9781479981311

Color and depth information provided simultaneously in RGB-D images can be used to segment scenes into disjoint regions. In this paper, a graph-based segmentation method for RGB-D image is proposed, in which an adaptive data-driven combination of color- and normal-variation is presented to construct dissimilarity between two adjacent pixels and a novel region merging threshold exploiting normal information in adjacent regions is proposed to control the proceeding of the region merging. We evaluate our method on the NYU-v2 depth database and compare it with several published RGB-D partition methods. The experimental results show that our method is comparable with the state-of-the-art methods and provides more details of structures in the scene.

关键词： RGB-D image segmentation graph-based segmentation data-driven weight color-normal-region merging

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