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Individual tree crown detection and delineation across a woodland using leaf-on and leaf-off imagery from a UAV consumer-grade camera

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JOURNAL OF APPLIED REMOTE SENSING 2020年第3期14卷

作者： Berra, Elias Fernando Newcastle Univ Geospatial Engn Grp Newcastle Upon Tyne Tyne & Wear England Univ Fed Rio Grande do Sul State Res Ctr Remote Sensing & Meteorol Porto Alegre RS Brazil

Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work is to investigate the potential and accuracy of UAV time-series data to automatically detect and delineate tree crowns across an entire woodland. The workflow (presented in a step-by-step manner) involves the construction of a canopy height model (CHM) based on digital elevation models derived from the UAV photogrammetric point clouds. A watershed-based approach is modified to automatically detect and delineate the tree crowns, based on the CHM and the brightness information from the UAV orthomosaics. The accuracy of the proposed method was evaluated by comparing its results against manually delineated tree crowns. The results show an overall accuracy of 63%, where conifer species were more accurately delineated (up to 80%), while broadleaf species returned lower accuracies (<50 % ). Continued research is necessary to improve the confidence of automated individual tree crown detection and delineation, especially over complex forest structures. (C) 2020 Society of Photo Optical Instrumentation Engineers (SPIE)

关键词： crown delineation drone forest inventory watershed algorithm phenology consumer off-the-shelf

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Adaptive image segmentation algorithm under the constraint of edge posterior probability

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IET COMPUTER VISION 2017年第8期11卷 702-709页

作者： Gong, Meiling Lan, Jinhui Yang, Changlin Wu, Hongtao Zhi, Tao Univ Sci & Technol Beijing Sch Automat & Elect Engn 30 Xueyuan Rd Beijing Peoples R China

Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti-noise performance, over-segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over-segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti-noise performance, highly precise segmentation result, and are useful in effectively segmenting low-contrast images.

关键词： image segmentation probability adaptive image segmentation algorithm image processing contemporary segmentation algorithm antinoise performance watershed algorithm edge threshold segmentation edge posterior probability model

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Robust region segmentation based on a pseudo-distance map

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JOURNAL OF ELECTRONIC IMAGING 2004年第3期13卷 602-612页

作者： Jeon, BK Jang, JH Hong, KS Postech Div Elect & Comp Engn Pohang 790784 South Korea

We present a robust region segmentation method based on a pseudo-distance map (PDM) that uses a watershed algorithm as a segmentation tool. The PDM is a regularized version of a Euclidean distance map (EDM) directly computed from the edge-strength function (ESF) of an input image without edge detection, which involves a thresholding operation. This unavoidably causes useful region boundary information loss from the original image. We show that applying the watershed algorithm to the PDM significantly reduces oversegmentation, and the final segmentation results obtained by a simple region-merging process are more accurate and meaningful and less sensitive to noise than those of the gradient-based or EDM-based methods. We also propose a simple and efficient region-merging criterion that considers both boundary strengths and inner intensities of regions to be merged. We tested and verified the robustness of our method with a variety of synthetic and real images. (C) 2004 SPIE and IST.

关键词： .original image Euclidean distance input image watershed algorithm pseudo-distance Robust region region segmentation method regularized version thresholding operation applying the watershed region-merging

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Tracking nonparameterized object contours in video

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2002年第9期11卷 1081-1091页

作者： Nguyen, HT Worring, M van den Boomgaard, R Smeulders, AWM Univ Amsterdam Fac Sci Intelligent Sensory Informat Syst Grp NL-1098 SJ Amsterdam Netherlands

We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.

关键词： contour tracking motion analysis watershed algorithm

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Automated detection of fluorescent cells in in-resin fluorescence sections for integrated light and electron microscopy

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JOURNAL OF MICROSCOPY 2018年第1期271卷 109-119页

作者： Delpiano, J. Pizarro, L. Peddie, C. J. Jones, M. L. Griffin, L. D. Collinson, L. M. Univ Los Andes Sch Engn & Appl Sci Santiago Chile UCL Dept Comp Sci London England Francis Crick Inst Electron Microscopy London England

Integrated array tomography combines fluorescence and electron imaging of ultrathin sections in one microscope, and enables accurate high-resolution correlation of fluorescent proteins to cell organelles and membranes. Large numbers of serial sections can be imaged sequentially to produce aligned volumes from both imaging modalities, thus producing enormous amounts of data that must be handled and processed using novel techniques. Here, we present a scheme for automated detection of fluorescent cells within thin resin sections, which could then be used to drive automated electron image acquisition from target regions via smart tracking'. The aim of this work is to aid in optimization of the data acquisition process through automation, freeing the operator to work on other tasks and speeding up the process, while reducing data rates by only acquiring images from regions of interest. This new method is shown to be robust against noise and able to deal with regions of low fluorescence.

关键词： Cell segmentation correlative light and electron microscopy image processing integrated light and electron microscopy in-resin fluorescence watershed algorithm

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Image-processing-based automatic crack detection and classification for refractory evaluation

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CERAMICS INTERNATIONAL 2022年第16期48卷 23232-23240页

作者： Wang, Guanzheng Tan, Fangguan Jin, Shengli Pan, Liping He, Zhu Wang, Xueqing Li, Yawei Li, Jing Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Natl Prov Joint Engn Res Ctr High Temp Mat & Linin Wuhan 430081 Hubei Peoples R China Montan Univ Leoben Chair Ceram A-8700 Leoben Austria Univ Sci & Technol Liaoning Sch Mat & Met Key Lab Chem Met Engn Liaoning Prov Anshan 114051 Liaoning Peoples R China Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Hubei Peoples R China

Crack generation and propagation in refractories is inevitable during their service life, which ultimately contributes to their failure. Accordingly, this study proposes a method that includes a watershed algorithm and classification criterion to quantitatively evaluate the fracture behavior in refractories. Additionally, to demonstrate the stability and reliability of the algorithm, its crack analysis results were compared with those of manual methods for alumina-magnesia ramming materials and corundum castables. The results indicated that the algorithm was capable of automatically identifying, extracting, and classifying cracks. Additionally, the type of crack propagation and its length and width were analyzed to determine the performance and properties of refractories. The proposed method was highly effective and accurate for the automated evaluation of material fracture behavior.

关键词： Refractory Classification criterion Crack statistics watershed algorithm Image processing

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An image segmentation algorithm for measurement of flotation froth bubble size distributions

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MEASUREMENT 2017年 111卷 29-37页

作者： Jahedsaravani, A. Massinaei, M. Marhaban, M. H. Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Upm Serdang 43400 Selangor Malaysia Univ Birjand Min Engn Dept POB 97175-376 Birjand Iran

The bubble size distribution at the froth surface of a flotation cell is closely related to the process condition and performance. The flotation performance can be reasonably predicted through continuous measuring the bubble size distribution by a machine vision system. In this work a new watershed algorithm based on whole and sub-image classification techniques is introduced and successfully validated by several laboratory and industrial scale froth images taken under different process conditions. The results indicate that the developed algorithms, in particular the sub-image classification based segmentation algorithm, can accurately and reliably identify the individual small and large bubbles in the actual froth images, which is often problematic. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Froth flotation Image classification Segmentation watershed algorithm

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Integration of synthetic aperture radar image segmentation method using Markov random field on region adjacency graph

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IET RADAR SONAR AND NAVIGATION 2007年第5期1卷 348-353页

作者： Xia, G.-S. He, C. Sun, H. Wuhan Univ Signal Proc Lab Elect Informat Sch Wuhan 430079 Hubei Peoples R China

A novel approach to obtain precise segmentation of synthetic aperture radar (SAR) images using Markov random field model on region adjacency graph (MRF-RAG) is presented. First, to form a RAG, the watershed algorithm is employed to obtain an initially over-segmented image. Then, a novel MRF is defined over the RAG instead of pixels so that the erroneous segmentation caused by speckle in SAR images can be avoided and the number of configurations for the combinatorial optimisation can be reduced. Finally, a modification method based on Gibbs sampler is proposed to correct edge errors, brought by the over-segmented algorithm, in the segmentations obtained by MRF-RAG. The experimental results both on simulated and real SAR images show that the proposed method can reduce the computational complexity greatly as well as increase the segmentation precision.

关键词： error correction combinatorial mathematics synthetic aperture radar image segmentation method radar imaging image segmentation over-segmented algorithm combinatorial optimisation Gibbs sampler Optical, image and video signal processing Combinatorial mathematics Radar equipment, systems and applications watershed algorithm edge error correction SAR images region adjacency graph synthetic aperture radar computational complexity Markov random field model Markov processes

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Segmentation for MRA image: An improved level-set approach

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2007年第4期56卷 1316-1321页

作者： Hao, Jiasheng Shen, Yi Wang, Qiang Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

Unsupervised segmentation of volumetric data is still a challenging task. Recently, level-set methods have received a great deal of attention, which combine global smoothness with the flexibility of topology changes and offer significant advantages over conventional statistical classification. However, level-set methods suffer from heavy computational burden because of a lot of iterations. We present a fast level-set framework based on the watershed algorithm for the segmentation of complicated structures from a volumetric data set. The driving application is the segmentation of 3-D human cerebrovascular structures from magnetic resonance angiography, which is known to be a very challenging segmentation problem due to the complexity of vessel geometry and intensity patterns. Experimental results show that the proposed method gives fast and accurate excellent segmentation.

关键词： biomedical measurements image segmentation level set magnetic resonance angiography (MRA) watershed algorithm

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Using a Method Based on a Modified K-Means Clustering and Mean Shift Segmentation to Reduce File Sizes and Detect Brain Tumors from Magnetic Resonance (MRI) Images

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WIRELESS PERSONAL COMMUNICATIONS 2016年第3期89卷 993-1008页

作者： Kim, JiHoon Lee, Sanghun Lee, GangSeong Park, YoungSoo Hong, YeongPyo Kwangwoon Univ Dept Plasmabiodisplay Seoul 139701 South Korea Kwangwoon Univ Dept Gen Educ 20 Kwangwoon Ro Seoul 139701 South Korea Int Univ Korea Dept Hosp Management Jinju 459721 South Korea

In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale image and remove noise by applying an erode operation. Finally, the brain tumors in the images are detected using a watershed algorithm. The proposed method was compared with two well-known methods namely the conventional K-mean clustering and Fuzzy C Means (FCM) clustering. We verified the precision and the objectivity of our proposed method. The average precision and recall for our proposed method were excellent with values of 0.914052 and 0.995641, respectively. Our method detected more brain tumors than the conventional K-means clustering and FCM clustering methods and was able to provide for an efficient image data processing with reduced file sizes.

关键词： IoT MRI K-means clustering Fuzzy C Means clustering Mean shift segmentation watershed algorithm

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