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12th IEEE International New Circuits and Systems Conference (IEEE NEWCAS)

作者： Fujita, Tomohiro Sawada, Syo Iwanaga, Kaihei Kumaki, Takeshi Nakanishi, Mamoru Ogura, Takeshi Ritsumeikan Univ Dept Elect & Comp Engn Kusatsu Shiga 5258577 Japan Microsyst Integrat Labs NTT Atsugi Kanagawa 2430198 Japan

ISBN: (纸本)9781479948857

A new version of Pixel Level Snake (PLS), which is an image segmentation algorithm, is proposed. The proposed algorithm is based on processes of Cellular Automaton (CA). In CA, the forces of resistance to stretch and bending are defined by the CA rule, and comparing these forces and the gradients of image pixels determines deforming of the active contour. The CA based PLS consists of simple transition rules, and therefore easy implementation and high speed processing are expected on our dedicated hardware engine. In our experiments, we confirmed the feasibility of the image segmentation with the CA based PLS.

关键词： Active contours Automata Computational modeling Computer architecture Force Hardware Microprocessors Active contours macroprocessor Computer Architecture Automata Computer hardware Computational modeling Force Cellular automata calcium image segmentation algorithm image segmentation Snakes PROCESSING SPEED

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Structured Forests for Fast Edge Detection

Structured Forests for Fast Edge Detection

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IEEE International Conference on Computer Vision (ICCV)

作者： Dollar, Piotr Zitnick, C. Lawrence Microsoft Res Philadelphia PA 19104 USA

ISBN: (纸本)9781479928392

Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.

关键词： edge detection realtime vision structure learning Edge detection Dataset image segmentation algorithm t tube computer vision Decision Trees

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GrabCut in One Cut

GrabCut in One Cut

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IEEE International Conference on Computer Vision (ICCV)

作者： Tang, Meng Gorelick, Lena Veksler, Olga Boykov, Yuri Univ Western Ontario London ON N6A 3K7 Canada

ISBN: (纸本)9781479928392

Among image segmentation algorithms there are two major groups: (a) methods assuming known appearance models and (b) methods estimating appearance models jointly with segmentation. Typically, the first group optimizes appearance log-likelihoods in combination with some spacial regularization. This problem is relatively simple and many methods guarantee globally optimal results. The second group treats model parameters as additional variables transforming simple segmentation energies into high-order NP-hard functionals (Zhu-Yuille, Chan-Vese, GrabCut, etc). It is known that such methods indirectly minimize the appearance overlap between the segments. We propose a new energy term explicitly measuring L-1 distance between the object and background appearance models that can be globally maximized in one graph cut. We show that in many applications our simple term makes NP-hard segmentation functionals unnecessary. Our one cut algorithm effectively replaces approximate iterative optimization techniques based on block coordinate descent.

关键词： MRF color separation graph cut segmentation Colour separations Cutting MYRF gene image segmentation algorithm Nondeterministic polynomial-time hard FUNCTIONALS

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Video Object segmentation with Occlusion Map

Video Object Segmentation with Occlusion Map

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International Conference on Digital image Computing Techniques and Applications (DICTA)

作者： Xiong, Hao Wang, Zhiyong He, Renjie Feng, David Dagan Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

ISBN: (纸本)9781467321808;9781467321792

Extracting foreground objects from a video captured by a hand-held camera has been a new challenge in video segmentation, since most of the existing approaches generally work well provided that certain assumptions on the background scene or camera motion (e. g. still surveillance cameras) are imposed. While some approaches exploit several clues such as depth and motion to extract the foreground layer from handheld camera videos, we propose to leverage the advances in high quality interactive image segmentation. That is, we treat each video frame as an individual image and segment foreground objects with interactive image segmentation algorithms. In order to simulate user interactions, we derive reliable occlusion map for foreground objects and use the occlusion map as the "seeding" interactive input to an interactive image segmentation approach. In this paper, we employ an optical flow based occlusion detection approach for extracting the occlusion map and Geodesic star convexity based interactive image segmentation approach. In order to obtain accurate "seeding" user interactions, both forward and backward occlusion maps are computed and utilized. As a result, our approach is able to extract the whole objects having only partial movements, which overcomes the limitation of the state-of-the-art algorithm. Experimental results demonstrate both the effectiveness and efficiency of our proposed approach.

关键词： image segmentation Occlusion VIDEO FRAMES maps image segmentation algorithm Videotapes MAPPING(MATHEMATICAL) Interactive Detective cameras Object segmentation Video object Cameras Optic Flow

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Depth-Adaptive Superpixels

Depth-Adaptive Superpixels

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21st International Conference on Pattern Recognition (ICPR)

作者： Weikersdorfer, David Gossow, David Beetz, Michael Tech Univ Munich Intelligent Autonomous Syst Grp D-80290 Munich Germany

ISBN: (纸本)9784990644109;9781467322164

We propose a novel oversegmentation technique for RGB-D images. The visible surface of the 3D geometry is partitioned into uniformly distributed and equally sized planar patches. This results in a classic oversegmentation of pixels into depth-adaptive superpixels which correctly reflect deformation through perspective projection. The advantages of depth-adaptive superpixels (DASP) are demonstrated by using spectral graph theory to create image segmentations in near realtime. Our algorithms outperform state-of-the-art oversegmentation and image segmentation algorithms both in quality and runtime.

关键词： image segmentation Graph theory image segmentation algorithm Runtime pixel Planar quasi-realtime language deformation

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In situ capture of spatter signature of SLM process using maximum entropy double threshold image processing method based on genetic algorithm

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OPTICS AND LASER TECHNOLOGY 2020年 131卷 106371-106371页

作者： Yang, Dekun Li, Hui Liu, Sheng Song, Changhui Yang, Yongqiang Shen, Shengnan Lu, Junwen Liu, Zefeng Zhu, Yilin Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China Wuhan Univ Minist Educ Key Lab Hydraul Machinery Transients Wuhan 430072 Peoples R China Wuhan Univ Inst Technol Sci Wuhan 430072 Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China Civil Aviat Flight Univ China Guanghan 618307 Sichuan Peoples R China

Although selective laser melting (SLM) has the advancements of fabricating complex geometric, multi-material and multi-functional structures, several defects still affect the process stability, in particular spatter. The formation of spatter in SLM depends on the process parameters, and these can potentially be used to tune the process to obtain better product quality. However, there is still a lack of efficient methods for processing spatter images to allow in situ detection of the onset of spatter. In this paper, an in situ monitoring method for acquiring spatter images in SLM was presented. A maximum-entropy double-threshold image processing algorithm based on genetic algorithm (MEDTIA-GA) was proposed to recognize spatter from images, and its results were compared with three conventional threshold segmentation methods: Otsu's method, Triangle threshold segmentation algorithm, and K-means clustering algorithm. Results show that MEDTIA-GA method was able to eliminate three types of errors: noise sensitivity, spatter conglutination, and spatter omission. In addition, the average processing time of 37 ms for MEDTIA-GA method was far shorter than those for other three conventional threshold segmentation methods. Finally, the relationship between the spatter area as well as the number and the laser energy density were analyzed

关键词： Selective Laser Melting Spatter In situ monitoring image segmentation algorithm

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Adaptive Weighted Morphology Detection algorithm of Plane Object in Docking Guidance System

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2010年第2期7卷 99-104页

作者： Guo yan-ying Yang guo-qing Jiang li-hui Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Civil Aviat Univ China Tianjin Key Lab Adv Signal Proc Tianjin Peoples R China

In this paper, we presented an image segmentation algorithm based on adaptive weighted mathematical morphology edge detectors. The performance of the proposed algorithm has been demonstrated on the Lena image. The input of the proposed algorithm is a grey level image. The image was first processed by the mathematical morphological closing and dilation residue edge detector to enhance the edge features and sketch out the contour of the image, respectively. Then the adaptive weight SE operation was applied to the edge-extracted image to fuse edge gaps and hill up holds. Experimental results show it can not only primely extract detail edge, but also superbly preserve integer effect comparative to classical edge detection algorithm.

关键词： Edge detection image segmentation algorithm Guidance systems Berthing hills Self tuning boundary detection algorithm image Detection algorithms Edge of a figure or solid

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Automation of green roof plant cover measurements using machine learning and a comparison of digital and thermal imaging techniques

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APPLIED VEGETATION SCIENCE 2024年第2期27卷

作者： Cao, Ronglan Maclvor, J. Scott Univ Toronto Scarborough Dept Biol Sci 1265 Mil Trail Toronto ON M1C 1A4 Canada

Aims: Post-analyses of digital red, green, blue (RGB) and thermal images have become increasingly popular as modern approaches to plant cover analysis. image analyses are often coupled with semi-automated or automated workflows to reduce the amount of human labor input compared with traditional manual procedures. This study aims to evaluate and compare different image segmentation methods for plant cover analysis using digital RGB and thermal images, focusing on the effectiveness of semi-automated and manual segmentation techniques in monitoring plant cover on green roofs. Location: An Extensive green roof in the City of Toronto. Methods: We surveyed the plant cover of an extensive green roof using digital and thermal imagery. The plant cover values were obtained using three methods: traditional manual segmentation based on a visual examination (MS), imageJ Color Threshold (CT) and Trainable Weka segmentation (TWS), all performed within FIJI (a distribution of imageJ). Manual segmentation based on visual examination was used as a reference standard. Results: Significant correlation was found between the cover estimation using the CT and TWS methods relative to MS, and between cover estimation using the thermal image and the RGB image. TWS overestimated plant cover on thermal images while producing an underestimation on RGB images. CT demonstrated a performance closer to MS than TWS, indicating that manually customized methods produced results more aligned with MS. The estimated cover values by MS were not significantly affected by the image type (digital RGB or thermal). Conclusions: Results suggest that RGB and thermal imaging techniques may provide complementary results and reveal unique information regarding the functioning of green roofs. The accuracy of supervised machine-learning methods could be enhanced with site-specific data to provide a more accurate and efficient estimation of plant cover, which might be beneficial for long-term studies on green roofs an

关键词： green infrastructure image segmentation algorithm imageJ microclimate cooling plant cover Sedum thermal imaging urban agriculture

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1099 Fully automated segmentation of the left and right ventricles: validation with a large clinical trial

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Journal of Cardiovascular Magnetic Resonance 2008年第1期10卷 1-2页

作者： Xiang Lin Brett Cowan Alistair Young University of Auckland Auckland New Zealand

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A NEW algorithm FOR WATER INFORMATION EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING imageRY

A NEW ALGORITHM FOR WATER INFORMATION EXTRACTION FROM HIGH R...

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IEEE International Conference on image Processing

作者： Shijin LI Shengte WANG Zhan ZHENG Dingsheng WAN Jun FENG School of Computer & Information Hohai University

ISBN: (纸本)9781467399623

Current algorithms that utilize water index to extract water information from high resolution remote sensing image are inadequate in that it is difficult to determine the optimal thresholds, the result of water boundary is not satisfactory and prone to error. We propose a new algorithm which combines image segmentation algorithm based on MRF model with Normalized Difference Water Index (NDWI) for extracting water information. We represent the pixels in image as random variables in a MRF model and introduce a hybrid feature space in the energy function on these variables, then minimize the energy function by iterative graph cut scheme to find the optimal water boundary. Water boundary is refined according to water index and color features of extracted main water body. The experiments on ShiLianghe Reservoir show that our approach can achieve a better accuracy and the boundary is precise, especially at the reservoirs that surrounding environments are complicated.

关键词： MRF model NDWI Graph cut Hybrid feature space Water information extraction Water Boundary images Eigenvector algorithms high resolution remote sensing information High definition remote sensing image segmentation algorithm color feature reservoirs

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