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8th International Symposium on Visual Computing (ISVC)

作者： Vu, Huyen Olsson, Roland Ostfold Univ Coll Halden Norway

ISBN: (纸本)9783642331916

Automatic Design ofAlgorithms through Evolution (ADATE) is a system for fully automatic programming that has the ability to either generate algorithms from scratch or improve existing ones. In this paper, we employ ADATE to improve a standard image processing algorithm, namely graph based segmentation (GBS), which has emerged as one of the very most popular methods for image segmentation, that is partitioning an image into regions. The key contribution of the paper is to show that a proven and well-known computer vision code is easy to improve through automatic programming. This may presage a change to the entire field of computer vision where automatic programming becomes a routine way of improving standard as well as state-of-the art image processing and pattern analysis algorithms. GBS was mostly chosen as case study to investigate how useful the ADATE automatic programming system may be in computer vision. Numerous other algorithms in the field could have been chosen instead.

关键词： Image processing graph based segmentation Machine learning Automatic programming ADATE

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TOPOLOGY CONSTRAINT graph-based MODEL FOR NON-SMALL-CELL LUNG TUMOR segmentation FROM PET VOLUMES 11

TOPOLOGY CONSTRAINT GRAPH-BASED MODEL FOR NON-SMALL-CELL LUN...

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11th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Cui, Hui Wang, Xiuying Zhou, Jianlong Fulham, Michael Eberl, Stefan Feng, Dagan Univ Sydney Sch IT BMIT Res Grp Sydney NSW 2006 Australia Natl ICT Australia Sydney NSW Australia Royal Prince Alfred Hosp Dept PET & Nucl Med Camperdown NSW Australia Univ Sydney Sydney Med Sch Sydney NSW 2006 Australia

ISBN: (纸本)9781467319614

We propose a graph-based segmentation model that takes into account both topological information and intensity similarity to segment non-small-cell lung carcinoma (NSCLC) from PET volumes. The proposed model estimates the probabilities of each voxel belonging to the given foreground and background labels. The topological information is derived from our region of interest topological skeleton tree model (ROI-TKT) to better capture the inclusion and nesting information. Topological information contributes to the separation of tumors from adjacent lesions with similar intensities and the delineation of tumors with heterogeneous density distributions. The experimental evaluation on 20 PET volumes with manual tumor segmentation results from two doctors demonstrated that our method outperformed two other graph-based segmentation methods as measured by the Dice similarity coefficient (DSC).

关键词： PET graph based segmentation topology

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Improved Face Recognition Method based on segmentation Algorithm using SIFT-PCA

Improved Face Recognition Method based on Segmentation Algor...

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35th International Conference on Telecommunications and Signal Processing (TSP)

作者： Kamencay, Patrik Breznan, Martin Jelsovka, Dominik Zachariasova, Martina Univ Zilina Dept Telecommun & Multimedia Zilina Slovakia

ISBN: (纸本)9781467311182

This paper provides an example of the face recognition using SIFT-PCA method and impact of graph based segmentation algorithm on recognition rate. Principle component analysis (PCA) is a multivariate technique that analyzes a face data in which observation are described by several inter-correlated dependent variables. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. The paper presents a proposed methodology for face recognition based on preprocessing face images using segmentation algorithm and SIFT (Scale Invariant Feature Transform) descriptor. The algorithm has been tested on 50 subjects (100 images). The proposed method first was tested on ESSEX face database and next on own segmented face database using SIFT-PCA. The experimental result shows that the segmentation in combination with SIFT-PCA has a positive effect for face recognition and accelerates the recognition PCA technique.

关键词： graph based segmentation PCA SIFT ESSEX database face recognition

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Nuclei segmentation from Breast Cancer Histology Images

Nuclei Segmentation from Breast Cancer Histology Images

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1st International Conference on Emerging Trends and Applications in Computer Science (ICETACS)

作者： Nikam, Amresh Gopal, Arpita Shresh Gyan Vihar Univ Jaipur Rajasthan India Sinhgad Inst Bussiness Adm & Res Pune Maharashtra India

ISBN: (纸本)9781467352499

Histology image analysis using computer-aided diagnosis systems has become increasingly important during the last years. One reason is the need to alleviate the heavy workload of medical experts. In this paper, we introduce a general purpose framework which is able to segmentation of cell nuclei from microscopic images based on graph based segmentation and threshold methods.

关键词： Breast cancer graph based segmentation Thresholding

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Hyperspectral Image Denoising with segmentation-based Low Rank Representation

Hyperspectral Image Denoising with Segmentation-based Low Ra...

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30th IEEE Conference on Visual Communications and Image Processing (VCIP)

作者： Ma, Jiayi Jiang, Junjun Li, Chang Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

ISBN: (纸本)9781509053162

Recently, low-rank representation (LRR) based hyperspectral image (HSI) denoising method has been proven to be a powerful tool for removing different kinds of noise simultaneously, such as Gaussian, dead pixels and impulse noise. However, the LRR based method cannot make full use of the spatial information in HSI. In this paper, we integrate the graph based segmentation (GS) into the LRR, and propose a novel denoising method named GS-LRR. We first use the principle component analysis (PCA) to obtain the first principle component of HSI. Then the graph based segmentation is adopted to the first principle component of 1151 to get homogeneous regions Finally, we employ the LRR to each homogeneous region of HSI, which enable us to simultaneously remove all the above mentioned mixed noise. Extensive experiments on both simulated and real HSIs demonstrate the efficiency of the proposed GS-LRR.

关键词： Denoising graph based segmentation hyper-spectral image low-rank representation mixed noise

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3D graph segmentation for Target Detection in FOPEN LiDAR Data

3D Graph Segmentation for Target Detection in FOPEN LiDAR Da...

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Conference on Laser Radar Technology and Applications XVIII

作者： Shorter, Nicholas Locke, Judson Smith, O'Neil Keating, Emma Smith, Philip Harris Corp Melbourne FL 32902 USA

ISBN: (纸本)9780819495228

A novel use of Felzenszwalb's graph based efficient image segmentation algorithm* is proposed for segmenting 3D volumetric foliage penetrating (FOPEN) Light Detection and Ranging (LiDAR) data for automated target detection. The authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form the graph for segmentation. Following graph formation, the angular difference in the points' estimated normal vectors is proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are presented on several data sets. Felzenszwalb, P. F. and D. P. Huttenlocher, "Efficient graph based Image segmentation", International Journal of Computer Vision, 59( 2), September 2004

关键词： graph based segmentation Foliage Penetration FOPEN Target Detection LiDAR Light Detection and Ranging Data LADAR GPGPU

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The segmentation of a point cloud using locally fitted surfaces 18

The segmentation of a point cloud using locally fitted surfa...

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18th Mediterranean Electrotechnical Conference (MELECON)

作者： Bizjak, Marko Univ Maribor Fac Elect Engn & Comp Sci Maribor Slovenia

ISBN: (纸本)9781509000586

This paper proposes a new method for the segmentation of the scanned point clouds that achieves an accurate definition of objects' faces, critical for their digitalization. The topological structuring of the input point cloud is realised by a nearest-neighbour graph, while locally fitted surfaces are used to quantify the objects' shapes. Noise is filtered by a graph-cut and the segmentation is performed by linking the nodes to those faces to which they fit best. The results show that the proposed method is capable of improving the accuracy of the segmentation by 5%, while reducing the computation time in comparison to the state-of-the-art.

关键词： LiDAR point cloud graph based segmentation LoFS

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Multisensor image fusion based on biorthogonal multiwavelet transform and region competition

Multisensor image fusion based on biorthogonal multiwavelet ...

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5th International Conference on Wavelet Analysis and Pattern Recognition

作者： Hong, Ri-Chang Ge, Yong Wu, Xiu-Qing Univ Sci & Technol Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China

ISBN: (纸本)9781424410651

This paper proposed a novel region based novel multisensor image fusion algorithm. A graph based segmentation method and biorthogonal multiwavelet transform (BMWT) is combined to segment the features of the source images to produce a region map. Then we use a scheme, which calculates the characteristics of each region of sources in wavelet domain by averaging and selection, to fuse the images. The experiment results demonstrate that our proposed algorithm is comparable to pixel based fusion methods, moreover has many advantages such as reduced sensitivity to noise and mis-registration, more flexible fusion scheme despite its increase in complexity.

关键词： image fusion region based fusion graph based segmentation average and selection

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WALK ENVIRONMENT ANALYSIS USING MY VISION: TOWARD A NAVIGATION SYSTEM PROVIDING VISUAL ASSISTANCE

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第3期15卷 861-871页

作者： Tan, Joo Kooi Ishimine, Tomoki Arimasu, Shohei Kyushu Inst Technol Dept Engn Tobata Ku 1-1 Sensui Cho Fukuoka Fukuoka 8048550 Japan Kyushu Inst Technol Grad Sch Engn Tobata Ku 1-1 Sensui Cho Fukuoka Fukuoka 8048550 Japan

This paper proposes a method of analyzing a human walk environment using MY VISION. MY VISION is an ego-camera and a computer system which analyzes a video obtained from the ego-camera to acquire certain visual information useful for human daily activities. The system is expected to be a virtual eye of a visually impaired person or the third eye of a pedestrian absorbed in a mobile phone. The proposed method keeps in a database the background images of key points along a sidewalk and judges if a MY VISION user is walking along the sidewalk or if he/she has come to a crosswalk by referring to the backgrounds. If the former, the method finds a safe road region on the sidewalk, whereas, if the latter, it searches for the crosswalk for finding an appropriate walk direction and a traffic light to know the proper timing to cross it. Experimental results show effectiveness of the proposed method.

关键词： Safe walk Visually impaired person Ego-camera MY VISION Background Bag of features graph based segmentation Computer vision

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