检索结果-内蒙古大学图书馆

simple linear iterative clustering based low-cost pseudo-LiDAR for 3D object detection in autonomous driving

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 25253-25269页

作者： Le, Duy Nguyen, Linh Australian Natl Univ Coll Engn & Comp Sci Canberra ACT 0200 Australia Federat Univ Australia Sch Engn Informat Technol & Phys Sci Churchill Vic 3842 Australia

The paper presents a low-cost and LiDAR-free approach to efficiently detect 3D objects from stereo camera images, towards autonomous driving applications. It is first proposed to exploit the simple linear iterative clustering algorithm to segment stereo images into superpixel feature maps. The segmented superpixel maps are then used to estimate a depth map. By utilizing the depth map and stereo images, a 3D point cloud can be generated;and the 3D data is considered as pseudo-LiDAR representation as it is similar to measurements collected by a LiDAR sensor. The generated pseudo-LiDAR point cloud can ultimately be fed into any the state-of-the-art LiDAR based 3D object detection techniques to localize objects. By doing this, the proposed approach can effectively detect 3D objects by only employing low-cost stereo cameras, which can save tens of thousands of dollars on LiDAR costs from the existing LiDAR based methods. Effectiveness of the proposed algorithm was evaluated in the real-world KITTI dataset where the obtained results are about 1.33% better than those obtained by the benchmarking pseudo-LiDAR++ method (You et al. 2020).

关键词： LiDAR 3D object detection simple linear iterative clustering Superpixel Autonomous driving

来源：评论

学校读者我要写书评

暂无评论

Video superpixels generation through integration of curvelet transform and simple linear iterative clustering

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第17期78卷 25185-25219页

作者： Bommisetty, Reddy Mounika Prakash, Om Khare, Ashish Univ Allahabad Dept Elect & Commun Allahabad Uttar Pradesh India Nirma Univ Inst Technol Nirma India

Superpixel generation finds wide variety of applications in the field of image processing, particularly brain tumour detection, human body pose estimation, person re-identification as a pre-processing step. In this paper, we present a novel superpixel segmentation approach through integration of curvelet transform and conventional simple linear iterative clustering (SLIC) for image and video. The proposed algorithm follows a two-step framework, clustering in curvelet and spatial domain separately. It performs conventional simple linear iterative clustering on curvelet coefficients obtained at different decomposition levels of R, G, B components as well as directly on R, G, B components to get initial boundaries of superpixels. We obtain final boundaries of superpixels by taking the high probable boundaries from the initial formed boundaries. By incorporation of curvelets in five different scales the proposed method is capable of generating superpixels with high boundary adherence even in the presence of background change, object motion and noise. Superpixels generated by the proposed method are homogeneous in regions of complex texture and weak boundaries. The method has been tested on different images taken from Berkeley segmentation dataset and frames of several other videos. The algorithm under study is evaluated in terms of ten evaluation parameters: boundary recall, boundary precision, quality percentage, detection percentage, accuracy, Jaccard index, specificity, figure of merit, structural content, achievable segmentation accuracy. The results are tabulated, shown graphically and discussed in detail. The critical analysis of results show sound performance of the proposed method over other state-of- art methods.

关键词： Video segmentation Superpixel simple linear iterative clustering Curvelet transform Structural content Achievable segmentation accuracy

来源：评论

学校读者我要写书评

暂无评论

Automated Assessment of Small Bowel Motility Function Based on simple linear iterative clustering (SLIC) 12

Automated Assessment of Small Bowel Motility Function Based ...

引用

12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chen, Yen-Wei Taniguchi, Ayako Tateyama, Tomoko Furukawa, Akira Kanasaki, Shuzo Cent South Univ Forestry & Technol Coll Comp Sci & Informat Technol Changsha 410004 Hunan Peoples R China Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Shiga 5258577 Japan Tokyo Metropolitan Univ Fac Hlth Sci Tokyo 1920364 Japan Koseikai Takeda Hosp Dept Radiol Kyoto 6008558 Japan

ISBN: (纸本)9781467376822

In this paper, we propose an automated method for assessment of small bowel contraction movement based on the simple linear iterative clustering (SLIC) with cine-MRI. In our proposed method, the small bowel in each frame is considered as a super-pixel and is first segmented by the use of SLIC. SLIC performs a local clustering of pixels in a 3-D space defined by intensity and (x, y) coordinates. Then the dynamic area change of the segmented small bowel is used for quantitative analysis of the small bowel motility function. Compared with existing methods, our proposed method can achieve better results.

关键词： Small Bowel Bowel Motility Cine-MRI Spuerpixel Segmentation simple linear iterative clustering

来源：评论

学校读者我要写书评

暂无评论

IFT-SLIC: A general framework for superpixel generation based on simple linear iterative clustering and image foresting transform 28

IFT-SLIC: A general framework for superpixel generation base...

引用

28th SIBGRAPI Conference on Graphics, Patterns and Images SIBGRAPI

作者： Alexandre, Eduardo Barreto Chowdhury, Ananda Shankar Falcao, Alexandre Xavier Vechiatto Miranda, Paulo A. Univ Sao Paulo Inst Math & Stat IME Dept Comp Sci Sao Paulo SP Brazil Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Univ Estadual Campinas Inst Comp Dept Informat Syst Campinas SP Brazil

ISBN: (纸本)9781467379625

Image representation based on superpixels has become indispensable for improving efficiency in Computer Vision systems. Object recognition, segmentation, depth estimation, and body model estimation are some important problems where superpixels can be applied. However, superpixels can influence the efficacy of the system in positive or negative manner, depending on how well they respect the object boundaries in the image. In this paper, we improve superpixel generation by extending a popular algorithm - simple linear iterative clustering (SLIC) - to consider minimum path costs between pixel and cluster centers rather than their direct distances. This creates a new Image Foresting Transform (IFT) operator that naturally defines superpixels as regions of strongly connected pixels by choice of the most suitable path-cost function for a given application. Non-smooth connectivity functions are also explored in our IFT-SLIC approach leading to improved performance. Experimental results indicate better superpixel extraction using the proposed approach as compared to that of SLIC.

关键词： simple linear iterative clustering Image Foresting Transform Superpixel unsupervised segmentation

来源：评论

学校读者我要写书评

暂无评论

Evaluating segmentation methods for UAV-Based Spoil Pile Delineation

引用

Scientific Reports 2025年第1期15卷 1-13页

作者： Thiruchittampalam, Sureka Banerjee, Bikram Pratap Glenn, Nancy F Raval, Simit School of Minerals and Energy Resources Engineering University of New South Wales Sydney 2052 NSW Australia Department of Earth Resources Engineering University of Moratuwa Moratuwa 01400 Sri Lanka School of Surveying and Built Environment University of Southern Queensland Toowoomba 4350 QLD Australia Department of Geosciences Boise State University Boise ID United States

Mine waste dumps consist of individual, blob-like spoil piles, each with unique geological and geotechnical attributes that contribute to the overall stability of the dump. Manually characterising these individual spoil piles presents challenges due to issues of accessibility, safety risks, and time consumption. Analysis of remotely acquired images, through object-based classification, offers a promising solution for the effective identification and characterisation of individual spoil piles. However, object-based classification’s effectiveness hinges on segmentation, an aspect often overlooked in spoil pile analysis. Therefore, this study aims to identify and compare various segmentation approaches to pave the way for image-based spoil characterisation. A comparative analysis is conducted between traditional segmentation approaches and those rooted in deep learning methodologies. Among the diverse segmentation approaches evaluated, the morphology-based deep learning segmentation approach, Segment Anything Model (SAM), exhibited superior performance compared to other approaches. This outcome underscores the effectiveness of incorporating morphological data and deep learning techniques for accurate and efficient segmentation of spoil pile. The findings of this study provide valuable insights into the optimisation of segmentation strategies, thereby contributing to the application of image-based monitoring of spoil piles and promoting the sustainable and hazard free management of mine dump environments. © The Author(s) 2024.

关键词： Mean shift segmentation Segment anything model simple linear iterative clustering StarDist segmentation Voronoi-based segmentation

来源：评论

学校读者我要写书评

暂无评论

Fractional Vegetation Cover Estimation using Hough Lines and linear iterative clustering 5

Fractional Vegetation Cover Estimation using Hough Lines and...

引用

5th IEEE International Conference on Image Processing Applications and Systems (IPAS)

作者： Margapuri, Venkat Rife, Trevor Courtney, Chaney Schlautman, Brandon Zhao, Kai Neilsen, Mitchell Villanova Univ Dept Comp Sci Villanova PA 19085 USA Clemson Univ Dept Plant & Environm Sci Clemson SC USA Land Inst Salina KS USA Kansas State Univ Dept Comp Sci Manhattan KS 66506 USA

ISBN: (纸本)9781665462198

A common requirement of plant breeding programs across the country is companion planting - growing different species of plants in close proximity so they can mutually benefit each other. However, the determination of companion plants requires meticulous monitoring of plant growth. The technique of ocular monitoring is often laborious and error prone. The availability of image processing techniques can be used to address the challenge of plant growth monitoring and provide robust solutions that assist plant scientists to identify companion plants. This paper presents a new image processing algorithm to determine the amount of vegetation cover present in a given area, called fractional vegetation cover. The proposed technique draws inspiration from the trusted Daubenmire method for vegetation cover estimation and expands upon it. Briefly, the idea is to estimate vegetation cover from images containing multiple rows of plant species growing in close proximity separated by a multi-segment PVC frame of known size. The proposed algorithm applies a Hough Transform and simple linear iterative clustering (SLIC) to estimate the amount of vegetation cover within each segment of the PVC frame. When applied as a longitudinal study on a 177 field image dataset, this analysis provides crucial insights into plant growth. As a means of comparison, the proposed algorithm is compared with SamplePoint and Canopeo, two trusted applications used for vegetation cover estimation. The comparison shows a 99% similarity with both SamplePoint and Canopeo demonstrating the accuracy and feasibility of the algorithm for fractional vegetation cover estimation.

关键词： Fractional vegeation cover estimation hough transform simple linear iterative clustering Daubenmire metho

来源：评论

学校读者我要写书评

暂无评论

Identification of Damaged Building Regions From High- Resolution Images Using Superpixel-Based Gradient and Autocorrelation Analysis

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1010-1024页

作者： Liu, Chaoxian Sui, Haigang Huang, Lihong Wuhan Univ State Key Lab Informat Engn Surveying Mappint & R Wuhan 430079 Peoples R China

An efficient and accurate identification of damaged buildings after earthquakes is critical for ensuring a quick response and efficient rescue operations. Considering the time pressure during disaster emergency response and the convenience of practical application, a new approach for identifying areas with damaged buildings is proposed in this study. The approach is validated on high-resolution images from areas impacted by the Haiti earthquake. The approach consists of the following steps. (i), the simple linear iterative clustering method is improved to segment the images into high-quality superpixels by combining both color and texture information;(ii) the characteristics of the damage in the area within the cluster and the difference between pre- and post-earthquake images are obtained at superpixel level using the Sobel gradient;and (iii) local indicators from the spatial association analysis are used to extract the result of the gradient clustering using the synthetic- damage index calculated by an improved relief algorithm;and (iv) vegetation and shadows are masked out to identify the damaged buildings. This framework achieved an overall accuracy of similar to 90% in terms of visual interpretation, with precision and recall of similar to 85% and similar to 90%, respectively. Inaccuracies mainly occur around the boundaries of the damaged building regions. It is evaluated that the proposed framework is more convenient compared to other supervised and unsupervised methods. The successful application in different scenes demonstrates that the proposed framework has the ability to rapidly and accurately identify regions with damaged buildings, which is beneficial for post-disaster emergency assessment.

关键词： Buildings Feature extraction Indexes Image color analysis clustering algorithms Image segmentation Earthquakes Earthquake building damage simple linear iterative clustering synthetic damage index improved-relief local indicators of spatial association analysis

来源：评论

学校读者我要写书评

暂无评论

An improved method for SIFT-based copy move forgery detection using non-maximum value suppression and optimized J-Linkage

引用

SIGNAL PROCESSING-IMAGE COMMUNICATION 2017年 57卷 113-125页

作者： Jin, Guonian Wan, Xiaoxia Wuhan Univ Sch Printing & Packaging Engn Wuhan Hubei Peoples R China Hubei Univ Sci & Technol Coll Comp Sci & Technol Xianning Peoples R China

In looking to improve the detection performance of the keypoint-based method involving smooth tampered regions, there are three problems to be addressed, namely the nonuniform distribution of the keypoints, the discriminative power of low contrast keypoints, and the high computational cost of clustering. In this study, the classical implementation framework of the keypoint-based method is improved by introducing new techniques and algorithms in order to overcome these problems. First, to acquire uniformly distributed keypoints in the test image, we propose a new solution of selecting the keypoints by region instead of contrast. To this end, we first separate the keypoint detection and selection processes. After obtaining all discernible keypoints, we adapt the non-maximum value suppression algorithm to select keypoints by combining the contrast and density of each keypoint. Second, we apply the opponent scale-invariant feature transform descriptor to enhance the discriminative power of keypoints by adding color information. Finally, to alleviate the computational cost of clustering, we optimize the J-Linkage algorithm by altering the method of computing initial clusters and affine transformation hypotheses. For this purpose, we propose the matched pair grouping algorithm that can obtain a smaller number of initial clusters by utilizing the correspondence between the superpixels in the original and duplicated regions. Experiments performed on three representative datasets confirm that the proposed method can significantly improve the detection performance in smooth tampered regions, and considerably reduce the clustering time in the case of a mass of matched pairs, compared with the state-of-the-art methods. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Copy move forgery detection Non-maximum value suppression OpponentSlFT simple linear iterative clustering J-Linkage

来源：评论

学校读者我要写书评

暂无评论

Rethinking Road Surface 3-D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 5799-5808页

作者： Fan, Rui Ozgunalp, Umar Wang, Yuan Liu, Ming Pitas, Ioannis Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Ophthalmol La Jolla CA 92093 USA Cyprus Int Univ Dept Elect & Elect Engn TR-518040 Mersin Turkey SmartMore Ind R&D Ctr Shenzhen Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.

关键词： Roads Surface morphology Sensors Three-dimensional displays Cameras Surface reconstruction Estimation Disparity map transformation perspective transformation pothole detection simple linear iterative clustering

来源：评论

学校读者我要写书评

暂无评论

Image splicing localization using noise distribution characteristic

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第16期78卷 22223-22247页

作者： Zhang, Depeng Wang, Xiaofeng Zhang, Meng Hu, Jiaojiao Xian Univ Technol Xian 710048 Shaanxi Peoples R China

Image splicing/compositing is common content tampering operation. In this work, we devote to improve the detection accuracy of the splicing/compositing attack for image, and propose an effective image splicing localization method based on the noise distribution characteristic in image. Firstly, the test image is divided into non-overlapping blocks by using an improved simple linear iterative clustering (SLIC) algorithm. Then block-wise local noise level estimation and noise distribution characteristic estimation are performed to generate distinguishing features. Utilizing the fact that image regions from different sources tend to have larger inter-class difference, the fuzzy c-means clustering is used to identify spliced regions. Compared to existing noise-based image splicing detection methods, experimental results on different datasets have shown that the proposed method has superior performance, especially when the noise difference between the spliced region and the original region is small. Moreover, the proposed method is robust for content-preserving manipulations.

关键词： Image splicing detection Image splicing localization simple linear iterative clustering Noise distribution characteristic Fuzzy c-means clustering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：