高效车位分析中的图像分割与处理

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摘要翻译：
本文利用MATLAB的图像处理能力，提出了一种在分割和轮廓不清的环境中检测空车位的方法。由于泊车位内存在的异常情况，如照明不均匀、槽线扭曲、车与车重叠等。目前的传统算法难以对图像进行精确的处理。该算法结合了图像预处理和伪轮廓检测技术，提高了检测效率。该方法也不需要单独使用传感器来检测一辆汽车，而是使用实时静态图像来考虑一组槽，而不是通常的单槽方法。这大大降低了设计高效停车系统所需的费用。我们将我们的算法与其他技术的性能进行了比较。这些比较表明，该算法可以在忽略虚假数据和其他失真的情况下检测出停车点的空位。
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英文标题：
《Image Segmentation and Processing for Efficient Parking Space Analysis》
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作者：
Chetan Sai Tutika, Charan Vallapaneni, Karthik R, Bharath KP, N Ruban
  Rajesh Kumar Muthu
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  In this paper, we develop a method to detect vacant parking spaces in an environment with unclear segments and contours with the help of MATLAB image processing capabilities. Due to the anomalies present in the parking spaces, such as uneven illumination, distorted slot lines and overlapping of cars. The present-day conventional algorithms have difficulties processing the image for accurate results. The algorithm proposed uses a combination of image pre-processing and false contour detection techniques to improve the detection efficiency. The proposed method also eliminates the need to employ individual sensors to detect a car, instead uses real-time static images to consider a group of slots together, instead of the usual single slot method. This greatly decreases the expenses required to design an efficient parking system. We compare the performance of our algorithm to that of other techniques. These comparisons show that the proposed algorithm can detect the vacancies in the parking spots while ignoring the false data and other distortions.
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PDF链接：
https://arxiv.org/pdf/1803.0462

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关键词：Segmentation Construction Architecture Applications Mathematical 提高 大大降低 方法 detect 进行