摘要翻译：
设计了一种基于三次样条插值的图像分割方法，并将其与不同的分割方法进行了比较，以确定哪种方法能够有效地分割图像。本文比较了多项式最小二乘插值法和传统的Otsu阈值法与样条插值法在图像分割中的应用。使用上述技术确定阈值，然后使用这些技术将图像分割成二值图像。将该算法与传统算法进行图像均衡后的结果进行了比较。当与精确分割的图像相比较时，基于偏差和均方误差来确定更好的技术。采用最小偏差和均方误差的图像作为较好的图像处理方法。
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英文标题：
《Cubic Spline Interpolation Segmenting over Conventional Segmentation
  Procedures: Application and Advantages》
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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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英文摘要：
  To design a novel method for segmenting the image using Cubic Spline Interpolation and compare it with different techniques to determine which gives an efficient data to segment an image. This paper compares polynomial least square interpolation and the conventional Otsu thresholding with spline interpolation technique for image segmentation. The threshold value is determined using the above-mentioned techniques which are then used to segment an image into the binary image. The results of the proposed technique are also compared with the conventional algorithms after applying image equalizations. The better technique is determined based on the deviation and mean square error when compared with an accurately segmented image. The image with least amount of deviation and mean square error is declared as the better technique.
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PDF链接：
https://arxiv.org/pdf/1803.04621