基于随机场与随机簇理论的边缘分布估计图像分割、压缩与重建
Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories
AI总结：
该研究利用随机场和随机簇理论估计图像最小有界区域及强度分布，提出仅需略多于半数像素即可最坏情况下重建图像的压缩方法，并结合定制深度信念网络实现无监督对象分割、检测与定位。
摘要：
随机场与随机簇理论被用于描述若干数学结果，这些结果涉及被表征为通用二维整数阵列的图像像素强度的概率分布。研究估计图像内最小有界区域的尺寸以分割图像，并由此恢复强度的平衡分布。从所估计的有界区域出发，推导出强度的次优分布与平衡分布的性质，进而形成一种图像压缩方法：在最坏情况下，仅需略多于全部像素一半的像素即可重建原始图像。定制的深度信念网络与启发式方法可实现图像中对象的无监督分割、检测和定位。文中给出一个示例来说明这些数学结果。
英文摘要：
Random field and random cluster theory are used to describe certain mathematical results concerning the probability distribution of image pixel intensities characterized as generic $2D$ integer arrays. The size of the smallest bounded region within an image is estimated for segmenting an image, from which, the equilibrium distribution of intensities can be recovered. From the estimated bounded regions, properties of the sub-optimal and equilibrium distributions of intensities are derived, which leads to an image compression methodology whereby only slightly more than half of all pixels are required for a worst-case reconstruction of the original image. A custom deep belief network and heuristic allows for the unsupervised segmentation, detection and localization of objects in an image. An example illustrates the mathematical results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
