Saliency Region Selection in Large Aerial Imagery using Multi-scale SLIC Segmentation

1 : Saliency Region Selection in Large Aerial Imagery using Multi-scale SLIC Segmentation,
Proc. SPIE 8360, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications IX, 2012

2 :

此论文中值得留意的几个观点:

(1)

At a glance, salient regions are stand out while the rest of the scene is neglected since they do not attract visual attention.

(2)

When large and mostly uniform background regions, such as land, sea and snow field, are segmented by the superpixels of smaller scale, most superpixels will be of the average size and regular shape, as shown in left
figure.

(3)

With the presence of the objects of interest, the superpixels will adhere tightly to the object structural boundaries, resulting in various sizes and strongly deformed shapes, as shown in right figure.

(4)

The irregularity in size and shape of the superpixels may serve as a measure of saliency. The method is valid when the superpixel scale is smaller that of the regions of interest.

(5)

In the case when the sizes of the salient regions are unknown, or the objects of interest have various sizes over the scene, the multi-scale SLIC may be applied with a set of the preset scale values.

(6)

Firstly, when the scale S is much smaller than the size of the salient structures, most superpixels will be of average size and regular shape.

Secondly, when the scale S is much larger than the size of the objects of interest, the object detail structures will be ignored in the SLIC segmentation.

(7)

In the multi-scale SLIC segmentation, the variation in size and shape of a superpixel associated to the presence of the object of interest can be characterized by the
Hausdorff distance defined in the superpixel string-to-string presentation.

3: 论文算法流程

时间: 2024-10-08 02:23:34

Saliency Region Selection in Large Aerial Imagery using Multi-scale SLIC Segmentation的相关文章

目标检测之显著区域检测---国外的一个图像显著区域检测代码及其效果图 saliency region detection

先看几张效果图吧 效果图: 可以直接测试的代码: 头文件: // Saliency.h: interface for the Saliency class.//////////////////////////////////////////////////////////////////////////===========================================================================// Copyright (c) 2009 R

{ICIP2014}{收录论文列表}

This article come from HEREARS-L1: Learning Tuesday 10:30–12:30; Oral Session; Room: Leonard de Vinci 10:30  ARS-L1.1—GROUP STRUCTURED DIRTY DICTIONARY LEARNING FOR CLASSIFICATION Yuanming Suo, Minh Dao, Trac Tran, Johns Hopkins University, USA; Hojj

CVPR 2017 Paper list

CVPR2017 paper list Machine Learning 1 Spotlight 1-1A Exclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaojie Guo, Xiaobo Wang, Zhen Lei, Changqing Zhang, Stan Z. Li Borrowing Treasures From the Wealthy: Deep Transfer Learning Thro

CVPR 2015 papers

CVPR2015 Papers震撼来袭! CVPR 2015的文章可以下载了,如果链接无法下载,可以在Google上通过搜索paper名字下载(友情提示:可以使用filetype:pdf命令). Going Deeper With ConvolutionsChristian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke

【GDAL】GDAL栅格数据结构学习笔记(一): 关于Metadata

在维护一段代码时看到前任程序员写的获取栅格数据的CellSize的功能,竟然在知道GDAL的情况下去调用AE的接口来解算,觉得费解. 原来的思路是使用AE的Raster对象读取出Raster的文件大小和真实投影坐标对构造的矩形外框,再来算每个cell的长宽,觉得实在无语. 于是研究了下GDAL怎么获取到一些数据基本信息(Metadata)的. 搬运一下GDAL官方对其数据模型的Metadata的描述: GDAL metadata is auxiliary format and applicati

ECCV 2014 Results (16 Jun, 2014) 结果已出

Accepted Papers     Title Primary Subject Area ID 3D computer vision 93 UPnP: An optimal O(n) solution to the absolute pose problem with universal applicability 128 Video Registration to SfM Models 168 Image-based 4-d Modeling Using 3-d Change Detect

图像处理与计算机视觉基础,经典以及最近发展

*************************************************************************************************************** 在这里,我特别声明:本文章的源作者是   杨晓冬  (个人邮箱:[email protected]).原文的链接是 http://www.iask.sina.com.cn/u/2252291285/ish.版权归 杨晓冬 朋友所有. 我非常感谢原作者辛勤地编写本文章,并愿意共

Hbase框架原理及相关的知识点理解、Hbase访问MapReduce、Hbase访问Java API、Hbase shell及Hbase性能优化总结

转自:http://blog.csdn.net/zhongwen7710/article/details/39577431 本blog的内容包含: 第一部分:Hbase框架原理理解 第二部分:Hbase调用MapReduce函数使用理解 第三部分:Hbase调用Java API使用理解 第四部分:Hbase Shell操作 第五部分:Hbase建表.读写操作方式性能优化总结 第一部分:Hbase框架原理理解 概述 HBase是一个构建在HDFS上的分布式列存储系统:HBase是基于Google

How to Tell Science Stories with Maps

Reported Features How to Tell Science Stories with Maps August 25, 2015   Greg Miller This map, part of Audubon’s Birds and Climate Change report, depicts predicted changes in tree swallows’ summer (yellow) and winter (blue) ranges. Maps are amazing