Computer Vision:a Modern Approach 摘抄笔记——Chapter 9:Segmentation by Clustering

本文探讨了人类视觉系统中图像分割的基本原理,并介绍了基于聚类的图像分割方法。文中详细阐述了Gestalt心理学派提出的视觉元素组合原则,包括相似性、连续性和熟悉配置等,并讨论了图像分割在背景减除、镜头边界检测等实际应用。

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Chapter 9:Segmentation by Clustering

9.1   Human Vision: Grouping and Gestalt

A key feature of the human vision systemis that context affects how things are perceived.

A common experience of segmentation is the way that an image can resolve itself into a figure— typically, the significant, important object—and aground—the background on which the figure lies. 

The Gestalt school used the notion of a gestalt as central components in their ideas. Their work was characterized by attempts to write down a series of rules by which image elements would be associated together and interpreted as a group.

There are a variety of factors, some of which postdate the main Gestalt movement:

  • Proximity: Tokens that are nearby tend to be grouped.
  • Similarity: Similar tokens tend to be grouped together.
  • Common fate: Tokens that have coherent motion tend to be grouped together.
  • Common region: Tokens that lie inside the same closed region tend to be
    grouped together.
  • Parallelism: Parallel curves or tokens tend to be grouped together.
  • Closure: Tokens or curves that tend to lead to closed curves tend to be
    grouped together.
  • Symmetry: Curves that lead to symmetric groups are grouped together.
  • Continuity: Tokens that lead to continuous—as in joining up nicely, rather
    than in the formal sense—curves tend to be grouped.
  • Familiar configuration: Tokens that, when grouped, lead to a familiar
    object tend to be grouped together.

    


               

但是,如何把上面这些rules用于形成算法还有难度,如无法把握何时选用哪条规则。

9.2   Important Applications

  • 9.2.1 Background Subtraction
对于视频中背景移动改变等问题,一般用 a moving average 来估计背景像素的值。
  • 9.2.2 Shot Boundary Detection

长video是由很多短镜头(shots) 组成的,每个镜头中大多物体是不变的,每个shot可以用一个关键帧表示。

A shot boundary detection algorithm must find frames in the video that are significantly different from the previous frame. 可以用distance表示,计算distance有几种方法,目前不太需要,此处略去,有需要可以翻看原书。

  • 9.2.3 Interactive Segmentation
  • 9.2.4 Forming Image Regions
9.3 IMAGE SEGMENTATION BY CLUSTERING PIXELS

Capsule Networks for Computer Vision: A Survey 胶囊网络在计算机视觉中的应用:一篇综述 Abstract: 摘要: Capsule Networks (CapsNets)是一种新颖的深度神经网络架构,旨在克服传统卷积神经网络(CNNs)的一些限制,例如旋转不变性和视角不变性。Capsule Networks使用胶囊来表示图像或对象的各个特征,并且能够学习对象的姿态和空间关系。本文旨在提供对Capsule Networks的综述,重点介绍其在计算机视觉中的应用。我们首先介绍了Capsule Networks的基本原理和结构,并讨论了其与CNNs的区别。然后,我们概述了Capsule Networks在图像分类、目标检测、语义分割和图像生成等任务中的应用。接下来,我们总结了当前在Capsule Networks领域的最新研究进展,并讨论了该领域未来的发展方向。 Capsule Networks (CapsNets) are a novel deep neural network architecture aimed at overcoming some of the limitations of traditional Convolutional Neural Networks (CNNs), such as rotational and viewpoint invariance. Capsule Networks use capsules to represent various features of an image or object and are capable of learning the pose and spatial relationships of objects. This paper aims to provide a survey of Capsule Networks, with a focus on their applications in computer vision. We first introduce the basic principles and structure of Capsule Networks and discuss their differences with CNNs. Then, we outline the applications of Capsule Networks in tasks such as image classification, object detection, semantic segmentation, and image generation. Next, we summarize the latest research developments in the field of Capsule Networks and discuss future directions in this field.
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