Visual Object Recognition (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Kristen Grauman, Bastian Leibe

  • 出版商: Morgan & Claypool
  • 出版日期: 2010-11-01
  • 售價: $1,570
  • 貴賓價: 9.5$1,492
  • 語言: 英文
  • 頁數: 182
  • 裝訂: Paperback
  • ISBN: 1598299689
  • ISBN-13: 9781598299687
  • 相關分類: 人工智慧Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization.

Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

商品描述(中文翻譯)

視覺識別問題是計算機視覺研究的核心。從機器人技術到信息檢索,許多期望的應用都需要能夠識別和定位類別、地點和物體。這個教程概述了用於視覺物體識別和圖像分類的計算機視覺算法。我們介紹了主要的表示方法和學習方法,並強調了該領域的最新進展。目標受眾是從事人工智慧、機器人技術或視覺研究的研究人員或學生,他們希望了解這些問題可用的各種方法和表示。這堂課總結了今天可以可靠地做到的和不能做到的事情,並概述了可以在需要視覺分類的系統中使用的關鍵概念。

目錄:
引言 / 概述:特定物體的識別 / 局部特徵:檢測與描述 / 匹配局部特徵 / 匹配特徵的幾何驗證 / 示例系統:特定物體識別 / 概述:通用物體類別的識別 / 物體類別的表示 / 通用物體檢測:尋找和評分候選者 / 學習通用物體類別模型 / 示例系統:通用物體識別 / 其他考量與當前挑戰 / 結論