Computational Texture and Patterns: From Textons to Deep Learning (Synthesis Lectures on Computer Vision)

Kristin J. Dana

  • 出版商: Morgan & Claypool
  • 出版日期: 2018-09-13
  • 定價: $2,420
  • 售價: 9.0$2,178
  • 語言: 英文
  • 頁數: 113
  • 裝訂: Hardcover
  • ISBN: 1681732696
  • ISBN-13: 9781681732695
  • 相關分類: DeepLearningComputer Vision
  • 立即出貨 (庫存=1)

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商品描述

Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

商品描述(中文翻譯)

視覺模式分析是在挖掘知識方面的基本工具。模式和紋理的計算表示使我們能夠總結、存儲、比較和標記,以便了解物理世界。我們使用相機和傳感器捕捉視覺影像的能力已經產生了大量的原始數據,但要以特定任務的方式有效地使用這些信息,需要複雜的計算表示。我們列舉了這些表示的具體理想特徵:(1)類內不變性-支持識別;(2)對光照和幾何不變性-以適應成像條件;(3)支持預測和合成-使用模型推斷模式的延續;(4)支持變化檢測-檢測異常和干擾;(5)支持基於物理的解釋-從外觀推斷系統特性。近年來,計算機視覺經歷了一次變革,經典算法適應了深度學習的新趨勢。本文介紹了算法演化的過程,包括模式識別、分割和合成。我們考慮了視覺模式分析的普遍相關性和重要性,以及依賴於計算模型的應用。