Learn Computer Vision Using OpenCV: With Deep Learning CNNs and RNNs
Gollapudi, Sunila
- 出版商: Apress
- 出版日期: 2019-04-27
- 售價: $1,980
- 貴賓價: 9.5 折 $1,881
- 語言: 英文
- 頁數: 151
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484242602
- ISBN-13: 9781484242605
-
相關分類:
影像辨識 Image-recognition、DeepLearning、Computer Vision
海外代購書籍(需單獨結帳)
買這商品的人也買了...
-
$520$494 -
$4,010$3,810 -
$162OpenCV 3 計算機視覺 : Python 語言實現, 2/e (Learning OpenCV 3 Computer Vision with Python, 2/e)
-
$195Modbus 軟件開發實戰指南
-
$580$493 -
$590$460 -
$2,052Head First Agile: A Brain-Friendly Guide to Agile and the PMI-ACP Certification
-
$1,940$1,843 -
$301OpenCV Android 開發實戰
-
$650$585 -
$352FFmpeg 從入門到精通
-
$500$390 -
$1,220$1,159 -
$1,000$790 -
$650$553 -
$454Android 移動開發案例課堂
-
$1,780$1,691 -
$1,250$1,188 -
$650$514 -
$980$833 -
$480$379 -
$556機器視覺 — 使用 HALCON 描述與實現
-
$393機器視覺從入門到提高
-
$400$380 -
$299$284
相關主題
商品描述
Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples.
The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you'll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision.
After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work.
What You Will Learn
- Understand what computer vision is, and its overall application in intelligent automation systems
- Discover the deep learning techniques required to build computer vision applications
- Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy
- Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis
Who This Book Is ForThose who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.
商品描述(中文翻譯)
使用Python和OpenCV庫構建實用的計算機視覺應用程式。本書討論了計算機視覺的不同方面,如圖像和物體檢測、跟踪和運動分析,並通過示例介紹了它們的應用。
作者首先介紹了計算機視覺,然後使用Python從頭開始設置OpenCV。下一部分討論了專門的圖像處理和分割,以及計算機如何存儲和處理圖像。這涉及使用OpenCV庫進行模式識別和圖像標記。接下來,您將使用OpenCV進行物體檢測、視頻存儲和解釋,以及使用OpenCV進行人體檢測。詳細討論了跟踪和運動。本書還討論了使用CNN和RNN創建複雜的深度學習模型。最後,作者總結了計算機視覺中的最新應用和趨勢。
閱讀本書後,您將能夠使用Python和OpenCV理解和實現計算機視覺及其應用。您還將能夠使用CNN和RNN創建深度學習模型,並了解這些尖端深度學習架構的工作原理。
您將學到什麼:
- 理解計算機視覺是什麼,以及在智能自動化系統中的應用
- 發現構建計算機視覺應用所需的深度學習技術
- 使用最新的OpenCV、Python和NumPy技術構建複雜的計算機視覺應用
- 創建實用的應用和實現,如人臉檢測和識別、手寫識別、物體檢測、跟踪和運動分析
本書適合對機器學習和Python有基本了解並希望學習計算機視覺及其應用的讀者。
作者簡介
Sunila Gollapudi has over 17 years of experience in developing, designing and architecting data-driven solutions with a focus on the banking and financial services sector. She is currently working at Broadridge, India as vice president. She's played various roles as chief architect, big data and AI evangelist, and mentor.
She has been a speaker at various conferences and meetups on Java and big data technologies. Her current big data and data science expertise includes Hadoop, Greenplum, MarkLogic, GemFire, ElasticSearch, Apache Spark, Splunk, R, Julia, Python (scikit-learn), Weka, MADlib, Apache Mahout, and advanced analytics techniques such as deep learning, computer vision, reinforcement, and ensemble learning.
作者簡介(中文翻譯)
Sunila Gollapudi在開發、設計和架構數據驅動解決方案方面擁有超過17年的經驗,專注於銀行和金融服務行業。她目前在印度的Broadridge擔任副總裁。她曾擔任過首席架構師、大數據和人工智能倡導者以及導師等不同角色。
她曾在各種關於Java和大數據技術的會議和聚會上擔任演講嘉賓。她目前在大數據和數據科學方面的專業知識包括Hadoop、Greenplum、MarkLogic、GemFire、ElasticSearch、Apache Spark、Splunk、R、Julia、Python(scikit-learn)、Weka、MADlib、Apache Mahout以及深度學習、計算機視覺、強化學習和集成學習等高級分析技術。