000			03941cam a2200301zu 4500
001			88843555
003			FRCYB88843555
005			20250107220618.0
006			m o d
007			cr un
008			250107s2016 fr | o|||||0|0|||eng d
020			_a9781787125490
035			_aFRCYB88843555
040			_aFR-PaCSA _ben _c _erda
100	1		_aHowse, Joseph
245	0	1	_aOpenCV: Computer Vision Projects with Python _c['Howse, Joseph', 'Joshi, Prateek', 'Beyeler, Michael']
264		1	_bPackt Publishing _c2016
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aHowse, Joseph
700	0		_aJoshi, Prateek
700	0		_aBeyeler, Michael
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88843555 _qtext/html _a
520			_aGet savvy with OpenCV and actualize cool computer vision applicationsAbout This BookUse OpenCV's Python bindings to capture video, manipulate images, and track objectsLearn about the different functions of OpenCV and their actual implementations.Develop a series of intermediate to advanced projects using OpenCV and PythonWho This Book Is ForThis learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV.What You Will LearnInstall OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or UbuntuApply "curves" and other color transformations to simulate the look of old photos, movies, or video gamesApply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like imageRecognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorReconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniquesDetect and recognize street signs using a cascade classifier and support vector machines (SVMs)Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMsStrengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn DetailOpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:OpenCV Computer Vision with Python by Joseph HowseOpenCV with Python By Example by Prateek JoshiOpenCV with Python Blueprints by Michael BeyelerStyle and approachThis course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this comprehensive course, you'll learn to create computer vision applications from scratch to finish and more!.
999			_c66156 _d66156