Computer Vision In Python! Face Detection & Image Processing free ebook download.

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Computer Vision In Python! Face Detection & Image Processing free ebook download

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Date:	2020	Format:	MP4	Language:	English	ISBN/ASIN:	0000000000
Pages:		OCR:		Quality:		ISBN13:
Uploader:	oblivion	Upload Date:	2/14/2021 7:15:39 AM

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Learn Computer Vision With OpenCV In Python! Master Python By Implementing Face Recognition & Image Processing In Python
What you'll learn

Use OpenCV to work with image files
Understanding the fundamentals of computer vision & image processing
Use Python and OpenCV to draw shapes on images and videos
Get started with image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
OpenCV Image Manipulation Fundamentals using Python. Also includes a Python basics refresher session.
Open and Stream video with Python and OpenCV
Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
Create Face Detection Software Using Haar Classifier
Have a toolbox of the most powerful Computer Vision models
Understand the theory behind Computer Vision
Create powerful Computer Vision applications

Requirements

Basic Python programming knowledge

Description

Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.

Distinctions

The fields most closely related to computer vision are image processing, image analysis and machine vision. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented.

Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality.

The following characterizations appear relevant but should not be taken as universally accepted:

Image processing and image analysis tend to focus on 2D images, how to transform one image to another, e.g., by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither require assumptions nor produce interpretations about the image content.

There is also a field called imaging which primarily focuses on the process of producing images, but sometimes also deals with processing and analysis of images. For example, medical imaging includes substantial work on the analysis of image data in medical applications.

Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches and artificial neural networks. A significant part of this field is devoted to applying these methods to image data.

Applications

Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for:

Automatic inspection, e.g., in manufacturing applications

Assisting humans in identification tasks, e.g., a species identification system

Controlling processes, e.g., an industrial robot

Detecting events, e.g., for visual surveillance or people counting, e.g., in the restaurant industry

Interaction, e.g., as the input to a device for computer-human interaction

Modeling objects or environments, e.g., medical image analysis or topographical modeling

Navigation, e.g., by an autonomous vehicle or mobile robot  and

Organizing information, e.g., for indexing databases of images and image sequences.

Medicine

One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is detection of tumors, arteriosclerosis or other malign changes  measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information: e.g., about the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical area also includes enhancement of images interpreted by humans-ultrasonic images or X-ray images for example-to reduce the influence of noise.

Machine Vision

A second application area in computer vision is in industry, sometimes called machine vision, where information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects. Another example is measurement of position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in agricultural process to remove undesirable food stuff from bulk material, a process called optical sorting.

Military

Autonomous vehicles

One of the newer application areas is autonomous vehicles, which include submersibles, land-based vehicles (small robots with wheels, cars or trucks), aerial vehicles, and unmanned aerial vehicles (UAV). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, e.g. for knowing where it is, or for producing a map of its environment (SLAM) and for detecting obstacles. It can also be used for detecting certain task specific events, e.g., a UAV looking for forest fires. Examples of supporting systems are obstacle warning systems in cars, and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for autonomous driving of cars, but this technology has still not reached a level where it can be put on the market. There are ample examples of military autonomous vehicles ranging from advanced missiles to UAVs for recon missions or missile guidance. S

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	Title	Link	Password	Size (MB)	Thank to	Inform Admin
2964798	Part 1	http://www.nitroflare.com/view/0D167B1FB...		995.00	oblivion
2964799	Part 2	http://www.nitroflare.com/view/6E855E42F...		472.80	oblivion

Tags: Computer Detection Face Image Processing Python Vision

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