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Showing posts from January, 2016

Internet of computer vision things Movidius

About Movidius Share this for more tutorials and computer vision post from me.. Thanks best Vladimir Movidius is interesting company which developed embedded machine vision technology for new segment of fast growing smartphones and IoT apps. Smart devices in internet of things needs smart processing units like this for 3D depth application, reconstruction, detection and Natural user interface. Near future brings challenging task like smart mirror and communication walls and many more "disturbing things". Movidius provides optimized library for algorithm in modern deep learning in combination with low-power visual processing units.  Target Computer vision app  3D modeling and reconstruction in smart devices of eye of thinks apps In door navigation assistant Visual analysis  Augmented reality Recognition and classification apps Market  Smartphones   wearables, action cam , drones , security cameras , surveillance Embedded apps in hom

Hough lines and canny edge, Sobel derivatives Opencv Tutorial

Hough lines, Canny edges and Sobel derivatives HoughLines, Canny edges, for OpenCV line detection  edges detection in symple described C++ code, where all the steps are visualize and exmplayn. In this tutorial is used Visual studio 2015 instalation by nuget packages. Easy and fast without usual problems with version, dll, and environmental vatiables. Check this tutorial  here Sobel derivatives Sobel derivatives is convolution of image parts with kernel that represent sobel derivative approximation. The upper image is our sobel kernel. Simple 3 x 3 matrices with this parameters. This configurations can detect edges or changes which is vertically oriented. How?  Convolution of source image 3x3 part with this kernel generates a number. Kernel Convolution wiki Use this kernel with  3x3 image part 1. This image matrices has constant values 1. There is no edges in x direction. Number generates by convolution is 0. If you convolve kernel with image part 2. There is edge

Car NVIDIA deep Learning Platform. CES 2016

NVIDIA DRIVE PX2, deep learning for automotive  Capabilities  NVIDIA DRIVE™ PX 2 NVidia launched NVIDIA DRIVE™ PX 2 platform for in car AI deep learning. This processor is capable to    understand  data from 4 x lidar detectors, 4 x fisheye cameras, 2 x narrow field cameras and GPS in real time. This NVIDIA's advanced GPUs are intended to handle 360 degree situations around the car from large amount of sensors data. As official sources remark, The Drive PX2 provides super power equivalent to 150 MacBook Pros, which is really something impressive. DRIVE™ PX 2 specifications Platform is based on two next-generation Tegra processors and two special discrete GPU Pascal architecture, which is optimized for deep learning math acceleration up to 24 trillion operation over neural net per second. This power is capable to handle data from all the sensors at the same time in real time critical applications. Those 24 teraflops power can handle surround 365 view, pedestrian d

Opencv 3.1 Visual Studio 2015 support

Opencv 3.1 Visual studio 2015 Share this for more tutorials and computer vision post from me.. Thanks best Vladimir Opencv 3.1 just released with prebuild VC14 libs for Visual Studio 2015 . Installation is easy and you can follow standard tutorials on Quick start with Visual Studio 2015 Opencv 3.1 This process is exatly same as in other version with prebuild libs.  Install opencv 3.1 in visual studio Shortened procedure summary Download opencv 3.1 for windows extract to folder like c:/opencv Set enviroment varibales by setx -m This depends on your path setx -m OPENCV_DIR C:\opencv\build\x64\vc14      4. In Path editor just set  %OPENCV_DIR%\bin This is important steps. If you include Opencv project without this Project in visual studio failed because the project can not find DLL library.   Set project in Visual Studio 2015 Just create your new opencv project in Visual Studio 2015 and follow this steps.  Under