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The NVIDIA CUDA language extension has been designed for high-performance general-purpose parallel computing on recent GPUs and enables the developers to easily implement parallel programs. This package consists of a set of CUDA kernels, Visual Profiler and Visual Profiler GUI that allow you to analyze and optimize CUDA applications.
The NVIDIA CUDA development environment is essential for professional C/C++ programming and is available for Windows, Linux and OS X. Other software is developed by NVIDIA to optimize and compile CUDA software.

Test Toolkit for Image Processing and Computer Vision (TTIP) is a toolkit and a collection of C++ classes and simple OpenCV functions used to create and test image processing and computer vision algorithms.
It contains classes for Fast Feature Detection using an alternate detector to the SURF detector and a class for the implementation of landmark detectors, both named Region Proposal. It also offers classes for Image Viewers: Image Viewer, Image Viewer with Sequential Viewing, Image Viewer with Gallery Viewing and Image Viewer with Zoom.
Another set of classes is provided for Image Processing: Image Filter, Image Subtraction, FFT, Image Histogram, Median, Morphological Image Processing, Image Quotient and Canny Edge Detector.
In addition, TTIP includes 11 OpenCV functions for feature extraction, filtering and image processing. These are: DetectKeypoints, DetectDescriptors, WriteImage, WriteImageToFile, ReadImage, InRange, GaussianBlur, Mask, Median, Moments, Scale, Whitening.

The OpenCV C++ library is the first library of OpenCV components written in C++. This project offers a set of powerful OpenCV functions and classes for 2D/3D image processing.
It has support for various image formats including: BMP, JPG, JPEG, PNG, TGA, TIFF, GIF, PCX, PS, IFF, STL, FLIF, BABEL, TVP, AVI, MOV, M4V, WMV, MKV, FLV, SWF, MP4, OGG, OSA, TARGA and VP6.
The OpenCV C++ library comes with a set of ready-to-use classes, functions and algorithms, as well as with a book that describes these components.

The openCV_c C++ template library is part of the OpenCV image processing library. OpenCV_c offers a set of powerful

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The name Simd has been chosen to refer to SIMD, which is the short name for Single Instruction, Multiple Data. This name refers to SIMD extension CPUs that provide single-instruction, multiple-data instructions such as SSE3 (single-precision floating-point), SSE4.1 (4-integer data) and SSE4.2 (4-integer and 3-fraction data).
SIMD extension CPUs can perform well-optimized 32-bit or 64-bit floating-point arithmetic and unrolling (forming a loop over a vector rather than loading and storing each element separately). This allows SIMD extension CPUs to perform faster operations than using single-core processors.
The Simd project was originally started in 1999 by Dmitry Khovratovich, and is now maintained by Sergay Amashukevich. At first, Simd was a collection of just a few images and image processing algorithms and it was intended primarily for use by experienced developers. Over time, the project expanded and support for more image formats was added. Simd is now offering a wide range of operations that can help programmers with image processing, and includes over 100 algorithms.
The Simd is now one of the more advanced image processing libraries for C and C++, and its code is optimized for CPUs with SIMD extensions, such as the Intel and AMD SSE, SSE2, SSE3, SSE4.1, SSE4.2, SSSE3, AVX and AVX2.
The original intention of Simd was to allow people without experience in image processing and programming to use the library with ease, but it can also be used by experienced programmers to enhance their software. With Simd, you can easily create powerful software with image processing.
With Simd, you can process images using the SSE, SSE2, SSE3, SSE4.1, SSE4.2, SSSE3, AVX, AVX2, and NEON extensions in order to perform faster image processing than with a single-core CPU.
Simd is currently targeting x86/x64 and PowerPC processors, as well as ARM processors. However, the code for Simd has been ported to the following processors:
Support for:
Intel Core i3 (Ivy Bridge),
Intel Core i5 (Sandy Bridge),
Intel Core i7 (Ivy Bridge),
Intel Core i5 (Sandy Bridge),


– «Simd» is a C++ open-source image processing library created to help C and C++ programmers. It includes a number of high-performance image processing algorithms that are optimized using various SIMD CPU extensions.
– Naturally, this package is only intended to be used by experienced developers who wish to use the library in order to enhance their software.
– «Simd» comes equipped with numerous algorithms indented to help with various operations related to image processing, such as pixel format conversion, extraction of statistic information from pictures, image scaling and filtration, motion detection, object detection classification and neural network.
– The included algorithms have been optimized using several SIMD CPU extensions. To be more specific, the library offers support for the following CPU extensions: SSE, SSE2, SSE3, SSE4.1, SSE4.2, SSSE3, AVX and AVX2 (for x86/x64), VMX (Altivec) and VSX (Power7) for PowerPC, as well as NEON for ARM.
– Simd offers helpful C++ classes and functions in order to facilitate access to the C API. The library also offers support for dynamic and static linking.
– The downloadable package incorporates all the components required by software developers, and users can also take advantage of the documentation that is included in the archive. It contains descriptions of all the classes and functions of the Simd library.
– «Simd» supports the following Simd API documentation
– «Simd.Status»: These functions return the status of Simd objects. They can be used to verify that a given object has been successfully initialized. Some functions return some of the Simd status codes that can be found below.
– «Simd.Status.OK»
Simd status code: The operation was successful.
Simd status code: The Simd object does not meet the alignment requirement. Alignment can be done only when using the `bytes` object. The relevant function is `bytes::SetAlignment`.
– «Simd.Status.SIMD_FILL_NON_POWER2_IEEE754»
Simd status code: The Simd object is not fully IEEE754 compliant. According to this status code, the SIMD values are not the IEE754 normalized. To view the status code functions, please

What’s New in the Simd?

Simd is a high-performance image processing library that offers programmers an easy access to various image processing algorithms through its “C” API. Simd uses the SIMD extensions of modern x86/x64 and PowerPC/Power8 CPUs to increase performance, especially when dealing with data that are organized in a set of consecutive planes (e.g. color channels, x- and y-axes, or even individual RGB or RGBA pixels).
Simd is already easy to use: once it is installed in the project, a single initialization is enough to start using the library. One can also directly include the Simd headers in order to access the C API from within C++ programs.
Simd is fully portable: it can be compiled and linked on any operating system that supports the x86/x64 and PowerPC/Power8 platforms and offers C and C++ compilers. Simd is actually the official build system of the Renoise music editor.
Simd is also free: it is available under the GNU General Public License version 3.
Other features of Simd:
● The Simd API is widely documented;
● The library provides a decent C++ class for access to the C API through a C++ compiler;
● Since the library is intended to be portable, it can be used on any operating system that offers SIMD extensions;
● Simd provides performance boost to just about any application that processes image data;
● Simd comes with both a 32-bit and a 64-bit versions;
● The “libsimd.a” archive contains everything that is necessary for building a static or dynamic library or the main application.
Components of Simd:
* SimdC.h – Main header file that defines the SimdC interface
* SimdFwd.h – Forward declarations for C++ classes and functions
* – Configuration file for the C++ compiler, which ensures that all the errors of C++98 are addressed.
* SimdC_sse.h – SIMD implementation for SSE (X86/x64)
* SimdC_sse2.h – SIMD implementation for SSE2 (X86/x64)
* SimdC_sse3.h – SIMD implementation for SSE3 (X86/x64)
* SimdC_sse4.h – SIMD implementation for SSE4

System Requirements:

OS: 64-bit Windows 8.1, Windows 10 (32-bit and 64-bit)
Processor: Intel Core i3-2100, 3.2 GHz or AMD Phenom II X4 955 Processor or equivalent
Memory: 4 GB RAM
Storage: 1 GB available space
Video Card: Intel HD Graphics 4000, NVIDIA GeForce 8600 GT or AMD Radeon HD 7870 or equivalent
Additional Notes: See Software Requirements
OS: 64-bit Windows 8