Aspose.Imaging.Accord.Adapter 24.11.0

Aspose.Imaging.Accord.Adapter for .NET

Aspose.Imaging.Accord.Adapter is designed to extend the functionality of the Aspose.Imaging library by adding functions from FileFormat.Accord library. This association allows all raster formats supported by the Aspose.Imaging library to be covered by popular methods from the FileFormat.Accord library. The following methods from the FileFormat.Aссord library are applicable to all raster formats of the Aspose.Imaging library:

Computer vision algorithms and object boundary detection:

• FastCornersDetector (In the FAST corner detection algorithm, a pixel is defined as a corner if (in a circle surrounding the pixel), N or more contiguous pixels are all significantly brighter then or all significantly darker than the center pixel. The ordering of questions used to classify a pixel is learned using the ID3 algorithm. This detector has been shown to exhibit a high degree of repeatability. The code is roughly based on the 9 valued FAST corner detection algorithm implementation in C by Edward Rosten, which has been published under a 3-clause BSD license and is freely available at: http://svr-www.eng.cam.ac.uk/~er258/work/fast.html.)
• HarrisCornersDetector (this algorithm implements the Harris corners detector.)
• FastRetinaKeypointDetector (The FREAK algorithm is a binary based interest point descriptor algorithm that relies in another corner)
• SpeededUpRobustFeaturesDetector (Based on original implementation in the OpenSURF computer vision library by Christopher Evans (http://www.chrisevansdev.com). Used under the LGPL with permission of the original author. Be aware that the SURF algorithm is a patented algorithm by Anael Orlinski. If you plan to use it in a commercial application, you may have to acquire a license from the patent holder.)
• Haralick (Haralick's texture features are based on measures derived from Gray-level Co-occurrence matrices (GLCM).)
• HistogramsOfOrientedGradients (Navneet Dalal and Bill Triggs, "Histograms of Oriented Gradients for Human Detection", CVPR 2005. Available at: http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf)
• GrayLevelCooccurrenceMatrix (A co-occurrence matrix or co-occurrence distribution is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset.)
• FindContour(Border following algorithm for contour extraction.)

Joint representation of both Integral Image and Squared Integral Image:

• GetIntegralImage(This algorithm provides a unified representation for both integral images, squared integral images and tilted integral images under the same class. This class can be used to provide more efficient transformations whenever all those representations are required at the same time, such as when using the Viola-Jones (Haar Cascade) object detector.)

Image filtering algorithms:

• DifferenceOfGaussians (In imaging science, the difference of Gaussians is a feature enhancement algorithm that involves the subtraction of one blurred version of an original image from another, less blurred version of the original.),
• GaborFilter (In image processing, a Gabor filter, named after Dennis Gabor, is a linear filter used for edge detection. Frequency and orientation representations of Gabor filters are similar to those of the human visual system, and they have been found to be particularly appropriate for texture representation and discrimination. In the spatial domain, a 2D Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. The Gabor filters are self-similar: all filters can be generated from one mother wavelet by dilation and rotation.),
• NiblackThreshold (The Niblack filter is a local thresholding algorithm that separates white and black pixels given the local mean and standard deviation for the current window.),
• SaulovaThreshold (The Sauvola filter is a variation of the NiblackThreshold thresholding filter.)

More details

Platform dependence

Aspose.Imaging.Accord.Adapter for .NET can be used to develop applications on Windows Desktop (x86, x64), Windows Server (x86, x64), Windows Azure, Windows Embedded (CE 6.0 R2), as well as Linux x64. The supported platforms include .Net7.0, .Net8.0.

New Features & Enhancements in Version 24.11

New project

Getting Started with Aspose.Imaging.Accord.Adapter for .NET

Are you ready to give Aspose.Imaging.Accord.Adapter for .NET a try? Simply execute

Install-Package Aspose.Imaging.Accord.Adapter

from Package Manager Console in Visual Studio to fetch the NuGet package. If you already have Aspose.Imaging.Accord.Adapter for .NET and want to upgrade the version, please execute

Update-Package Aspose.Imaging.Accord.Adapter

to get the latest version.

Product dependencies

This product is dependent on :

• Aspose.Imaging .NET is distributed under Aspose EULA license;
• FileFormat.Accord is distributed under LGPL license

Usage example

Corner Detector

Aspose.Imaging.License imagingLic = new License();
imagingLic.SetLicense("license.lic");

using (var image = (RasterImage)Image.Load("polygons.cdr.png"))
{
	//detect corners
    var points = image.CornerDetector(image.Bounds, new FastCornersDetectorProperties());
    foreach (var point in points)
    {
        Console.WriteLine(point);
    }
}

Fast Retina Keypoint Detector

Aspose.Imaging.License imagingLic = new License();
imagingLic.SetLicense("license.lic");

using (var image = (RasterImage)Image.Load("polygons.cdr.png"))
{
    //detect keypoints
    var points = image.FastRetinaKeypointDetector(image.Bounds, new HarrisCornersDetectorProperties());
    Console.WriteLine(points.Count);
    Console.WriteLine(points[0].ToBase64());
}

Feature extractor

Aspose.Imaging.License imagingLic = new License();
imagingLic.SetLicense("license.lic");

using (var image = (RasterImage)Image.Load("polygons.cdr.png"))
{
    //detect features
    var descriptors = image.FeatureExtracor(image.Bounds, new HistogramsOfOrientedGradientsProperties());
    foreach (var descriptor in descriptors)
    {
        Console.WriteLine(descriptor);   
    }
}
 

Apply filter

Aspose.Imaging.License imagingLic = new License();
imagingLic.SetLicense("license.lic");

using (var image = (RasterImage)Image.Load("lena.jpg"))
{
    //Apply filter
    image.ApplyFilter(image.Bounds, new NiblackThresholdProperties());
    image.Save("lena_filtered.jpg");
}