Computer Vision and Image Recognition algorithms for R users

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useR!2017: Computer Vision and Image Recognition al...

**Keywords**: Computer Vision, Image recognition, Object detection, Image feature engineering

**Webpages**: https://github.com/bnosac/image

R has already quite some packages for image processing, namely [magick](https://CRAN.R-project.org/package=magick), [imager](https://CRAN.R-project.org/package=imager), [EBImage](https://bioconductor.org/packages/EBImage) and [OpenImageR](https://CRAN.R-project.org/package=OpenImageR).

The field of image processing is rapidly evolving with new algorithms and techniques quickly popping up from Learning and Detection, to Denoising, Segmentation and Edges, Image Comparison and Deep Learning.

In order to complement these existing packages with new algorithms, we implemented a number of *R* packages. Some of these packages have been released on https://github.com/bnosac/image, namely:

- **image.CornerDetectionF9**: FAST-9 corner detection for images (license: BSD-2).
- **image.LineSegmentDetector**: Line Segment Detector (LSD) for images (license: AGPL-3).
- **image.ContourDetector**: Unsupervised Smooth Contour Line Detection for images (license: AGPL-3).
- **image.CannyEdges**: Canny Edge Detector for Images (license: GPL-3).
- **image.dlib**: Speeded up robust features (SURF) and histogram of oriented gradients (HOG) features (license: AGPL-3).
- **image.darknet**: Image classification using darknet with deep learning models AlexNet, Darknet, VGG-16, Extraction (GoogleNet) and Darknet19. As well object detection using the state-of-the art YOLO detection system (license: MIT).
- **dlib**: dlib: Allow Access to the 'Dlib' C++ Library (license: BSL-1.0)

More packages and extensions will be released in due course.

In this talk, we provide an overview of these newly developed packages and the new computer vision algorithms made accessible for R users.

![](https://github.com/bnosac/image/raw/master/logo-image.png)

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