Computer Vision Library
OpenCV
Visual data extraction
As opposed to deep learning, OpenCV algorithms can be applied in cases where few - or even no - labeled data is available. Our previous OpenCV projects included: - Spotting differences between electrical equipment configurations for compliance purposes, - Pre-processing noisy images to feed our deep learning neural network in a food recognition app to execute automatic detection and billing of the menus.
OpenCV, which stands for Open Source Computer Vision, provides multiple algorithms to extract information from images. Several open OpenCV algorithms don't rely on machine learning and require few, or even no, labeled data to be efficient. Therefore, results are visible from the first days of our projects.
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4.1
Current version
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Launch
Some Figures
OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China (...) to inspecting labels on products in factories around the world on to rapid face detection in Japan.
OpenCV algorithms can be applied to cases where few or no labeled data is available
Feature Matching
OpenCV algorithms can be applied to cases where few or no labeled data is available
Feature Matching
OpenCV algorithms can characterize an object using keypoints, also known as descriptors. These descriptors can be specific angles or color variations. Using these keypoints, it becomes easier to find whether an object is present in any given image.
Pre-processing the data
Keeping only the most valuable information
Pre-processing the data
Keeping only the most valuable information
Before applying deep learning tools to your data, an OpenCV pre-processing can clear it from noisy information. Consequently, only the most valuable part of your data will feed the deep learning pipeline, boosting the performance of the solution. Edge detection is one of the most widely used pre-processing methods available in OpenCV and can be applied to live videos.
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