![]() ![]() However, verifying that the mask is of reasonable quality can be done in about 10 seconds. It takes about 5 minutes to manually create the mask on the right, which makes training data labeling very expensive. The approaches outlined in this article can be leveraged and adapted to generate training data for any image segmentation task.Įxample of a satellite image of a golf course (left) with a human-created mask in orange denoting the non-playable area (right). Specifically, we explore the use of thresholding methods in Python and OpenCV to segment the playable area on a golf course given a satellite image. Instead of requiring a human annotator to spend many minutes creating the segmentation masks, our technique enables the annotator to verify the segmentation masks in a matter of seconds. In the remainder of this article, we demonstrate the use of traditional computer vision techniques for pre-labeling training data for image segmentation tasks. Moreover, errors in the annotation process add noise to the labeled dataset and hurt the overall performance of the models. ![]() This annotation process was a major blocker for Arccos: the time expense to create precise masks led to small training sets for their playable-area detector. Compared to assigning tags for image classification or drawing bounding boxes for object detection, the process of creating training data for image segmentation is very time-consuming and prone to annotator error. To create training data for image segmentation tasks, complex shapes in images must be precisely outlined. Machine learning image cleaner full#The approach we developed applies to any image segmentation task that aims to identify a subset of visually distinct pixels in an image.Īs opposed to image classification – i.e., “what is this image?” – and object detection – i.e., “where are the bounding boxes of objects in this image?” – image segmentation is the task of finding the full complex shape of objects in a picture – i.e., “what is the exact pixel mask of objects in this image?”Ĭomparison of the three main image-based machine learning tasks: image classification (left), object detection (center) and image segmentation (right). If the caddie knows that there is a tree or some other non-playable obstacle between the golfer and the green (A and B), it can suggest an alternate path to the green.īy making more training data available, we generalized Arccos’ deep learning model and thus improved the virtual caddie’s performance. Screenshot of Arccos’ virtual caddie application. Instead, our technique enables annotators to label training data for image segmentation algorithms rapidly. The auto-labeling technique introduced in this article eliminates the need to painstakingly hand-annotate every pixel of interest in an image. That’s where our team came in: in March 2018 we partnered with Arccos to develop a method for rapidly pre-labeling training data for image segmentation models. Understanding course layout at the necessary granularity requires sophisticated image segmentation, built on deep learning techniques over vast amounts of training data. ![]() To deliver all this insight, the Arccos virtual caddie needed to understand the playable and non-playable areas of a golf course. Last year, they gifted golfers a new ace in the hole – their own “virtual caddie” app, powered by Microsoft’s cloud and machine learning.Īccompanying you on the links and crunching data collected on your personal swing history, the 61 million shots hit by other Arccos users, and 386 million geotagged data points from 40,000 courses, the Arccos virtual caddie provides sage advice on each shot, just like a real caddie. Partnering with Microsoft, golf performance tracking startup Arccos has set out to use artificial intelligence and machine learning technology to build apps that help golfers step up their game. ![]()
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