Due to variations in lighting conditions, shadowing, etc., it may be that one value of T will work for a certain part of the input image but will utterly fail on a different segment.

The problem here is that having just one value of T may not suffice. However, both of these methods are global thresholding techniques, implying that the same value of T is used to test all pixels in the input image, thereby segmenting them into foreground and background. Otsu’s thresholding method can automatically determine the optimal value of T, assuming a bimodal distribution of pixel intensities in our input image.

When applying basic thresholding we had to manually supply a threshold value, T, to segment our foreground and our background. You can start by choosing your own datasets or using our PyimageSearch’s assorted library of useful datasets.īring data in any of 40+ formats to Roboflow, train using any state-of-the-art model architectures, deploy across multiple platforms (API, NVIDIA, browser, iOS, etc), and connect to applications or 3rd party tools. Sign up or Log in to your Roboflow account to access state of the art dataset libaries and revolutionize your computer vision pipeline. Roboflow has free tools for each stage of the computer vision pipeline that will streamline your workflows and supercharge your productivity. This method helps in segmenting images in different light conditions, and a good dataset enables us to see its efficacy. Last week, we learned how to apply both basic thresholding and Otsu thresholding using the cv2.threshold function.Ī diverse set of images, particularly those with varying lighting conditions, is crucial for understanding adaptive thresholding.

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