Computer Vision News - October 2016

12 Computer Vision News Tool The image and the blur image are displayed side-by-side since we define those two under a “Row” object. Now comes the fun part :) including any change that you’ve made to the code, either the sigmaX, sigmaY or the ksize, or even specifying a different file to load. The effect of those changes will automatically appear on screen to let you quickly learn, configure and interact with the algorithms and their parameters. Let’s see another quick example: the canny edge detector. As in the GaussianFilter, the parameters are the same as in the OpenCV. The input to the Canny is bound to the ImRead. Note that here we define the filename as a properties string and not inside the ImRead section. As before changing any of the Canny parameters (i.e. thresholds), the result will immediately be reflected on screen. This lets you quickly evaluate whether edges can be extracted from the image, which edges will be easy to extract, which are subtler and so on. More elaborate examples include object recognition through feature detection. We won’t go into all the details but will mention the most important ones. In general, Image feature matching is a process that includes (1) finding features in the image, (2) describing those features, (3) comparing those descriptors between images to find similar images or to detect objects in images etc. Each step in this pipeline may involve many variants of algorithms, each with its own parameters. Any of those can be evaluated with LiveCV and, as before, the result is visible on screen as soon as the change is made. Tool “ Code less. Create more. Deploy everywhere. ”