Performing Image (The MIT Press)

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The psychology of color –of which you can learn more in this great article by Entrepreneur–proves they have a direct effect on human perception, and thus they are a very useful element in visual marketing and most of all in branding. Making sure the colors you use fit your brand is crucial for getting customers to like it, remember it, and choose it. In your marketing visual content, the use of color will help you define everything in them, from tone to goal to brand strengthening.

Just like an average blurring, Gaussian smoothing also uses a kernel of , where both and are odd integers. Figure 1: Having trouble configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University — you’ll be up and running with this tutorial in a matter of minutes. The end result is that our image is less blurred, but more “naturally blurred,” than using the average method discussed in the previous section. Furthermore, based on this weighting we’ll be able to preserve more of the edges in our image as compared to average smoothing. Thus far, the intention of our blurring methods have been to reduce noise and detail in an image; however, as a side effect we have tended to lose edges in the image. While adding augmentations to your images will make your model more robust to those types of effects, it also makes the training process more difficult for your model, so your model may take longer to train or may not achieve as high of an accuracy in training. Therefore, augmentations should be selected intentionally with regard to your use case.The network is not end-to-end trainable. We’re not actually “learning” to detect objects; we’re instead just taking ROIs and classifying them using a CNN trained for image classification. Now, take a look at the bottom-left where we have increased both and jointly. At this point we can really see the effects of bilateral filtering. WIDTH: Given that the selection of images/ for testing (refer to the “Project Structure” section) are all slightly different in size, we set a constant width here for later resizing purposes. By ensuring our images have a consistent starting width, we know that the image will fit on our screen. While this may sound counter-intuitive, by reducing the detail in an image we can more easily find objects that we are interested in. where and are the respective distances to the horizontal and vertical center of the kernel and is the standard deviation of the Gaussian kernel.

Object detection, on the other hand, not only tells us what is in the image (i.e., class label) but also where in the image the object is via bounding box (x, y)-coordinates ( Figure 1, right).Image enhancement is the process of bringing out and highlighting certain features of interest in an image that has been obscured. This can involve changing the brightness, contrast, etc. Image Restoration