Information from pixels

Editor's Notes

• #3: Aim is to give a basic intro to machine vision and some of the basic techniques using java Not going to talk about deep learning - plenty of other introductions to that Not going to talk about photogrammetry - very specialised subject
• #6: Google, Apple Facebook using machine vision to recognise faces to match photos in photo albums
• #14: - Additive moddel - red, green & blue light added together to produce different colours - creating colours unintuitive - device dependent, no guarantee colours will look same on different devices - sRGB standard colour space produced by HP & Microsoft in 1996 to allow reproduction - Should still use colour calibration for accurate results
• #15: - defines a colour using values for hue, lightness & saturation - often easier than RGB when creating colours - device dependent
• #16: - distances in other colour spaces don't correspond to how perceptually different colours look to humans - device independent (often used when converting from RGB to CMYK)
• #17: - Most machine vision operates on grayscale images - Sometimes colour can be useful if you know the colour and aren't able to train an object detector
• #19: http://docs.opencv.org/2.4/modules/core/doc/basic_structures.html#mat
• #20: Blurring the image smoothes it out and helps remove noise Using a sigma of zero causes it to be computed from the width & height
• #23: // erode to remove small specks // A foreground pixel in the input image will be kept only if ALL pixels inside the structuring element are > 0. Otherwise, the pixels are set to 0 (i.e. background). final Mat se = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(kernelSize, kernelSize)); Imgproc.erode(image, result, se, new Point(-1, -1), numIterations); // dilate to restore large areas and remove gaps // Dilations, just as an erosion, also utilize structuring elements — a center pixel p of the structuring element is set to white if ANY pixel in the structuring element is > 0. Imgproc.dilate(image, result, se, new Point(-1, -1), numIterations);
• #30: You may have heard the term convolutional neural networks in conjunction with deep learning. It’s a similar idea: convolutional layers apply a kernel or filter to the layer’s input. The difference is that the filter is learned as part of the training process. In CNNs a convolutional layer is typically followed by a pooling layer which allows a fixed output vector size and a degree of position independence.
• #42: The vertical scale is a log-scale so the drop of after even modest blurring is substantial
• #44: - line following at speed a classic robot competition - variants with obstacles/hurdles
• #52: Finding an object in an image done by sliding a window across the image checking whether the area under the window is an object of interest. Differences in scale handled by using a larger window and scaling down to the detector window size.
• #53: Each feature is a single value obtained by subtracting sum of pixels under white rectangle from sum of pixels under black rectangle.
• #59: Need a lot of images. I had 70 positive images and 1000 negative examples