Smooth the image with a gaussian filter to remove noise. Find gradients with max intensity (above a certain threshold). Apply non-maximum suppression to get rid of spurious response to edge detection (by finding the peaks in the gradients detected above the certain threshold, so it basically is the maximum ofContinue Reading
First when you just have 1D and want to find edges such as just a vertical or horizontal scanning, it’s easy to take the second derivative times the kernel and times the image: ∂²/∂x² * h(kernel) *f(image) for horizontal or ∂²/∂y² * h(kernel) * f(image) for vertical. Then just find aContinue Reading
An image gradient is a(or the most) directional change in the intensity or color in an image. * Canny edge detector uses image gradient for edge detection. The gradient of a two-variable function (here the image intensity function) at each image point is a 2D vector with the components givenContinue Reading
Get the supervised dataset (containing the image features and their labels). Validate the data – by size , by shape, by error data , by premature EOF, by Null type, by object type, by out of range of enumerated values, by business logic. Visualize the data by plotting it. CreateContinue Reading
The basic steps to detect a line in a frame are: 1. Turning the frame to grayscale via color maping conversion. 2. Bluring(A.K.A Smoothing) the frame via a Median Kernel. 3. Detecting edges on the frame via derivatives and comparing the result(gradient) which are highest above a threshold or thatContinue Reading
Principal Component Analysis – PCA is the process of projecting variance of data using minimal dimensions. The direction of dimension along which the variance is maximised(highest) will be chosen as the first principal component. The next best direction as the second P.C. and should be orthogonal to the first P.C,Continue Reading
Auto Encoders try to find the relations of the input with itself. These relations able the Auto Encoder algorithm to reproduce the same input via the relations it found. Auto Encoders – try to find the identity function f(x) using the latent factors of the data via f(x) = g(L)Continue Reading
In image processing, a kernel, convolution matrix, or mask is a small matrix (usually 3X3 but can be any matrix size). It is used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between a kernel and an image. Convolution is termed as theContinue Reading
Non-maximum suppression is used in CV in object detection where multiple probabilities that are above the given threshold are detected(classification result). Thus non-maximum suppression can help to suppress all the gradient values (by setting them to 0) except the local maxima classification result. If the strength of the current pixel/elemntContinue Reading
Similarity Measure The Jaccard index/coefficient, aka Intersection over Union is used in computer vision as one of the similarity measures for object detection on images. Formula The Jaccard similarity coefficient,aka Intersection over Union (IOU) score: Binary classification Formula The same score above can be described as binary classification as well to get anContinue Reading
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