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## I'm searching for Computer Vision: A Modern Approach (2nd Edition) pdf?

Start with. (1) Signals and Systems (2nd Edition). Alan V. Oppenheim, Alan S. Willsky, with S. Hamid. 9780138147570. Amazon.com. Books and then. (2) Two-Dimensional Signal and Image Processing. Jae S. Lim. 9780139353222. Amazon.com. Books and then. (3) Robot Vision (MIT Electrical Engineering and Computer Science). Berthold K.P. Horn. 9780262081597. Amazon.com. Books and then any one of these. Computer Vision. A Modern Approach (2nd Edition). David A. Forsyth, Jean Ponce. 9780136085928. Amazon.com. Books Computer Vision. Algorithms and Applications (Texts in Computer Science). Richard Szeliski. 9781848829343. Amazon.com. Books (or the kazillion other books that are roughly the same) And for optional reading. (4) Vision Science. Photons to Phenomenology. 9780262161831. Medicine & Health Science Books @ Amazon.com (5) Vision. A Computational Investigation into the Human Representation and Processing of Visual Information, David Marr, Tomaso A. Poggio, Shimon Ullman, eBook - Amazon.com (and other books on topics like deep learning, pattern matching, etc.) And please, please do this last (or in parallel), but don't only do this. Amazon.com. Learning OpenCV. Computer Vision with the OpenCV Library (9780596516130). Gary Bradski, Adrian Kaehler. Books Now that you're thoroughly bored. Hear me out. This is pretty much the electrical engineering and vision science path to computer vision. An undergraduate who wants to learn computer vision would usually take classes along this line. These things basically consists of 5 main things. - signal and image processing theory - physics based computer vision (1st principle approaches) - conventional computer vision (how hack algorithms together) - perception and biological vision (vision science) - lab work (opencv; how to hack code together) And the reason is because to really understand and be able to construct novel algorithms, you have to understand the underlying theory and work that's been done before. For example, this paper Spatiotemporal energy models for the perception of motion proposes something like this (I've modified it a bit to simplify things). h_1(x,y,t) = sin((u \cdot x + v \cdot y) \cdot f_s + t \cdot f_t + \phi) h_2(x,y,t) = sin((u \cdot x + v \cdot y) \cdot f_s + t \cdot f_t + \frac{\pi}{2} + \phi) y(t) = [(i \otimes h_1)(t)]^2 + [(i \otimes h_2)(t)]^2 for video input i(x,y,t) unit vector (u,v). y(t) is the extracted global motion in the direction of (u,v) at temporal and spatial frequencies f_t and f_s, respectively. And we can modify our h to get. g_j(x,y,t) = h_j(x,y,t) \cdot n(\mu, u,v,\sigma_1,\sigma_2) where n(.) is 2D gaussian. And voila. We have representative kernels for the first layer of a video processing convolutional neural network after training. Now, how do we reconcile what we just discussed with this. Horn–Schunck method That's for you to figure out after reading those books. I just wanted to illustrate 1 line of thinking in computer vision all the way from the very simple energy model developed in the 80s to the very hot convolutional neural network that everyone talks about now. All of this begins with. Riesz representation theorem which is the mathematical statement saying that every linear system (in practice) can be uniquely represented by. y(t)=(x \otimes h)(t) = \int x(\tau)h(\tau - t) d\tau (1), (2), (3), (4), and (5) would give everything you need to get from just after the representation theorem to motion detection. Building on top of that to deep learning is trivial. And now we can implement all of this in OpenCV, but please don't start with OpenCV. And you can pick just about any other topic in computer vision (with the exception of maybe a handful of graph and levelset based methods). - stereopsis - shape matching - edge detection - HoG, face detection, etc. - and many others Just about touches on most of the theories in the books. My personal thinking is that computer vision is a marriage of math (real + functional analysis), physics, signal processing, vision science, computer science, and software engineering. Leaving any of the pieces out is depriving yourself of something very profound .) Let's face it, any high schooler can do this. src = imread( "my-image-file.png" ); kernel = Mat..ones( 5,5 , CV_32F )/ (float)(5*5); filter2D(src, dst, -1, kernel, Point(-1,-1), 0, BORDER_DEFAULT ); imshow( "my blurred image", dst ); Where's the fun in that...

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