# Tag Info

32

My recommendation in terms of text books is Rick Lyons's Understanding DSP. My review of the latest edition is here. I, and many others from the ${\tt comp.dsp}$ community and elsewhere, have helped Rick revise parts of the text since the first edition. For self-study, I know of no better book. As an on-line, free resource, I recommend Steve Smith's ...

18

Paul Falstad's Java applets are a fantastic way to interact with systems and learn them intuitively. The Digital Filter applet is a revelation. Check out the rest at http://www.falstad.com/mathphysics.html. For a more informal introduction, I like A Digital Signal Processing Primer by Ken Steiglitz, which is exactly what it says it is. I TAed a class ...

17

The below three are the best referred Text books on this subject. Discrete-Time Signal Processing, Prentice-Hall Signal Processing Series by Alan V. Oppenheim, Ronald W. Schafer, John R. Buck. Digital Signal Processing: Principles, Algorithms and Applications, Prentice Hall John G. Proakis, Dimitris K Manolakis Signals and Systems, Prentice Hall Alan V. ...

13

I personally believe that the approach of studying something just in order to "feel ready" to study something else later on is not efficient and tends to overwhelm the student. (This of course only applies because I assume that you know what a computer scientist should know anyway: basic math, and - most importantly - how to think clearly and logically.) I ...

12

I believe Gonzalez and Woods are competent enough to rely on their opinion: There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a ...

12

It depends how you define the term "information" or "entropy". The conventional definition of entropy of an image is to think an image as a two dimensional matrix of pixel and $$H = - \sum_k p_k \log_2(p_k)$$ where $p_k$ is the probability, which is calculated from histogram, associated with gray level $k$. This kind of entropy is correct if we ignore the ...

10

For theoretical studies, Oppenheim is the god but if you're going to use it in practice, Mitra is one of the best: Digital Signal Processing: A Computer-Based Approach, Sanjit K. Mitra

10

The way I understand it, the objective of image processing is to get a (somehow transformed) image. The objective of computer vision is to find out something about the things in the image (like is the guy on the picture happy or sad, how many cars are there in the image and which way are they driving). solely (or mostly) Computer vision tools and ideas I ...

10

Tips for DSP self-study huh. Well, ...studying 'signals and systems' is a great idea and having Matlab software means you have the tools to learn an awful lot. I think Dr. Steven Smith's book "The Scientist and Engineer's Guide to Digital Signal Processing", which you can read online for free, is a terrific source of fundamental DSP information. Dr. Smith is ...

9

The two channels exist only inside a transmitter or a receiver; the channels are physically combined in a single signal (or channel) in the physical medium (wire, coax cable, free space, etc). At the transmitter, two signals $s_I(t)$ and $s_Q(t)$ (called the I (or inphase) signal and Q (or quadrature) signal respectively) are combined into a single signal $... 8 If I draw a number uniformly between zero and one, what is the probability that they are equal? Mathematically, it should be zero but I don't recall why? Can somebody please help in explaining why the probability should be zero? Avoiding formal definitions of (Lebesgue) probability measure, an informal way is thinking the probability at a point of a ... 7 Sometimes there are courses entitled 'statistical signal processing', that's a good place to start :-) If your university doesn't have this, try looking for 'detection and estimation', or 'advanced signal processing'. If you don't have a university handy, you could try http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-432-stochastic-... 7 You can visit the MIT OpenCourseWare. A set of 20 video lectures by professor Alan V. Oppenheim. 6 Here's the problem. With an opaque learning algorithm, you have to figure out if your algorithm has really learned something about some deeper structure common to the desired problem area (assuming there is some to be found), or had just learned to recognize some particular inputs and spit out the desired answer only for those inputs (similar to school ... 6 When you use machine learning algorithms on data sets, you use one part of the data (the training set) to train your algorithm (i.e., feature extraction). Once the training is completed, you'll need to evaluate the performance of the trained algorithm and you do this by applying it to new data, that is, the second part of your original data (the test data). ... 5 Film isn't absolutely "analog", as in continuous. Every individual silver halide molecule, after exposure and development, is either metalized or not; and there are a finite number of these molecules in every frame of film, thus quantizing the exposure measurement. However the density and location of the film grains and silver halide molecules is semi-... 5 These classical references are a good start: B. Porat, Digital Processing of Random Signals, Prentice-Hall, 1994. Library serial number 2144342. A. Papoulis, Probability, Random Variables and Stochastic Processes, 3rd Ed. , McGraw-Hill, 1991. Library serial number 21111643. S. M. Kay, Fundamentals of Statistical Signal Processing, Volume I: Estimation ... 5 In addition to the already mentioned books, if you are focused towards algorithm development, Proakis' Digital Signal Processing using MATLAB is an excellent resource for starters. The numerical recipes series is also an excellent resource regarding how to implement some core DSP algorithms (spectral decomposition, convolutions, interpolation and ... 5 Extending a bit on the answer of @Rob. If you are writing a paper in LaTeX (and friends), it is (imho) highly desirable to draw the diagrams also in a LaTeX related way. This comes with a number of advantages: Direct and easy integration, using either \input or including as PDF using \includegraphics The same fonts, font sizes, colors, etc. as in the rest ... 4 Hi, Assuming you are interested in doing research in the field, I will advice following a path built on a strong foundations in mathematics. I know this, beacuse I just have finished teaching a course in Estimation & Detection and I can assure you that there is a strong correlation between the quality and novelty of the work and your knowledge of math. ... 3 It is not necessary that your device's color mismatch can be corrected with a simple shifting of the Hue component (except of course if you are absolutely sure that this is the case with your equipment). In general, to correct the response of the device you somehow need to recover the mapping that it already performs between reference and observed colors ... 3 I believe it would if you intend to practice your knowledge, rather than go into academia. I suggest you look into the code base of opencv, simplecv, and scikit-image, as suggested. Once you become comfortable with using them, you can submit patches for bugs and new algorithms. Nevertheless, you will also need to understand what you are doing at a ... 3 Yes there is. There definetely is there such a thing called DSP Engineer, but it is not exactly what the article described, i think it even goes more high level. Nowadays, most companies relies on already established Knowledge such as standards, or papers from IEEE or AES, or such places. So a DSP engineer requires now more than ever, a lot of knowledge ... 3 I would add to the list the book "Digital Filters", by Richard Hamming. A short classic, rather than a heavy tome. 3 I found this applet very helpful when understanding the nature of convolution in time. The Joy of Convolution. It lets you "draw" your time signals and convolve them so you get a picture of what's happening in the time domain. 3 The DSP neophyte who has some mathematical maturity may want to start with Martin Vetterli, Jelena Kovačević, Vivek Goyal, Foundations of Signal Processing, 2014. which is freely available online. The authors have also made their two other books freely available online: Jelena Kovačević, Vivek Goyal, Martin Vetterli, Fourier and Wavelet Signal Processing, ... 3 I've been using the Microsoft Visio drawing software (version: 2003) for some years now. It works pretty well. (Like all Microsoft products it has 2000 special "features" that I don't need so it took quite a while to learn how to create simple drawings.) I see that the current price for the 'Standard' version of Visio is now$300. That's awfully expensive! ...

3

I've been making such line drawings in OpenOffice Draw. It has EPS and PDF export but I never tried the output files with Latex

3

You can start with the Compressive Imaging Code code by J. Romberg, illustrating the paper "Imaging via Compressive Sampling". Another great source of information and codes is on Nuit Blanche.

2

https://www.amazon.com/dp/B01MS8W9XI This book will go through different projects that will teach the reader how to write software: to improve their singing, synthesize different guitar sounds, change the human brainwave, break glass, help people to relax and learn about many different sound engineering and DSP tools : DFT, FFT, High pass filter, low pass ...

Only top voted, non community-wiki answers of a minimum length are eligible