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by Dr. Nathan Faggian
Python is a great language for prototyping computer vision algorithms, the availability of libraries such as Numpy and Scipy make for rapid development similar to that of Matlab, R and IDL. At the Bureau of Meteorology (BoM) we are solving the interesting problem of weather field warping. Warping (aka non-linear image registration) is used, for example, to determine what the predicted temperature will be hourly if we only have predictions every three hours. Practically, warps (weather advection fields) are estimated using salient image features or data-driven numerical optimizations and this presentation will demonstrate progress we are making on both fronts.
This is Part 1 of a showcase in solving a variety of interesting scientific and engineering problems using Python, NumPy, and SciPy, and producing beautiful plots with Matplotlib.
The focus is on the "wow" factor: "Wow! You can do all that in just a few lines of Python?!"
The topics discussed are:
- speech and image recognition (how to be head-hunted by Google)
- bioinformatics (how to cure cancer)
- statistical modelling (how to predict the stock market)
The intended audience is graduates in science and engineering disciplines; Python users who want to know more about what's possible with tools like NumPy and SciPy; and users of high-level packages like Matlab or R and low-level languages like Fortran who want to get more interesting work done with less effort.
This is Part 2 of a showcase in solving a variety of interesting scientific and engineering problems using Python, NumPy, and SciPy, and producing beautiful plots with Matplotlib.
The focus is on the "wow" factor: "Wow! You can do all that in just a few lines of Python?!"
Sample topics discussed include:
- speech and image recognition (how to be head-hunted by Google)
- bioinformatics (how to cure cancer)
- statistical modelling (how to predict the stock market)
The intended audience is graduates in science and engineering disciplines; Python users who want to know more about what's possible with tools like NumPy and SciPy; and users of high-level packages like Matlab or R and low-level languages like Fortran who want to get more interesting work done with less effort.