Cris’s Image Analysis Blog

Why are scientists so scared of writing their statements in the first person? Open any journal, and look for the word “I”. Chances are, you won’t find it. You’ll see article authors jump through hoops just to avoid this word. As if it were dirty, illegal. For example, instead of a normal, complete sentence like “I found existing methods to be insufficiently accurate,” you’ll find the sentence “Existing methods were found to be insufficiently accurate.” This leaves the most important thing out: who found them insufficiently accurate? Is this a generally known thing? Does the whole world share this opinion? Was it the dog that didn’t like the method? Why do they shy away from the word “I”? Does it make science less objective?

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Hollywood distorts everything, but especially likes to make up its own science. People writing the scripts for these movies clearly have no understanding of even basic science. They read too much stuff on the Internet about quantum mechanics, radiation, genetics, you name it. They then proceed to make up their own explanations for what they read, and give these explanations to their audience. The biggest propaganda machine the world has ever known is working hard to misinform the world about science.

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Noise in images perturbs measurements and makes the lives of image analysis people interesting. There are many sources of noise, depending on the imaging modality: photon noise, thermal noise, electronic noise, quantization noise, etc. I have just found out about another source of noise: etheric entities. Apparently these etheric entities appear especially in “certain photos of human/reptilian hybrids such as Bush or Obama,” and are completely indistinguishable from other types of noise. I’m not looking forward to test my algorithms against this form of noise.

RSA and DES keys keep growing in length, to keep up with increasing computational power. Keys we were using 15 years ago are laughable now. And according to a nice graph in April’s IEEE Spectrum, ridiculous amounts of computing power are cheaper than ever. I wonder how long before people give up on encryption altogether.

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According to Wikipedia, the ultimate source of knowledge, the word pixel comes from “picture element.” This means that a pixel is a part of a picture, and yet everybody I know keeps using that term to refer to a part of an image. Because it is “image analysis” that we do, right? Not “picture analysis”? Of course, this is just an insignificant detail that I’m blowing way out of proportion. No, my real beef with the word pixel is more complex.

The word pixel seems to be used only in the context of 2D images. For 3D images we have a different word: voxel. So if I have a 3D image, and take one 2D slice out of it by selecting a set of voxels, these voxels all of a sudden, magically, become pixels! And what happens when you record a multi-spectral volumetric image? Or a volumetric time series? What do you call the elements of a 4D, 5D or 10D image? Dean et al. use the word imel (for image element) in their ICS file format specification (P. Dean et al., “Propsed standard for image cytometry data files”, Cytometry 11(5):561-569, 1990, DOI:10.1002/cyto.990110502). Yes, it’s more general. Yes, it’s more awkward. And yes, it still hides the fact that the images that we analyse are sampled and discretised representations of some continuous reality. When we sample a function we obtain samples, not pixels. The data sets that we analyse are collections of samples. A digital image is composed of samples, whether it be a 2D image, a 3D image or a 10D image. Only after you paint a little rectangle on the screen with the color of your sample does it become a pixel.

PS: I’m suggesting the word lixel for samples in a 1D signal. You heard it here first!