Remnants of my Tesla coil

My first year of high school I tried to build a functioning, high frequency Tesla coil entirely from scrap parts. This project is almost a cliche nowadays; thousands of dedicated hardware hackers have successfully created ominous and occasionally dangerous coils, and so-called “singing” Tesla coils are the new trend among hobbyists. But the project was one of my first earnest attempts to learn about something on my own and apply that knowledge to a non-scholastic project, and so I wanted to link to a few resources here that I found invaluable when I was first starting out:


The Powerlabs Tesla coil page. This is the most “professional” Tesla coil I have found that was built by a hobbyist. The craftsmanship is impeccable, from the precision of the secondary coil winding to the care with which the capacitor bank was assembled. The care is reflected in the results; I am very confident that this is one of the most efficient Tesla coils I’ve come across, as it appears to regularly generate 18-inch streamers despite its compact size

The trashy Tesla coil. I like this project because the author defiantly avoids using any store-bought components or parts, using piping and wiring entirely scavenged from his local rubbish yard. This site is also home to one of my favorite anecdotes from a hobbyist:

For some funky reason every time I switched on the power, the sprinkler system in the yard turned on. I’m not kidding here. The yard gets watered every time I fire it up.

Primary and Secondary Coil

The red, long coil in image at the top of this post is the secondary coil from my own Tesla coil, which took me about a week of winding 28 gauge enamel-coated wire over oven-baked PVC pipe. That the toroid is a doorknob is a good tip-off that the payload isn’t resonantly coupled. The pancake-spiral in the foreground is a remnant of my original primary coil design, which I based on tutorial found on this page.


I first realized how attainable a homemade Tesla coil would be when I saw just how simple it can be to make high-voltage capacitors at home in the form of Leyden jars, which can be made from a film canister or bottle and some aluminum foil. Using a CD jewel case and some foil, I’ve even made capacitors that can be charged from a CRT screen but which will produce 3-inch sparks upon discharge—although predicting the discharge rate and stability of Leyden jars against dielectric breakdown is almost an art when one is using plastics and glass with aluminum foil. The best page for an intro to Leyden jars and their uses can be found here.

Primary transformer

Most Tesla coils use a step-up transformer even before the current reaches the primary circuit. This allows shielding of the electrical mains from sparks and shorts in the primary circuit, and it also allows one to get by using capacitors made from beer bottles, air gap discharges, etc. because a higher voltage primary circuit requires less finicky specifications (it would also be very difficult to use a spark gap to modulate the frequency if one was only using mains voltage). I originally ran my coil off of car batteries by using an electromagnetic buzzer and a pair of ignition coils in my primary circuit; however, if I were rebuilding it today I would instead use a neon sign transformer, which I believe offers much more reliable and safe performance despite running on mains power. Here’s a buying guide for NSTs for Tesla coils.

Spark Gap

When I was in high school, I always found the spark gap to be the most mysterious component in the Tesla coil primary circuit. After all, the primary circuit is already an AC circuit, and it seems like forcing the current to regularly jump an air gap would induce significant power losses that would reduce the efficiency of the transformer. The latter point is correct, but it turns out that the spark gap is still worthwhile because the timescale of the AC cycles coming out of the HV transformer being used to drive the primary circuit is way too fast to effectively switch most Tesla coil designs, given the dimensions and couplings of the primary and secondary coils. The spark gap allows the capacitors to fully charge and discharge at a rate set by their time constants and the properties of the spark gap itself (since things like pointed electrodes can create corona discharge, reducing the effective dielectric constant of the air in the gap). As a result, the spark gap acts like a high-power switch at a low enough frequency to allow effective transfer of energy between the primary and secondary coils. A good description of the idea behind using a spark gap (instead of a high-power relay and integrated circuit or other solid-state switch) can be found here and here.

Computer-generated prophecies of the apocalypse

Who said ever to no crown to was the listener and SECOND: my man, and to the wheat,
and had eat in this angel.

Their pieces kill the sort the angel come up to another translucent and weep any stone.
Her timeless will measure them to the day, hold created with earth noises and hurled every nation.

There shown out upon the voice
It be in seventh which is to trample, I.

This tampering opened not for its time.

The land to their moment Who threw their glory to cherish that art.

The glory to the speaking, and at that white appearance, and say given the thousand for the sake.
And said show in myself. And it of no sweet victory whose gateways enemies was loathe to the bowl
and it for them and worked out as my hast to every vision.

Their noise erase me.

I’ve been working on improving my context-free grammar parser for the automated generation of sentences. My current heuristic involves picking a sentence at random from a text, parsing it, and then swapping all terminal symbols in the sentence with other, equivalent symbols drawn from randomly the pool of all valid terminal symbols found in the text. This means that the structure of every sentence generated by my program will match the structure of at least on sentence in the text, but the specific words or nonterminal symbols are entirely unconstrained aside from their syntactic function. I tried this approach on the 1957 translation of the Book of Revelation, and I ended up with the spooky (albeit occasionally ungrammatical) prophecies at the top of this post.

Re-sorting Pitchfork’s top albums of 2010-2014

The difference between the official and expected ranking of Pitchfork's top 100 albums of the decade so far.

Pitchfork just released their rankings for the best albums of the decade so far. Unsurprisingly, hipster favorites like Vampire Weekend and Kanye West won out (as any longtime reader of Pitchfork would expect), but, surprisingly, several relatively unknown artists or lesser-known albums by famous artists sneaked onto the list, including Earl Sweatshirt’s debut Earl and Frank Ocean’s first mixtape Nostalgia, Ultra. I’ve read before that Pitchfork tends to modify its editorial opinions in order to adjust to current trends in music, and so I was curious about the degree to which the assigned ranking matched an equivalent, “expected” ranking generated by comparing the numerical score that Pitchfork gave to each album at the time of its release. The above figure is a graph of the difference in ranking of the top 100 given by the “official” Pitchfork ranking, and a ranking generated by looking up the numerical score given to each album (in the list) upon its release and sorting the albums from lowest to highest score. The order of the vertical axis is the official Pitchfork ranking, from position 1 at the top to position 100 at the bottom. The bars indicate the difference in ranking for each album, which was generated by subtracting from the official Pitchfork ranking the expected ranking based on its numerical score after release. Large differences in the position on the list thus indicate Pitchfork’s relative opinion of the piece changing substantially by the time the “official” top albums ranking was compiled.

At least two of the albums that made the list, Earl Sweatshirt’s Earl and Jai Paul’s eponymous album, were so obscure at the time of their release that Pitchfork didn’t even rank them. In recognition of this fact, Pitchfork rated them both near the bottom of the top 100, and so their difference in ranking doesn’t seem that large on the graph. But the honorific inclusion of these two albums underscores a more general trend apparent in the Pitchfork list: an emphasis on contemporary R&B and hip-hop at the expense of electronica and downtempo. In a list sorted purely by numerical ranking, Beyonce’s Beyonce would not have scored as absurdly high as it does on the Pitchfork official ranking, nor would have Thundercat’s debut Apocalypse, which is the biggest winner in the ratings. These won out over albums like Reflektor or To Be Kind, which both showed relatively large drops relative to their expected positions on the list.

Pitchfork undoubtedly sees itself as a ratings site capable of setting the zeitgeist for a given decade, and so the emphasis on new artists and movements over indie staples like Arcade Fire or Swans suggests that the website sees the newer artists as representative of the next major movement in indie music. To this end, it’s worth noting that the most recent album declared “Best New Music” by Pitchfork before the creation of the ranking was FKA Twig’s outstanding LP1, which stands at a healthy position on the official list and which generally represents many of the stylistic frontiers of emerging indie music.

The relatively large change in Pitchfork’s opinion of albums is well-captured by a scatterplot of the numerical, review-based ranking versus the official ranking released by Pitchfork (shown below, concept originally suggested by reddit user gkyshr). Surprisingly, there seems to be barely any correlation between the two variables (the line y = x, corresponding to the case where Pitchfork’s released ranking coincides with the sorted ranking, is underlaid). This variation is captured by the mean of the absolute value of the differences reported in the bar chart, which came out to 20 (a surprisingly high value, given that the maximum change in ranking for a given album data is 99). It’s almost as if Pitchfork deliberately attempted to make its rankings differ from expectations, with the only albums really falling on the line corresponding to very highly rated albums, like the number 1 album, My Beautiful Dark Twisted Fantasy:

Scatterplot of rankings

In order to make these plots, I made use of Python’s outstanding BeautifulSoup and pandas libraries.



Building a high power voltage multiplier


A simple high-voltage circuitry project is the Cockroft-Walton voltage multiplier. I first created this as a demonstration for a class in high school, but I’ve altered it over the years in order to improve its performance. The nice thing about this project is that it can be created entirely using cheap, store-bought components—diodes and capacitors—and so it is thus relatively easy to ensure that it will work and perform at the voltage estimated. I got my components from West Florida Components, and the total cost of everything was under $10. A good tutorial that gives possible specifications for the components can be found on Instructables.

The total voltage drop across many capacitors in series is equal to the sum of the voltage drop across each component—this is a consequence of Kirchoff’s circuit laws, and can be mentally visualized as a charge on one end of a capacitor displacing and equal but opposite charge on the opposite plate, which in turn displaces an opposite charge on any other capacitor plates to which it connects, and so on. The Cockroft-Walton multiplier can be visualized as a fancy way of putting a bunch of capacitors in series and charging them so that they each have a voltage drop of 120V, resulting in a total discharge voltage of 120 V times the number of capacitors. This output is roughly DC, and it has a much lower current than the device draws from the mains, hence preserving energy conservation since power = (voltage)*(current). A simple diagram of the half-wave CW multiplier looks like this:

A circuit schematic for a half-wave Cockroft-Walton voltage multiplier.

A circuit schematic for a half-wave Cockroft-Walton voltage multiplier.

The manner by which the CW multiplier can charge each capacitor separately to 120 V is essentially by charging them in parallel and discharging them in series. The concept borrows from the design of a basic half-wave rectifier, which uses a diode and smoothing capacitor to convert the positive portions of AC sine waves to a smooth-ish DC current. The idea is that the first stage in the circuit (capacitor 1 and diode 1) converts the AC to an approximately constant DC signal, which then gets fed forward through diode 2 to the right plate of capacitor 2. During the first positive cycle, that capacitor charges to +120V. During the “off” cycle (the negative portion of the AC sine wave gets blocked by the first diode), the second capacitor discharges through diode 3 into capacitor 3 because, during the off cycle, there’s now -120V on the bottom plate of that capacitor, leading to a potential difference that allows charging. During the next “on” cycle, the current ignores capacitor 3 because it is fully charged (and so it essentially acts like a break in the circuit there), and so now capacitor 4 gets charged instead. During the next off cycle, capacitor 4 discharges through diode 4 to charge capacitor 5, and the cycle repeats itself until, after (number of capacitors)x(charging time) all the capacitors are charged.

There are several equivalent ways of visualizing what is going on in the CW circuit, but the key things to remember are that the capacitors store the charge (differential) and the diodes force the AC to feed forward and charge each capacitor in sequence. The charging time can be adjusted by adjusting the time constants for each capacitor in the circuit relative to the AC cycle frequency (60 hz in the US).

The device would build up charge twice as quickly if one instead uses a full-wave design (which is analogous to a full-wave bridge rectifier), because it would then take advantage of the negative swing of the AC sine wave, which gets lopped off by the first diode in the half-wave version.

I the video above, I have added a switch and fuse for safety reasons (visible in the upper-left hand portion of the screen; I used a plastic lid as a base for the two components). In the first cut, the ~1 mm spark regularly produced by the device is visible. This spark can be used to drive continuously an 8 inch fluorescent tube (shown in the second section), but, curiously, the frequency of the pulses through the fluorescence in the tube depends on the proximity of other conducting objects—in the fourth clip, it is apparent that touching pliers to the glass reduces the frequency of the pulses, rather than increasing them as I would have expected. My best guess for the cause of this effect is charge build-up on the glass interior beneath the metal, leading to low-frequency discharge for the same reason that high-voltage capacitors decrease the sparking frequency in the primary circuit of a spark-gap Tesla coil. The last clip shows the device discharging through a 1 inch xenon flash tube salvaged from a disposable camera. The firing frequency is low due to the relatively large distance that the spark has to cover, despite the low dielectric constant of xenon gas. In other tests, I’ve noticed that large spark gaps that require charge build-up over periods longer than the ~3-5 s for the flash tube will generally result in short circuits occurring upstream in the capacitors in the CW, which probably cause damage to the solder joints and possible the capacitor ceramic due to dielectric breakdown.

Cockroft-Walton generators have a special significance in the history of physics because they were used to generate steering currents in one of the earliest particle accelerators, enabling their creators to win the first Nobel Prize in Physics ever given to a collider project. For this reason, one of the first large-scale CW multipliers (manufactured by Philips Co.) is prominently displayed in the National Science Museum in London:


A Cockroft-Walton generator built in 1937 by Philips of Eindhoven. National Science Museum, London, England.

A Cockroft-Walton generator built in 1937 by Philips of Eindhoven. National Science Museum, London, England. Image from Wikimedia Commons.



Algorithmic interactions on social networks

I recently built a simple program that attempts to impersonate users of an online forum using a combination of a Markov model and a context-free grammar.

Recently, I’ve been using Python to scrape data from the content-aggregation website Reddit, at which users can submit links and vote on each other’s submissions. Links that generate large numbers of upvotes in the community may eventually make it to the front page of Reddit, where they will be viewed by millions of individual casual browsers as they first enter the site. Reddit’s voting algorithm and method for determining the ranking is closely guarded in order to prevent marketing companies from spamming the front page with links.

A really great Python utility for working with Reddit data is PRAW, which provides an interface between Reddit’s API and Python. The module not only allows easy scraping of information like the popularity and content top articles, the number of upvotes, and the number of comments; it also simplifies the creation of “bots,” or automated users in the Reddit community that comment on posts. The function of bots ranges from practical—one bot posts a text-mined summary of every Wikipedia article that makes it to the front page, while another posts metric conversions of every reddit headline containing imperial units—to whimsical: one bot periodically scans comment threads for rhyming words, and adds a comment to the thread demanding users cease deploying puns.

For my bot, I wanted to create a user who could convincingly act like a normal “human” user. My immediate idea was to scrape comments on an article, create a word-as-token Markov model, and then automatically post a comment containing a fixed-length output generated by the model. Markov text generators are an incredibly common opening exercise in introductory computer science courses—they are relatively easy to prepare, yet can produce surprisingly realistic (and occasionally humorous) output if trained on the right corpus of text, like this sample output from a 3-word-token Markov model I trained using Mary Shelley’s Frankenstein; or, The Modern Prometheus:

This towering above all, thy mountains, obscured in darkness and cast my eyes were shut to the conversations he held with his parents ever since our infancy. We were in my enterprise. I have confessed myself guilty of a peculiar and overpowering nature ; nor do the floating sheets of ice , which was one which attracted my attention suddenly grew despicable. By one of your sentiments of this wretched mockery of a different conclusion

Because of the relatively small size of the training text in comparison to the token size, a lot of the phrases, like “his parents ever since our infancy” or “floating sheets of ice,” are complete phrases taken directly from the novel. Nonetheless, randomly picking three word phrases based on their frequency in the corpus captures the style of the novel remarkably well, despite there being no explicit specification of sentence structure when the code is trained on the corpus. If I reduce the order of the model to 2, I get an output text that’s significantly more “original,” but also marred with more grammatical and stylistic errors:

This apprehensions as he had hitherto been present to your health rendered , no disaster is murdered , on your duty towards the most beautiful that you could I opened my, I, the reflections determined thenceforth to a direction, and my appetite. One by Elizabeth. She was not unfolded to recollect what has been adduced against me was free last night; such a fiend can not describe their native town of great crime , in death shall be the beginning of food or take their inquiries clear conception of the dashing waves continually renewed violence.

I originally intended to have my bot write pseudoscience or new-age philosophy, which would convincingly allow other users to overlook its frequent lapses in grammar as kooky derailment. I quickly learned, however, that the output of a Markov model is distinctive enough that other, tech-savvy users could infer that the comments were algorithmic. Looking to refine my approach, I instead investigated to approach used by SCIgen, a well-known automatic scientific paper generated that gained attention in 2005 when its authors successfully submitted SCIgen papers with titles like “Rooter: A Methodology for the Typical Unification of Access Points and Redundancy” to several less-reputable journals and conferences. Contrary to what I expected, SCIgen does not use an augmented Markov model, but rather context-free grammar, a token-based approach to generating text that takes into account the natural hierarchies observed in sentence structure first described by Chomsky. The method is outlined in much better detail elsewhere, but the essential concept is that sentences contain a natural hierarchy of information that motivates their representation as a tree data structure. For example, the sentence, “The dog who was dirty ate the slippers” can be split first into two parts—the subject cause about the dirty dog, and the verb clause about his crime—and each of these parts can be further subdivided into adjectival clauses, predicates, and finally individual words like articles, nouns, and adjectives. In a context-free grammar, the non-terminal nodes of the tree (clauses, etc) are governed by production rules (verb clause ->  verb + article + direct object) that state the next levels of decomposition, each of which has its own production rule (article -> “the” OR “a” OR “an”). A more advanced grammar tree (for a Victorian sentence from “Frankenstein”) looks like this:

A context-free grammar for a sentence from Frankenstein, parsed using the NLTK module for Python.

A context-free grammar for a sentence from Frankenstein, parsed using the NLTK module for Python.

A CFG text generator would start with the start symbol (‘S’) and then move down the tree from left to right, outputting a terminal symbol each time it sees one and then moving back up the the lowest incomplete branch. In order to get truly random text, the CFG is trained with many sentences, and so the parser will have multiple possible options for each symbol—after it parses S, it could choose to move to a sentence that looks like NP VP (like the one above), or one that looks like NN PN (subject noun – predicate noun, like “the man is a dog”). After it randomly makes that decision, it then has many options for each of the subsequent nonterminal nodes, and then it finally has a choice of many possible terminal symbols (once it reaches an NN, it can pick from any of the NN used in the training set, since they are syntactically equivalent).

One noteworthy detail of the sentence shown in the figure is the presence of repeated units, like  noun phrases (NP) that link to other noun phrases, which capture the fractal nature of language constructions in which sentence-like clauses are used to modify individual components of a sentence. For this reason, when generating text from a CFG, it is very easy to implement recursion, but it’s also very easy for that recursion to get stuck in an infinite loop, where a lower symbol links to a higher symbol, resulting in repetition:

The professor hated the man who hated the professor who hated the man who hated the…

The sentence is grammatical but undesirable. For large grammars, the incidence of long sentences seems to increase, since the number of possible interlinking loops and knots in the grammar tree becomes large.

In order to use a CFG to generate grammatically-correct gibberish, I made use of the famous NLTK module for Python, which contains many tools for processing and cleaning natural language data sets, as well as text in which human experts have tagged individual words as nouns, verbs, etc. This makes it possible to use pre-built functions in the module to tag individual words in a user-chosen sample text based on their similarity to words in the text used by the experts, while simultaneously identifying the overall relations among words in a data set in order to identify non-terminal symbols like clauses. In my particular code, I scan a text for all terminal symbols first, which is a compratively fast operation. I then pick sentences at random and parse their full grammar (which can take up to a minute), but then keep only their nonterminal symbols (so I discard individual words). I then concatenate the word rules with the nonterminal production rules, resulting in a grammar with the full vocabulary of my corpus, but only a subset of its grammatical structures. Since there are multiple possible terminal symbols for a given word (ie, noun -> ‘Frankenstein’ or ‘professor’ or ‘night’ or [any other noun in the entire book]), the generated text is structured yet completely random in its choice of specific word to fulfill a given function in the sentence. But restricting the nonterminal grammar rules also allows me to monitor whether a specific sentence structure tends to cause infinite recursion or other problems. Running this model with the Frankenstein corpus resulted in occasionally eerie text:

All delight not, spoke for tears unknown and conceived. I and a mankind , and in the place , close wonder paid to saw passed this behaviour. My beautiful not hired inquietude of immeasurable serenity these beautiful care, my variable indeed enslaved sorrow this fellow, that remained eyes with this willow endeavour in the courage of the truth before interested daylight.

The past innocence not, was I of feeble horror. A invader near a white slave which a loss answered with this truth: Man broken in the considerable atmosphere. Misery. her brother, my remorse, the world.

The text may be less convincing than that generated by the 3-gram Markov model, but that text borrowed entire phrases from the source text whereas this model only borrows specific sentence structures: the words used to fulfill the various functions in the sentence, such as the nominative or verb, are chosen entirely at random from all nouns and verbs found in the source text, making it pretty unlikely that a specific chain of multiple distinct words in the corpus, like “floating sheets of ice,” would recur here. Thus while the 3-gram Markov text might better fool a human reader (who doesn’t have the novel memorized), the context-free grammar model is more likely to fool a computer program that detects plagiarism by searching for phrases in an online database.

Because of the lyrical quality of the Frankenstein CFG output, I tried to post a few (re-formatted) outputs in the poetry criticism subreddit, /r/OCpoetry. The verses received generally positive feedback until one user noticed that my account name and post history suggested that the text was random gibberish. In retrospect, this wasn’t a particularly surprising outcome, given the unusual training corpus and the need for the bot to fool human users, and so in future work I may try to restrict the bot to more specific types of comments or posts with formulaic structures. However, for the doodles or occasional flashes of brilliant nonsense that I come across while training the bot, I created a subreddit /r/markovpoetry where my bots (and other users) can post amusing grammar generated by their text-generation programs.

The full code for this project, along with sample corpora, can be found on my GitHub page.

Scratching holograms into bulletproof glass

A few months ago I came across an old piece of bulletproof glass I bought in high school when I was trying to make a camera shield for a thermite project. I had a little bit of free time, and so I was reminded of William Beatty’s famous method for making holograms, in which a virtual image can be created by literally scratching a pice of acrylic glass. I tried to make a simple star pattern, and the resulting video does not really do the effect justice:


A much better video comes from the author himself:


A detailed tutorial is available on Bill Beatty’s colorful website, Because the depth below the surface at which the virtual image appears scales with the radius of curvature of the arcs scratched into the glass, more sophisticated images can be generated by varying the radius of curvature in order to create images at multiple depths—allowing one to create a real 3D rendering rather than a 2D image that appears to sit below a surface.

Identifying fossils using machine learning.

This weekend I wrote an image processing routine that uses machine learning methods to classify fossil shark teeth from my collection.

Some of my favorite early childhood memories involve wandering up and down a the beach in Venice, FL, searching for fossilized shark teeth for which the region is known:

Over the years, my collection has grown to roughly 10,000 full or partial teeth, which are roughly sorted by morphology or, by proxy, species. Sorting the teeth by eye is not entirely trivial, particularly because of various subspecies and close relatives that have large variations in tooth shape, and because the shape of teeth from a particular species of shark will vary depending on their location in the mouth. However, because I already have a large set of teeth pre-classified, I thought I would use my collection as an opportunity to play with Python’s scikit-learn library for machine learning, to see if algorithmic methods might identify patterns or distinctions that I am missing or neglecting when I sort the teeth by eye.

My manual classification is based on the guides to each shark available on the University of Florida website, augmented by additional photos available on a now 404’d website I used to access when I was younger and first initiated the collection:

Generation of image outlines for classifier

I first drew a 1″ x 1′ grid on a piece of white paper and placed around 54 teeth within the resulting 9 x 6 grid, taking care to space them as evenly as possible. I took a quick image of this array with my iPhone, taking care to square the corners of the viewing frame with the page, which conveniently has the same aspect ratio as the page, allowing me to both minimize tilting/rotation and to enforce the same scale for all images without using a tripod.

Fossilized sand shark teeth.

Fossilized sand shark teeth arranged in a 1″ x 1″ grid.

I processed the resulting images through imagesplitter, allowing me to divide the grid into separate images for each shark tooth. There are probably fancier ways of creating separated segmented images for each tooth that don’t involve aligning them in a grid or splitting the images (MATLAB’s bwlabel() function comes to mind), but I didn’t mind having separate images for each tooth in case they come in handy for a later project.

I took the resulting image series for each species and opened them in Fiji (Fiji is just ImageJ) as a stack. While most operations for which ImageJ/Fiji are commonly used can be done easily without a GUI in MATLAB, I like using a GUI for stacks because it’s easier to preview the effect that operations have across the entire stack. For other projects, whenever I’ve needed to something fancier than a standard segmentation or tracking I’ve found it easier to write a Java program to run in Fiji than to go down the rabbit hole of opening and processing stacks of images in MATLAB, which never quite performs as smoothly as I would expect.

For the teeth that have left or right crooks, such as tiger shark teeth, I went through and manually flipped the images of teeth pointing the other direction, since nothing I’ve read suggests that teeth with a given chirality have other distinct features that would be useful to the classifier—the orientation just depends on which side of the shark’s mouth the tooth originally came from (a quick lookover confirms that I appear to have roughly equal numbers of left and right pointing teeth—apparently ancient sharks didn’t preferentially chew on one side)

I then performed rigid registration (rotation/scaling/translation) of the binary images for each species onto one another using the “StackReg” module that comes built-into the Fiji “registration” toolkit. I cropped the resulting images to a square in order to simplify resizing and stacking. The sequences registered images end up looking like this:

Different morphologies for fossilized tiger shark teeth.

Different morphologies for fossilized tiger shark teeth.

Different morphologies for fossilized bull shark teeth.

Different morphologies for fossilized bull shark teeth.


In a sense, I am already passing the classifier much more information than it needs to know, since the boundary of the segmented region is the only feature that contains information in the images.

For this project, I thought I would try both a supervised and unsupervised classification approach. Since I already have approximate classifications for the teeth based on my manual sorting (by shape) over the years, I can label each set of segmented images with a candidate species, train a classifier, and then apply the resulting regression to a couple of new images of teeth to see if the classifier agrees with my best guess.

The more intriguing aspect of the project is determining whether the code can work out distinctions itself given an unlabeled set of teeth from multiple species. This would give me an idea of just how distinctive the different morphologies really are, and it could reveal the presence of other species with similar looking teeth that I’ve been mis-classifying because they look so much like other species.


Perhaps unsurprisingly, even basic Naive Bayesian classification performed extremely well for this image set, even for similar tooth morphologies (in incisors of bull shark teeth look very similar to those of dusky sharks, yet the classifier was miraculously proficient at discerning them. I’d estimate an accuracy of about 96% for the collection of teeth I processed today.

Update 1/4/2015: I recently made this website, which features images (with scale bars!) of all the pieces in my fossil collection, including these shark teeth.