Componica

Location: Iowa City, IA

Remote: Yes

Willing to relocate: Iowa, Minnesota, Michigan, Wisconsin, Chicago, Maine, Vermont, and other areas less affected by future climate change with a reasonable cost of living.

Technologies: C/C++, Python, Javascript, OpenCV, PyTorch

Resume/CV: Upon request, LinkedIn https://www.linkedin.com/in/stevencharlesmitchell/

Email: steve@componica.com

More than a decade of experience in creating innovative products and co-founding startups within the computer vision space, I have a strong background in developing object tracking in 2D and 3D, facial features interpretation, and eye gaze tracking. My current project involves eye-tracking technology for fighter pilots, designed for use in both simulator cockpits and helmet displays. Previous projects include systems to optically grade student assessments via Chromebooks and WSAM in real-time, 3D multi-camera tracking of body poses, visual interpretation of emotional affect in both humans and animals, and technology for measuring pupil responses. I also have a background in medical computer vision applications, including automated computation of ejection fractions from cardiac MR and echocardiogram data, as well as interpretation of intravascular ultrasound (IVUS) images.

You're probably right in terms of the NN research world, but I've been staring at a wall reminiscing for a 1/2 hour and concluded... Neural networks weren’t widely used in the late 90s and early 00s in the field of computer vision.

Face detection was dominated by Viola-Jones and Haar features, facial feature detection relied on active shape and active appearance models (AAMs), with those iconic Delaunay triangles becoming the emblem of facial recognition. SVMs were used to highlight tumors, while kNNs and hand-tuned feature detectors handled tumors and lesions. Dynamic programming was used to outline CTs and MRIs of hearts, airways, and other structures, Hough transforms were used for pupil tracking, HOG features were popular for face, car, and body detectors, and Gaussian models & Hidden Markov Models were standard in speech recognition. I remember seeing a few papers attempting to stick a 3-layer NN on the outputs of AAMs with limited success.

The Yann LeCun paper felt like a breakthrough to me. It seemed biologically plausible, given what I knew of the Neocognitron and the visual cortex, and the shared weights of the kernels provided a way to build deep models beyond one or two hidden layers.

At the time, I felt like Cassandra, going from past colleagues and computer vision-based companies in the region, trying to convey to them just how much of a game changer that paper was.

My take during that era was neural nets were considered taboo after the second AI winter of the early 90s. For example, I once proposed a start-up to consider a CNN as an alternative to their handcrafted SVM for detecting retina lesions. The CEO scoffed, telling me neural networks were dead only to acknowledge they were wrong a decade later. Younger people today might not understand, but there was a lot of pushback if you even considered using a neural network during those years. At the time, people knew that multi-layered neural networks had potential, but we couldn’t effectively train them because machines weren't fast enough, and key innovations like ReLU, better weight initializations, and optimizers like Adam didn't exist yet. I remember it taking 2-3 weeks to train a basic OCR model on a desktop pre-GPU. It wasn't until Hinton's 2006 work on Restricted Boltzmann Machines that interest in what we now call deep learning started to grow.

The Yann LeCun paper 'Gradient-Based Learning Applied to Document Recognition' specified the modern implementation of a convolutional neural network and was published in 1998. AlexNet, which woke up the world to CNNs, was published in 2012.

Between that time in the early 2000s I was selling implementations of really good object classifiers and OCRs.

In our area of the rural Midwest, a local AM station made a niche for themselves being the critical go to point of information during tornado, storm, flooding, and derecho warnings. Sadly as the storms keep showing up more often and more powerful, I've actual bought a couple more portable AM/FM radios spread around the house because of it.

There have been several times being in a car during those warnings that that AM station was the only reliable source of information.

One of our partners is a renowned neuro-ophthalmologist at our local university, and he's always suggested medical applications for our array. However, the combination of low volume, slim margins, and regulatory challenges (acquiring patient training sets and navigating FDA approval) makes this direction potentially perilous for us. What helps us is our human factors partner has decades long connections with NASA, FAA, and DoD which gets us a foot in the door, and allows us adopt a low volume/high margin strategy. This approach is letting us refine our technology and then we can focus on standardizing it, reducing costs, and perhaps refocus on medical applications.

I do think your suggestion is a good approach, but what's key is finding smart investment that has experience in developing medical devices.

Luxury car prices per array due to development, customization and the type of clientele. We have a mech engineer onsite to adapt the camera arrays to the environment, such as a cockpit or centrifuge, while taking into account the field of views and mounts. Yes it can handle multiple screens and curves like domes, the fun part is working out the geometry to convert gaze vectors into pixel coordinates. Pixel accuracy is a function of the number of camera (6 to 12), distance to the array, and geometry of the subject and array but we can typically discern which button / control / digit a pilot observed.

Of course cost can be substantially decreased if everything was standardized and the mech engineering was done once. BOMs (excluding the high-end desktop computer) is about $2000-$3000. My dream would be to reduce that cost by moving the computer vision to compute modules per pairs of cameras reducing the BOM to < $1000 and avoiding the desktop.

My three partners and I have be developing and selling multi-camera arrays specifically for eye tracking as well as measuring other physiological features for several years now. Our main customers are a couple university research groups, a human factors group in Lockheed, and just recently the US Air Force. In fact we just returned from a trip to Wright-Patterson installing an array in a hypobaric chamber to perform gaze tracker and pupil response for pilots under hypoxic conditions. Phase two will be a custom gaze tracker for their centrifuge. Our main features are accurate eye and face tracking up to a meter from the array, minimal calibration per subject (about 10 seconds staring at a dot), pupil response for measuring fatigue and other things, plus we can adapt the array for the client ranging from a cockpit to a large flat screen TV. We've looked into medical usage such as ALS, but we're bootstrapped based in Iowa and found the military niche as a more direct way to generate cash flow. It's ashame we can't apply this work towards people with medical needs, but we don't have the funds nor the clients to make such a pivot.

Location: Iowa City, Iowa

Remote: Yes

Willing to relocate: Impossible, due to family.

Technologies: Computer Vision (PyTorch / OpenCV), C++, Python, Javascript

Résume: https://www.linkedin.com/in/stevencharlesmitchell/

Email: componica [] gmail.com

Over 20 years of computer vision experience (it's true) and been involved in several startups involving document scanning / grading, education, medical, face and eye tracking. My favorite things to do are smashing conventional and deep learning computer vision algorithms into embedded devices, tracking landmarks on video, and figuring out where a human is looking.

I think this is the video: https://www.youtube.com/watch?v=gsBIQIBavms

Quoting Wikipedia: "As of 2020, the majority of hydrogen (∼95%) is produced from fossil fuels by steam reforming of natural gas, partial oxidation of methane, and coal gasification"

"As of 2020 most of hydrogen is produced from fossil fuels, resulting in carbon emissions."

https://en.wikipedia.org/wiki/Hydrogen_production

My ears perk up whenever I hear a "Just think of the Children" argument because after Sandy Hook, I'm pretty certain the US could careless about children. There's a real reason behind this.

It's most likely a demand by China so that they can create an infrastructure to locate political dissidents. Oh look, a Winnie the Pooh Xi meme ended up in your gallery/inbox. Why is there a knock at the door? I'm pretty sure thats the real reason.

Imagine taking a photo or have in your gallery a photo a dear leader doesn't want to spread. Ten minutes later you heard a knocking at your door. That's what I'm most worried about, how is this not creating the infrastructure to ensnare political dissidents.

Imagine taking a photo or have in your gallery a photo a dear leader doesn't want to spread. Ten minutes later you heard a knocking at your door.

Imagine taking a photo or have in your gallery a photo a dear leader doesn't want to spread. Ten minutes later there's a knock at your door.

Why can't there just be an option to subscribe for one year and it cancels? No auto-renewals, just a polite reminder it's about to expire. The world is filled with roach-motel dark patterns everywhere that I never subscribe to anything, and they're losing out on people like me.

It like other neural network research was ignored because neural networks were considered a dead-end at the time. In the early 2000s I recreated his LeNet-5 implementation, and no one was interested despite the great results I was getting in OCR and medical image processing with tumor detection.

Younger people don't realize there was strong bias against using neural networks in the late 90s up until Hinton's talk on NNs around 2007. I get the feeling we're going through the same thing where novel research is becoming ignored because everything must fit the deep learning paradigm to be noticed.

Definitely not drowning in $variables, he's 40 something.

Looking at https://imgur.com/a/eTphrRF The code that he writes is a blend of languages. The arrow operators -> is indicative of C/C++ but C/C++ doesn't use nil or function. It's like he thinks in C/C++ first which puts him at about my age mid-40s. Anyone younger than 40 would be using dots '.' due to Java/Javascript/Python instead of '->'. Does anyone else get that vibe?

We discovered, after accidentally having twins and bumping the kids count to 3, a typical sedan cannot physically carry more than two children because you cannot fit 3 or more child seats in the back as required by law. That's why families of 3 or more kids have minivans. It's not like the 80s where you toss the kids in a station wagon and let them wander in the back, they are required to be in massive padded seats.

Running a computer vision consulting shop, that's our way of life. For almost a decade we've developed semi-automated means to make the problem saner, but we have to maintain a network of people and software for labeling images.

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