Snowflake Images _VERIFIED_ Free Download
Harel Akridge
Myhrvold, who holds a PhD in theoretical mathematics and physics from Princeton University and served as the Chief Technology Officer at Microsoft for 14 years, leaned on his background as a scientist to create the camera. He also tapped into his experience as a photographer, most notably as the founder of Modernist Cuisine, a food innovation lab known for its high-resolution photographs of various food stuffs published into a five-volume book of photography of the same name that focuses on the art and science of cooking. Myhrvold first got the idea to photograph snowflakes 15 years ago after meeting Kenneth Libbrecht, a California Institute of Technology professor who happened to be studying the physics of snowflakes.
In order to get the snowflake on the sapphire slide, he first had to catch one. A piece of foam board that he painted black and clamped onto to the end of a mop handle did the trick. Once enough snowflakes fall onto the board, Myhrvold does a quick visual inspection of the specimens before deciding which one is best suited for his purposes. He then transfers it over to the sapphire slide using a small sable brush, similar to what watercolorists use when painting the finest of details.
Once safely on the slide, he focuses his microscope to take the photograph, changing the exposure one micron at a time. (For reference, the width of a human hair measures approximately 70 microns.) On average, Myhrvold photographs each snowflake more than 100 times, or as many times as he can before the snowflake starts to melt. Using specialized computer software, Myhrvold combines multiple photographs of a single specimen to create the final photograph.
Kenneth G. Libbrecht, a professor of physics at CalTech who has extensively studied the physics and pattern formation of ice, and whose work was what inspired Myhrvold to pursue this project in the first place, is no stranger to the challenges of building a high-res snowflake camera. He too has created a similar device, which he uses for his own research purposes. Besides himself, he says only Myhrvold and a Canadian photographer named Don Komarechka have accomplished the feat of photographing snowflakes at such a micro-level.
Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging information, like x-rays or a CT scan, along with medical record metadata. To generate holistic patient insights, parse medical DICOM images on Snowflake using Snowpark to create a DICOM metadata repository that includes clinical data. Then train and deploy machine learning models using Snowpark to classify medical images based on diagnosis for a health condition, such as pneumonia on a chest x-ray.
Snowpark python packages read and convert DICOM images into vectorized data. A Python Tensorflow model is built to read the vectorized medical image data and predict the possibility of pneumonia in chest x-rays. The model is then exported and deployed for inference in the form Snowflake User Defined Function (UDF). This UDF can then be called through a stored procedure for batch inference or through external API calls.
Some recent snowflake shots. Each were taken using the Olympus EM1 Mark 2, MC-20 teleconverter, 16mm kenko tube, Olympus 60mm macro lens and a Raynox 250. Constant lighting using the GODOX 126LED in my left hand
For this shot, I removed the surrounding fabric hairs around the snow crystal with Lightroom to be able to see the snowflake with little distraction. There is a tiny ice particle on the surface which even has a reflection itself. The color you see comes from thin film interference.
I became interested in snowflake formation in my early years of backcountry skiing because snowflake form as well as faceting processes in the snowpack and at it's surface are very important in snowpack stability. I originally read about this in the US Department of Agriculture's Avalanche Handbook 489, which was the original professional level avalanche book.
A repository is a named location in your account where you store images.This is similar to the relationship between a DBMS and a table within the DBMS.That is, a DBMS is equivalent to a registry, and a table is equivalent to a repository.
You can create one or more repositories in your Snowflake account. For example,DEV, TEST, and PROD repositories can store images during development, testing,and production. You can also create repositories that have differentpermissions; for example, some repositories may be read-only for some roles.
Using fundamental precipitation research from atmospheric scientist Timothy Garrett, who developed the original multi-angle snowflake camera with Fallgatter, the technology can resolve falling particles down to the diameter of a human hair and also measure the speed at which they fall. The first images produced by the team revealed the surprising diversity of snowflakes.
Michael Notaro with the University of Wisconsin-Madison is teaching Wisconsin school children the similarities in snowflakes to share the wonder of nature and information about the Great Lakes climate, but also to expand an international environmental database.
Based on the shape of the crystal, the students can classify what type of snowflake it is. Some of the options include columns, hexagons, two branches, four branches, and the typical Christmas-card version with six branches.
Winter is now settling over December, with flurries on brisk mornings and heavy snows that muffle the night. It was in this season of cold that Wilson Bentley, a farmer in Jericho, Vermont, attempted to capture the fleeting geometry of the snowflake with his DIY contraption of a microscope combined with a bellows camera. In 1885, at the age of 19, he became the first known person to photograph a snowflake, but it was hardly his last. Working until his death in 1931, Bentley photographed more than 5,000 snowflakes.
Bentley was not a trained scientist, or photographer for that matter, but when his parents gave him a microscope at the age of 15, he was hooked on examining the natural forces of the world. He would also study clouds and frost, yet nothing captured the public imagination like the snowflake photographs.
Even altered by the hand of Bentley, these images represent beautiful ghosts from a winter that bristled the air over a century ago. As it happens, it was in one of those harsh storms that Bentley was overtaken by the weather he so loved. After walking six miles home through a blizzard in 1931, the same year his exhaustive Snow Crystals monograph was published, he died of pneumonia on December 23, on the farm, the snow stacking up around him.
Example of a normalized raw MASC image. Several snowflakes can be seen in addition to background glare (center left) and subtle ground and sky glow (top and bottom). Note that the ground and sky glow may not be visible in all prints or computer monitor settings.
Example of a binary image produced by application of a brightness threshold and five-pixel radius to the normalized raw image in Fig. 2. Possible snowflake silhouettes are now apparent. Background glare (center left) was rejected due to exceeding the mean brightness threshold. Dimmer glare cases are reliably assigned to the not-flakes Q&R category.
Examples of image chips in the columnar crystal (CC) class of the final geometric dataset. All image chips in the final geometric dataset had been automatically categorized into the good Q&R category. We included a variety of sizes, forms, and degrees of riming. An example of a backlit snowflake is shown in row 2, column 2. Such cases were rare but were included whenever backlighting did not interfere with recognizability.
We present improvements over our previous approach to automatic winter hydrometeor classification by means of convolutional neural networks (CNNs), using more data and improved training techniques to achieve higher accuracy on a more complicated dataset than we had previously demonstrated. As an advancement of our previous proof of concept study, this work demonstrates broader usefulness of deep CNNs by using a substantially larger and more diverse dataset, which we make publicly available, from many more snow events. We describe the collection, processing, and sorting of this dataset of over 25 000 high-quality Multi-Angle Snowflake Camera (MASC) image chips split nearly evenly between five geometric classes: aggregate, columnar crystal, planar crystal, graupel, and small particle. Raw images were collected over 32 snowfall events between November 2014 and May 2016 near Greeley, Colorado, and were processed with an automated cropping and normalization algorithm to yield 224 224 pixel images containing possible hydrometeors. From the bulk set of over 8 400 000 extracted images, a smaller dataset of 14 793 images was sorted by image quality and recognizability (Q&R) using manual inspection. A presorting network trained on the Q&R dataset was applied to all 8 400 000+ images to automatically collect a subset of 283 351 good snowflake images. Roughly 5000 representative examples were then collected from this subset manually for each of the five geometric classes. With a higher emphasis on in-class variety than our previous work, the final dataset yields trained networks that better capture the imperfect cases and diverse forms that occur within the broad categories studied to achieve an accuracy of 96.2% on a vastly more challenging dataset.
df19127ead