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The algorithm was implemented in the following steps: 1 Convert the image from RGB to HSV colorspace 2 Obtain the luminance channel, V, from the HSV colorspace 3 Create a variable, histogram, that has bins 4 Scale the luminance value from to 5 Compute the histogram 6 Calculate the cumulative histogram 7 Transform the intensity values in the V channel of the original image to occupy the full range in a new image so that the histogram of V' is roughly flat 8 Combine the original H and S channels with the V' image to create a new color image 9 Convert the image back to RGB colorspace.

This is an algorithm that resizes images in a content-aware manner. The algorithm was implemented in the following steps: 1 Create an energy image, in this case by computing the gradient magnitude 2 Write a function to compute one horizontal seam via dynamic programming, then find one vertical seam by rotating the image by 90 degrees by using permute. I did spatial analysis and created maps and visualizations for a study on biogeography related to the Ryukyu archipelago's bird biodiversity.

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Link to Project Page Link to Publication. This study used the survey methods and data collected during the first Silent Steppe study to further investigate the trends in IWT in Mongolia. I created some of the maps and graphics for this study. The sharks and rays art show is meant to educate people about these animals along with the major threats they are facing and what we can do to help. At the end we raised over USD for the three organizations!

I created the interactive story scrollytelling that goes along with the technical report. The final visualization is in English, Chinese and Spanish. The website uses Wordpress so anyone with a user account can easily add content, and I created a Wordpress theme to allow for customization as well as ease-of-use for the user, as I added custom post types such as events, projects and monthly challenges so anyone can update the website even without technological skills.

I created three posters to highlight the main threats that sea turtles face: marine debris, illegal trade and fishery bycatch. See blog post: Sea Turtle Art Show. I organize an art show that aimed to raise awareness on sea turtle conservation and highlight their threats, and to demonstrate what people can do to help alleviate the problem.

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I focused on three main threats at the art show: marine debris, illegal trade and fishery bycatch. Go to site: Interactive Story. Link to landing page: The Rosewood Racket. Link to report: Report. I created the interactive visual story and a couple of static graphics for the report Rosewood Racket by the EIA. I created some visuals for a plastic awareness project by the ECO club at OIST to attempt to educate people about the negative effects of plastic, discourage people from using so many plastic bags and encourage them to cut plastic consumption.

The visuals I created were focused on the wildlife that are harmed by plastic and different plastic-related policies across countries. I also designed the logo for the ECO club. I took a course on GIS that focuses on conservation and related problems.

Here are some of the projects from the course. As part of my M. This is the first of a series of papers that documents the results. Link to Project Link to Report. I created many ststic maps and graphics for the report, including the following:. Link to interactive visualization: Hongmu Trade Prototype. This is a prototype of an interactive story that was created to visualize a technical report on China's Hongmu trade.

The final version isn't complete yet. The report is created by the Environmental Investigation Agency. I created an interactive shark animation with information on 10 shark species and the major threats that sharks are facing in general. Bingata is a traditional Okinawa printing technique. I created a series of educational infographics on sharks, their threats, and the common misconceptions related to them in order to educate people about them. These graphics were displayed at the sharks and rays art show, and most of them were translated into Japanese by Aina Menneken and Yuna Hattori.

Feel free to distribute. Below are some images of the different environments that the user can choose from:. In Photoshop, I first loaded three images taken at three different bands band 2 - B, band 3 - G, band 4 - R of Landsat8 into the RGB channel to create a multiband composite. Next I manipulated the histogram of the image for the purpose of dynamic image stretching by by using the "curve" tool in Photoshop. Next, in order to enhance the blues and the greens, I created a false color composite image with bands 7 swir 2 , 5 nir and 3 G and setting these bands to the R, G, and B channels respectively, then I blended this false composite image with the true color image.

Next, to further enhance the blues, I created a mask from band 6 swir 1.

I inverted the image so that water is white and land is black and adjusted further by using the "curve" tool. Finally, I enhanced the image by using panchromatic sharpening.

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Band 8 is the panchromatic band and has a finer spatial resolution at 15m so it can be used to sharpen the image. I resampled then stretched the image from band 8, then pasted it back on top of the true color image, then blended these images by using the luminosity mode. Link to Report K-means unsupervised classification: In unsupervised classification, the identities of land cover types to be specified as classes within a scene is not known.

The unsupervised classification algorithm will cluster pixels with similar spectral characteristics and produce a number of classes, with the number being determined by the user. The user then relabels and combines the spectral classes into land cover type classes. First, I determined the number of land cover classes I would like the landscape scene to be classified into and what they are. Next I choose the classification scheme to use and determined various parameters. For this particular project, I used the K-means unsupervised classification algorithm.

In the first step, the K-means algorithm assigns each pixel to a cluster randomly and finds the centroid of each cluster. Then the algorithm iterates over the next two steps until it reaches a point where class variation cannot be further reduced: 1. Re-assign pixels to clusters whose centroid is closer, and 2. Re-calculate centroid of each cluster. The parameters I had to choose for the algorithm include number of classes, change threshold, and maximum iterations. Initially I chose 20 for my number of classes, 5 for the change threshold and 5 for the maximum iterations, and gradually increased the number of classes to Maximum Likelihood supervised classification: Maximum likelihood is a supervised classification algorithm that assumes the class samples are normally distributed, it evaluates the mean, variance and covariance of the training data then computes the probability for each unknown pixel belonging to a particular class.

A pixel with the maximum likelihood or highest probability is classified into the corresponding class. The satellite image was first analyzed in order to determine which land cover class types to use to classifying the image, and some adjustments were made along the way.

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After having decided the initial set of land cover type classes to use, in ENVI I created many Region of Interests for each land cover class type by drawing polygons. For each land cover type I created from five to 20 polygons depending on how complicated and how much spectral variation the land cover type is. Then I ran the Maximum Likelihood algorithm on the image 18 times, each time altering the polygon number or shapes, adding or deleting classes as well as changing the probability threshold until I produced a decent image.

Support Vector Machine supervised classification: The SVM algorithm is derived from statistical learning theory and produces an optimal hyperplane by determining the location of decision boundaries that produce the optimal separation between classes. The optimal hyperplane maximizes the distance between itself and the planes representing the two classes.

The support vectors are the data points that lie at the edge of each individual class hyperplane and are closest to the optimal hyperplane. SVMs can handle non-linear boundaries between classes by using kernel functions. Colorscheme for this image: Red: Great deforestation The goal of this project is to detect changes in land use in particular, the degree of deforestation in an area in Bolivia between and Finally, density slicing Raster Color Slices in ENVI is applied on the difference image in order to highlight the areas that have changed.

The map of Carpathians mountain and illegal wood cut from it and imported to Japan was created for a blog post related to the topic. I am investigating what are some potential factors that influence housing prices in the Madison area. I obtained information on 96 individual postings for house sales 95 after removal of an outlier from the real estate website Trulia, and at the end I used the following parameters to perform a multiple regression to predict house prices: area of house in square feet , number of bedrooms, and number of bathrooms, percent people commuting by car in neighborhood, number of restaurants in vicinity, and distance to capitol in meters.

The result of the model suggests that there is a relationship between the variables investigated and price given that the p-value is less than 2.

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The R squared value is 0. Toggle navigation Julia Janicki. What I do Portfolio CV. What I do. Conservation Biology A lot of my work is motivated by species conservation. Environmental Outreach I do a lot of environmental outreach to raise awareness, for example I received a small grant from the Department of State and started the Educational Artshow Fundraiser series in Okinawa. Data Visualization, Interactive Storytelling I really enjoy the whole process of gaining insights from data, from data acquisition to data analysis to data visualization.

Cartography, Spatial Analysis I have taken many courses on cartography while at school and have worked on many static and interactive mapping projects, such as antmaps. Portfolio Work In Progress. Legal Atlas Primate Maps Cartography. Cyclone Idai Flood mapping Remote sensing, Classification. Multiple Regression: Housing Statistics, R. Nigeria's Kosso Trade Interactive Storytelling. China's Hongmu Trade Prototype Interactive storytelling. Shark Infographics Design, Education, Conservation.

Bingata Art Traditional Okinawan Printing. Plastic Awareness Exhibition Design, Education. Global Madison Web Mapping. Weevil Viewer Mapping Application, Biodiversity. Screen Printing Screen printing, Studio setup, Activism. Color Transfer Computational Photography. Histogram Equalization Computational Photography. Seam Carving Computational Photography. CV Julia's Timeline. Environmental Observation and Informatics. Okinawa, Japan.