Cartoon Network Game Jam

Cartoon Network Game Jam

Corey  Warning

Corey Warning

Portland, Oregon

A 48 hour game making competition to compliment the launch of "OK K.O.!" 200 developers, 45 games, 1 grand prize: complete your winning gam

Game Development, Networking

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Description

I co-organized a game jam where game makers created 48-hour jam entries based on judging criteria determined by Cartoon Network. At the end of the weekend, entries were given a short polishing period for their projects, then a winner was chosen! 16 Finalists won prize money and will have their work published in a Cartoon Network Game Jam bundle! The winning game jam team had a chance to negotiate a contract with Cartoon Network to provide the services to bring their project to fruition as a fully-fledged mobile app.

Links

Pump Up The Jam (Portland Mercury)

CN Game Jam Website

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Gunasekaran S. updated status

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Gunasekaran Sengodan

I'm learning and trying to develop my own idea to solve lot of real world problems existing in IOT space(This may be for a Person, Business, NGO etc...). I'm looking to aware what best practices available already to share up my idea and collaborate like minded engineers to discuss various ideas to brings it next level as every day progress.

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Anurag K. updated status

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Anurag Kushwaha

I'm currently working on a project called DoorBell. This is a IoT virtual door bell that sends respose as an email or sms if someone knocks the door. Also it can send the picture of the person standing behind the door.

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jnc j. updated project Mini Operadora Bluetooth status

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jnc jnc

A Mini Operadora Bluetooth consta em substituir os ramais de empresas no que ocasiona em um baixo consumo de energia. Tendo com base sua distância de 10 metros, no que consegui expandi-la para o dobro ou mais a partir de um pequeno banco de dados que armazena e transfere a voz em tempo real.

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donel a. updated status

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donel adams

Google or Bing name to view associate networks fostering business development by maximizing any platform potential for growth utilizing AI

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Dhruv R. created project Business Card Scanner

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Business Card Scanner

Here’s how it works:

It uses Google’s Tesseract to built an Optical Character Recognition Engine inside the phone once installed, thus it works completely offline.

Then using Leptonica Image Processing and various other algorithms the image clicked is enhanced so as to best suite for the OCR purpose.

The engine then extracts the text which undergoes entity detection using Open Natural Language Processing(OpenNLP).

The entities are put under appropriate fields and the contact is saved in the phone directory along with the business card.

Using Parse as backend, Android Studio as IDE, stackoverflow as mentor I finally completed the app in 1 month time period.

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Dhruv R. updated status

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Dhruv Rathi

Fishes classification from under water is a huge problem for fishermen and survey guides, organisers and government itself. I am working on a new algorithm to classify fishes based on textures from underwater images into the corresponding species.

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Suraj R. created project Vehicle Detection

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Vehicle Detection

Detecting vehicles in a video stream is an object detection problem. An object detection problem can be approached as either a classification problem or a regression problem. In the classification approach, the image are divided into small patches, each of which will be run through a classifier to determine whether there are objects in the patch. The bounding boxes will be assigned to patches with positive classification results. In the regression approach, the whole image will be run through a convolutional neural network directly to generate one or more bounding boxes for objects in the images.

The goal of this project is to detect the vehicles in a camera video. The You Only Look Once (YOLO) algorithm is used here to detect the vehicles from a dash camera video stream. This feature is an extremely important breakthrough for self-driving cars as we can train the model to also recognize birds, people, stop signs, signals and much more.

In this project, we will implement the version 1 of tiny-YOLO in Keras, since it’s easy to implement and is reasonably fast.

The YOLO approach of the object detection is consists of two parts: the neural network part that predicts a vector from an image, and the postprocessing part that interpolates the vector as boxes coordinates and class probabilities. For the neural network, the tiny YOLO v1 is consist of 9 convolution layers and 3 full connected layers. Each convolution layer consists of convolution, leaky relu and max pooling operations. The output of this network is a 1470 vector, which contains the information for the predicted bounding boxes. The 1470 vector output is divided into three parts, giving the probability, confidence and box coordinates.

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Will Lewis

Cofounder of Rose City Games. Lead community organizer of Portland Indie Game Squad. Making AR/PC/tabletop indie games.

Portland, OR, USA

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Bob Duffy

Folsom, CA, USA