Neuroimaging: connecting the pixels

PixelNet: Representation of the pixels, by the pixels, and for the pixels

We explore design principles for general pixel-level prediction problems, from low-level edge detection to midlevel surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fullyconvolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computational...

Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images

Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of depicted persons. Therefore, we aim for automated approaches to redact such private information and thereby protect privacy of the individual. By conducting a user study we find that obfuscating the image regions rel...

Polymorphic Pixels

We present an alternative to traditional texture compression techniques whereby a clear data channel is embedded within a texture using steganographic techniques. The resulting pixels are polymorphic – they can be used without decompression and the embedded data can be extracted and used for alternative purposes. The technique has fewer degenerate cases than DXT, does not suffer from high-frequ...

Medullary cavernous angioma with hemorrhage Neuroimaging Neuroimaging Neuroimaging Neuroimaging Neuroimaging Neuroimaging Neuroimaging Neuroimaging Pathology

Urban Pixels

“Urban Pixels” are wireless, solar-powered lighting units for cities that blur the boundary between digital display technology and traditional urban lighting. By combining a renewable energy source with RF communication it is possible to achieve a self-sustaining, distributed display network that can be attached to any building surface and reconfigured with ease. Depending on their configuratio...