Ronell Sicat

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We introduce a new type of multi-resolution image pyramid for high-resolution images called <i>sparse pdf maps</i> (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations(More)
This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse(More)
Volume segmentation is an integral data analysis tool in experimental science. For example, in neuroscience, analysis of 3D volumes of neural structures from electron microscopy data is a key analysis step. Despite advances in computational methods, experts still prefer to manually proofread and correct the automatic segmentation outputs. Such corrections(More)
We report on a controlled user study comparing three visualization environments for common 3D exploration. Our environments differ in how they exploit natural human perception and interaction capabilities. We compare an augmented-reality head-mounted display (Microsoft HoloLens), a handheld tablet, and a desktop setup. The novel head-mounted HoloLens(More)
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