SnapFind is a sample domain-specific search application built on the OpenDiamond® platform. SnapFind enables users to quickly search through collections of unlabeled photographs (such as holiday photos). Our motivation is that digital cameras allow users to generate thousands of photos, yet few users have the patience to manually index them. Users typically want to find photos by content, but computers are not yet smart enough to understand the semantics of an image. SnapFind interactively filters images using a variety of pre-defined and user-trained filters. Partial results from earlier searches are used to refine subsequent queries allowing the user to iteratively refine the query as needed.
The current implementation of SnapFind supports user-defined color and visual texture filters that scan regions in every image. The user can create and refine these filters using image patches. SnapFind also supports popular shape-based filters such as face and pedestrian detectors (available in OpenCV). Many computer vision algorithms are well-suited for implementation on OpenDiamond since they can be treated as a cascade of filter stages. This enables much of the search to be downloaded to the storage node, where unpromising candidates can be quickly discarded.
The task of manually counting adipocytes (fat cells) in cell microscopy images and characterizing their size is very time consuming. FatFind is a Diamond application, developed in collaboration with Merck Research, that exploits the almost perfectly circular shape of adipocytes in solution to efficiently locate fat cells.
ISAD is a collaborative research effort with the University of Pittsburgh and the University of Pittsburgh Medical Center.
The goal of ISAD is to enable doctors to make better decisions about a given case by providing a selection of similar annotated cases from a large database. For instance, a radiologist examining a suspicious mass could study labeled mammograms with similar conditions and weigh the outcome of their biopsy results before determining whether to recommend a biopsy. The fundamental challenge in developing ISAD systems is the identification of similar cases, not simply in terms of superficial image characteristics, but in a medically-relevant sense.
Additional
information about ISAD and the collaboration.