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Their new chosen direction was helping consumers organize (store, search, classify) their smart phone pictures in the cloud. Blank's book) and therefore they decided to `pivot' and develop a new business plan. After developing an MVP and carrying out about 60 interviews the grantees decided that they had not found an `earlyvangelist' (cfr S. detect all cancerous cells in a microscope slide, count all cars in a parking lot from satellite imagery).

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The grantees initially had a plan to develop software to allow scientists and commercial entities to train a computer system to annotate images automatically (e.g. The team was called `Anchovi'.Īnchovi's technology was based on trainable computer vision systems developed in Perona's lab at Caltech and on techniques for crowdsourcing visual tasks also developed by Perona and collaborators at Caltech. Three people participated: the technical lead (B. This included traveling to Stanford University to attend classes and participate in class-related activities, developing a canvas-based business model, developing a minimum viable product, holding about 120 in-person interviews with potential customers. The award covered the cost of attending Steve Blank's entrepreneurship class organized by the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation NSF has not approved or endorsed its content. This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. By combining computer vision and machine learning automations with humans (both experts and non-expert annotators), the system promises to be quickly configurable and trainable across virtually any image analysis challenge. The proposed effort will address the need to scale annotation and analysis of this data while keeping the process as inexpensive and fast as possible with today's computational power.

This is true for the scientific, research, geospatial information systems and consumer markets. As imaging becomes more available and storage inexpensive, the amount of image data will continue to increase. To demonstrate effectiveness and potential commercial opportunity, the team will develop a prototype and a business model around this approach. The team's research suggests that it is possible to combine the complementary strengths of human annotators and machines into a hybrid system that is flexible, accurate, fast and inexpensive. In recent years, a complementary solution has become available: crowdsourcing, that is, dynamically recruiting thousands of people to carry out an assigned task from their computer. However, accuracy is not yet sufficient for many applications. Machine vision is making progress in these arenas. Primary Place of Performance Congressional District:Īnnotating a large body of images quickly, accurately and inexpensively would be a valuable capability in scientific, medical and other commercial applications. Pietro Perona (Principal Investigator) Sponsored Research Office:.I-Corps: Combining Machine Vision and Crowdsourcing for Convenient and Accurate Image Annotation NSF Org:Įrrol Arkilic TI Translational Impacts TIP Dir for Tech, Innovation, & Partnerships

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