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2020 Vision Product of the Year for Best AI Technology awarded to Morpho, powered by Qualcomm Snapdragon

Jun 8, 2020

Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries.

Toshi Torihara, vice president of Morpho US, Inc. demos the Morpho Semantic Filter on a Snapdragon 865 prototype smartphone live at the 2019 Qualcomm Snapdragon Tech Summit.

The Qualcomm Snapdragon 865 5G Mobile Platform is loaded with advanced CV and AI technology that enables our customers and partners to harness and create bleeding edge camera experiences. Qualcomm Technologies’ partner Morpho did just that and has been awarded a “2020 Vision Product of the Year Award” at this year’s virtual Embedded Vision Summit for their “Morpho Semantic Filtering” technology product, which was showcased on a Snapdragon 865 smartphone . 

The annual Embedded Vision Summit, founded in 2012, is a conference for showcasing camera innovations that use CV and AI. In the past few years, Morpho has been a star at this show – this year’s award is its 3rd in a row.  The company was previously awarded “2019 Vision Product of the Year” for its Morpho Video Processing Software and won “2018 Vision Product of the Year” award for its Morpho SoftNeuro deep learning AI. 

The “Morpho Semantic Filtering” demoed on a Snapdragon 865 smartphone, was awarded the “2020 Vision Product of the Year Award.”  Morpho’s Semantic Filter uses AI to recognize the meaning of pixels in an image and improve image quality.   

Watch a demo of the Morpho Semantic Filter: 

Morpho describes their Semantic Filter as follows: 

“Typical conventional image quality improvement technologies apply a single image processing method, globally to the entire image. When such technologies are used, if noise reduction is applied to the entire image, for example, noise is reduced from the entire image but the details and the texture of the objects within the image that should be preserved may be lost. 

The Semantic Filter technology showcased at this time uses a semantic segmentation technology that uses AI to recognize the meaning of respective pixels in an image, sorts the pixels in the image by category, and performs image processing, such as noise reduction, dynamic range compensation, edge enhancement, and defocusing appropriately for each category. The new technology thus realizes an improvement of image quality, with its ability to process images to increase clarity, and prevent deterioration of the details and texture of each object within an image that should be preserved.” 

The Morpho Semantic Filter software was optimized for Snapdragon 865 so it could improve speed and quality of the segmentation and filtering algorithms. The software leverages the speed of the Qualcomm Spectra 480 Image Signal Processor and 5th Generation Qualcomm Artificial Intelligence Engine.  

The Morpho Semantic Filter and the Snapdragon optimizations were first unveiled at Qualcomm Tech Summit in December of 2019.  See the videos below for a live demonstration of the Morpho Semantic Filter at Tech Summit (jump to 1:35:45.)

 

Congrats to Morpho! You can read more about the company and its Morpho Semantic Filtering on their website.

 

Qualcomm Snapdragon, Qualcomm Spectra and Qualcomm AI Engine are products of Qualcomm Technologies, Inc and/or its subsidiaries.

 

Opinions expressed in the content posted here are the personal opinions of the original authors, and do not necessarily reflect those of Qualcomm Incorporated or its subsidiaries ("Qualcomm"). Qualcomm products mentioned within this post are offered by Qualcomm Technologies, Inc. and/or its subsidiaries. The content is provided for informational purposes only and is not meant to be an endorsement or representation by Qualcomm or any other party. This site may also provide links or references to non-Qualcomm sites and resources. Qualcomm makes no representations, warranties, or other commitments whatsoever about any non-Qualcomm sites or third-party resources that may be referenced, accessible from, or linked to this site.

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