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US 10,769,483 B2Granted
Retinal encoder for machine vision
AssigneeCornell University
InventorsShelia Nirenberg, Illya Bomash
Life of this patent
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Abstract
A method is disclosed including: receiving raw image data corresponding to a series of raw images; processing the raw image data with an encoder to generate encoded data, where the encoder is characterized by an input/output transformation that substantially mimics the input/output transformation of one or more retinal cells of a vertebrate retina; and applying a first machine vision algorithm to data generated based at least in part on the encoded data.
Claims17
A method including:
- applying, by an encoding module, a spatiotemporal transformation to image data to generate retinal output cell response values;
- generating, by the encoding module, encoded data based on the retinal output cell response values;
- applying, by a machine vision module, a machine vision algorithm to the encoded data;
- monitoring, by a controller, performance of the machine vision algorithm, wherein monitoring the performance of the machine vision algorithm comprises:
- calculating an error rate of the machine vision algorithm;
- and
- comparing the error rate to a threshold level;
- and
- adjusting, by the controller, the machine vision algorithm based on the monitored performance.
6 more elements
A method including:
- applying, by an encoding module, a spatiotemporal transformation to image data to generate retinal output cell response values;
- generating, by the encoding module, encoded data based on the retinal output cell response values;
- applying, by a machine vision module, a machine vision algorithm to the encoded data;
- monitoring, by a controller, performance of the machine vision algorithm, wherein monitoring the performance of the machine vision algorithm comprises:
- calculating an error rate of the machine vision algorithm;
- and
- comparing the error rate to a threshold level;
- and
- adjusting, by the controller, the machine vision algorithm based on the monitored performance, wherein adjusting the machine vision algorithm comprises modifying a parameter of the machine vision algorithm, and wherein the machine vision algorithm comprises an artificial neural network, and wherein adjusting the machine vision algorithm comprises changing a plurality of connections in an artificial neural network.
6 more elements
A method including:
- applying, by an encoding module, a spatiotemporal transformation to image data to generate retinal output cell response values;
- generating, by the encoding module, encoded data based on the retinal output cell response values;
- applying, by a machine vision module, a machine vision algorithm to the encoded data:
- monitoring, by a controller, performance of the machine vision algorithm, wherein monitoring the performance of the machine vision algorithm comprises:
- calculating an error rate of the machine vision algorithm;
- and
- comparing the error rate to a threshold level;
- and
- adjusting, by the controller, the machine vision algorithm based on the monitored performance, wherein adjusting the machine vision algorithm comprises iteratively adjusting one or more parameters of the machine vision algorithm until an incremental increase in performance per iteration falls below a threshold level.
6 more elements
An apparatus including:
- a memory storage device configured to store image data corresponding to a series of images;
- and
- a processor operably coupled with the memory and programmed to:
- receive the image data corresponding to the series of images;
- generate encoded data from the image data, wherein, to generate the encoded data, the processor is configured to:
- apply a spatiotemporal transformation to image data to generate retinal output cell response values;
- generate encoded data based on the retinal output cell response values;
- apply a machine vision algorithm to the encoded data;
- monitor performance of the machine vision algorithm, wherein, to monitor the performance of the machine vision algorithm, the processor is further configured to:
- calculate an error rate of the machine vision algorithm;
- and
- compare the error rate to a threshold level;
- and
- adjust the machine vision algorithm based on the monitored performance.
11 more elements
A non-transitory computer-readable medium having computer-executable instructions for implementing operations comprising
- applying a spatiotemporal transformation to image data to generate retinal output cell response values;
- generating encoded data based on the retinal output cell response values;
- applying a machine vision algorithm to the encoded data;
- monitoring performance of the machine vision algorithm;
- and
- adjusting the machine vision algorithm based on the monitored performance, wherein adjusting the machine vision algorithm comprises iteratively adjusting one or more parameters of the machine vision algorithm until an incremental increase in performance per iteration of the machine vision algorithm falls below a threshold level.
3 more elements
Dependencies are inferred from the claim text.