Immervision, Inc.

Julie Buquet

Simon Thibault

Jocelyn Parent

Patrice Roulet

Pierre Konen

Pascale Nini

Valentin Bataille

Zhenfeng Zhuang

Manuel Magini

Viacheslav Natashyn

Zhiqiang Miao

A method for training and using a convolutional neural network with images having on-purpose distortion is disclosed. By distorting on-purpose the image to create zone of interest with higher number of pixels, the resulting output from the neural network is improved. The distorted images are created using an imager device either comprising an optical system specifically designed to output distorted images or comprising software or hardware image distortion manipulation algorithm to create the distorted images from normal images. Also described is a method for training the neural network using a distorted image generator from various existing datasets.

Using imager with on-purpose controlled distortion for inference or training of an artificial intelligence neural network

A method for controlling an optical system including at least one active light-controlling element to better control the illumination of a scene when imaged on an image sensor is presented. This active light-controlling element is used to control the amount of light in an image zone depending on an electric signal from a controller. This allows imaging scenes with bright objects without over-exposure or scenes with dark objects without under-exposure on the image sensor. After image processing to reverse the effect of the active light-controlling element, the resulting processed images have an enhanced depth without the drawbacks associated to traditional HDR imaging. The method can use any shape of sub-elements in the active light-controlling element depending on the application. The method can also be used with multiple active light-controlling surfaces to further control the light on the image sensor, including separating the colors in the image.

Adaptive relative illumination optical system

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Montreal, Canada

Julie Buquet