Fotonation Limited

Directly Software, Inc.

Directly, Inc.

Vlad Georgescu

Corneliu Zaharia

Mihai Constantin Munteanu

Jeff Patterson

Piotr Stec

Jean Tessier

Scott Golubock

Stephen D Hsu

Ruben Kislaki

Petronel Bigioi

Jason Fama

Joe Lemley

Szabolcs Fulop

Eugene Mandel

Antony Brydon

Gyorgy Simon

Lorant Bartha

Iulia Suciu

Antony Fenwick Brydon

Alexei Pososin

Tanausu Cerdeña

A method of producing an image frame from event packets received from an event camera comprises: forming a tile buffer sized to accumulate event information for a subset of image tiles, the tile buffer having an associated tile table that determines a mapping between each tile of the image frame for which event information is accumulated in the tile buffer and the image frame. For each event packet: an image tile corresponding to the pixel location of the event packet is identified; responsive to the tile buffer storing information for one other event corresponding to the image tile, event information is added to the tile buffer; and responsive to the tile buffer not storing information for another event corresponding to the image tile and responsive to the tile buffer being capable of accumulating event information for at least one more tile, the image tile is added to the tile buffer.

Event camera hardware

A method for correcting an image divides an output image into a grid with vertical sections of width smaller than the image width but wide enough to allow efficient bursts when writing distortion corrected line sections into memory. A distortion correction engine includes a relatively small amount of memory for an input image buffer but without requiring unduly complex control. The input image buffer accommodates enough lines of an input image to cover the distortion of a single most vertically distorted line section of the input image. The memory required for the input image buffer can be significantly less than would be required to store all the lines of a distorted input image spanning a maximal distortion of a complete line within the input image.

Method and system for correcting a distorted input image

An image processing apparatus comprises a normalisation module operatively connected across a bus to a memory storing an image in which a region of interest (ROI) has been identified within the image. The ROI is bound by a rectangle having a non-orthogonal orientation within the image. In one embodiment, the normalisation module is arranged to divide the ROI into one or more slices, each slice comprising a plurality of adjacent rectangular tiles. For each slice, the apparatus successively reads ROI information for each tile from the memory including: reading a portion of the image extending across at least a width of the slice line-by-line along an extent of a slice. For each tile, the apparatus downsamples the ROI information to a buffer to within a scale S&#x3c;2 of a required scale for a normalised version of the ROI. The apparatus then fractionally downsamples and rotates downsampled information for a tile within the buffer to produce a respective normalised portion of the ROI at the required scale for the normalised ROI. Downsampled and rotated information is accumulated for each tile within a normalised ROI buffer for subsequent processing by the image processing apparatus.

Image processing apparatus

A method for producing a histogram of oriented gradients (HOG) for at least a portion of an image comprises dividing the image portion into cells, each cell comprising a plurality of image pixels. Then, for each image pixel of a cell, obtaining a horizontal gradient component, gx, and a vertical gradient component, gy, based on differences in pixel values along at least a row of the image and a column of the image respectively including the pixel; and allocating a gradient to one of a plurality of sectors, where n is a sector index, each sector extending through a range of orientation angles and at least some of the sectors being divided from adjacent sectors according to the inequalities: b*16&#x3c;a*(n+1) where 0&#x2264;n&#x2264;5, b=gy and a=gx; and b*16&#x3c;a*(13&#x2212;n) where 7&#x2264;n&#x2264;12, a=gx+gy and b=gx&#x2212;gy. At least one sector is associated with a bin; and each instance of a pixel gradient of a cell associated with a bin is accumulated to provide a HOG for the cell.

Method for producing a histogram of oriented gradients

In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service inquiries. Multiple experts may provide responses to a particular customer service inquiry, optionally within a single chat window.

CRM including multi-thread messaging

In a crowd sourcing approach, responses to customer service inquiries are provided by routing the inquiries to an independent group of experts. Machine learning based systems are used in combination with contact center staff and/or the independent experts to resolve the customer service inquiries. The AI based systems can be configured to determine which of the customer service inquiries should be routed to the independent experts, and/or to generate content for inclusion in responses to the customer service inquiries. The machines systems are optionally trained based on scored responses to prior customer service inquiries. These scored responses can include content provided by a machine learning system and/or a human expert.

Al based CRM system

In a crowd sourcing approach, responses to customer service inquiries are provided by routing a subset of the inquiries to an independent group of experts. The customer service inquiries are optionally routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. Embodiments include methods of classifying customer service inquiries, training a machine learning system, and/or processing customer service requests.

Methods of AI based CRM

A method for producing a histogram of oriented gradients (HOG) for at least a portion of an image comprises dividing said image portion into cells, each cell comprising a plurality of image pixels. For each image pixel of a cell, a horizontal gradient component, gx, and a vertical gradient component, gy, is obtained based on differences in pixel values along at least a row of said image and a column of said image respectively including the pixel. A gradient is allocated to one of a plurality of sectors, each sector extending through a range of orientation angles. At least some of said sectors are divided from adjacent sectors along lines including gx=2&#xb7;gy, where n is any integer value with a magnitude greater than or equal to 1. At least one sector is associated with a bin; and a count of each instance of a pixel gradient of a cell associated with a bin is performed to provide a HOG for said cell.

In a crowd sourcing approach, responses to customer service inquiries are provided by providing the inquiries to an independent group of experts. These experts are typically able to resolve the inquiry before the inquiries are routed to a traditional contact center. The customer service inquiries are routed to specific experts based on matches between identified subject matter of the inquiries and expertise of the experts. In some embodiments, more than one expert can contribute to the resolution of an inquiry.

Expert based customer service

Customer service routing

A method for correcting a distorted input image comprises determining a local region of an image to be displayed and dividing said region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within said input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of said input image sufficient to span said boundary of said distorted tile are determined. Successive rows of the distorted input from between said maximum and minimum addresses are read. Distortion of the non-rectangular portion of said distorted input image is corrected to provide a tile of a corrected output image which is stored.

Growing community of inventors

Brasov, Romania

Vlad Georgescu