Fotonation Limited

Tobii Technologies Limited

Szabolcs Fulop

Corneliu Zaharia

Petronel Bigioi

Peter Corcoran

Stefan Petrescu

Mihnea Gangea

Vlad Georgescu

Mihai Constantin Munteanu

Gyorgy Simon

Vlad Ionut Ursachi

Radu Nicolau

Bogdan Sultana

Cristina Raceala

Nicolae Nicoara

Cian Ryan

Dragos Dinu

Oana Iovita

Joseph Lemley

Tudor Topoleanu

Radu Ene

A method for identifying a gesture from one of a plurality of dynamic gestures, each dynamic gesture comprising a distinct movement made by a user over a period of time within a field of view of an image acquisition device comprises iteratively: acquiring a current image from said image acquisition device at a given time; and passing at least a portion of the current image through a bidirectionally recurrent multi-layer classifier. A final layer of the multi-layer classifier comprises an output indicating a probability that a gesture from the plurality of dynamic gestures is being made by a user during the time of acquiring the image.

Method for identifying a gesture

An image processing system comprises a template matching engine (TME). The TME reads an image from the memory; and as each pixel of the image is being read, calculates a respective feature value of a plurality of feature maps as a function of the pixel value. A pre-filter is responsive to a current pixel location comprising a node within a limited detector cascade to be applied to a window within the image to: compare a feature value from a selected one of the plurality of feature maps corresponding to the pixel location to a threshold value; and responsive to pixels for all nodes within a limited detector cascade to be applied to the window having been read, determine a score for the window. A classifier, responsive to the pre-filter indicating that a score for a window is below a window threshold, does not apply a longer detector cascade to the window before indicating that the window does not comprise an object to be detected.

Image processing system

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 template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Face or other object detection including template matching

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Brasov, Romania

Szabolcs Fulop