Parrot

Parrot Drones

Prophesee

Gaspard Florentz

Henri Seydoux

Christoph Posch

Stephane Valente

Manuele Brambilla

Geoffrey F Burns

The present disclosure generally relates to systems and methods for imaging and sensing vibrations of an object. In one implementation, a system is provided for detecting vibrations that comprises a sensor with a plurality of pixels, each pixel including at least one photosensitive element. The sensor is configured to output signals related to illumination on each pixel. The system also includes at least one processor configured to receive the output signals from the sensor and detect, based on the output signals, one or more polarities. The at least one processor is also configured to determine timestamps associated with the output signals, analyze the timestamps and the polarities to detect vibrations of an object in the field of view of the sensor, and determine one or more frequencies of the detected vibrations.

Systems and methods for imaging and sensing vibrations

This system comprises a drone and a remote station with virtual reality glasses rendering images transmitted from the drone, and provided with means for detecting changes of orientation of the user's head. The drone generates a 'viewpoint' image (P') and a 'bird's eye' image (P&#x2032;) whose field is wider and whose definition is lower. When the sight axis of the &#x201c;viewpoint&#x201d; image is modified in response to changes of position of the user's head, the station generates locally during the movement of the user's head a combination of the current &#x201c;viewpoint&#x201d; and &#x201c;bird's eye&#x201d; images, with outlines (CO&#x2032;) adjusted as a function of the changes of position detected by the detection means.

Video system for piloting a drone in immersive mode

A camera () produces a sequence of images () processed by a point of interest search algorithm () that is parameterizable with a detection threshold (&#x3c4;) such that the number (N) of points of interest detected in the image varies as a function of the threshold level. The characteristic giving the number (N) of detected points of interest as a function of the threshold (&#x3c4;) is modelled by a square root decreasing exponential function, which is dynamically parameterizable with values linked to the image to be analyzed. The method comprises the steps of: a) determining () values of parameterization of the decreasing exponential function for the current image; b) predicting (), for this current image, an optimum value of the threshold by using the modelled characteristic, parameterized with the values determined at step a); and c) applying (), for at least one later image, the point of interest search algorithm with the optimum threshold value (&#x3c4;) computed at step b).

Method of detection of points of interest in a digital image

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Paris, France

Gaspard Florentz