Abstract: A method estimates an ego-motion of an apparatus between a first image and a second image of a succession of images captured by the apparatus, in a SLAM type algorithm containing a localization part including the ego-motion estimating and a mapping part. The ego-motion comprises a 3D rotation of the apparatus and a position variation of the apparatus in the 3D space, and the ego-motion estimating comprises performing a first part and performing a second part after having performed the first part, the first part including estimating the 3D rotation of the apparatus and the second part including, the 3D rotation having been estimated, estimating the position variation of the apparatus in the 3D space.

Abstract: A method determines a movement of an apparatus between capturing first and second images. The method includes testing model hypotheses of the movement by for example a RANSAC algorithm, operating on a set of first points in the first image and assumed corresponding second points in the second image to deliver the best model hypothesis. The testing includes, for each first point, calculating a corresponding estimated point using the tested model hypothesis, determining the back-projection error between the estimated point and the second point in the second image, and comparing each back projection error with a threshold. The testing comprises for each first point, determining a correction term based on an estimation of the depth of the first point in the first image and an estimation of the movement between the first and second images, and determining the threshold associated with the first point by using said correction term.

Abstract: A method determines a movement of an apparatus between capturing first and second images. The method includes testing model hypotheses of the movement by for example a RANSAC algorithm, operating on a set of first points in the first image and assumed corresponding second points in the second image to deliver the best model hypothesis. The testing includes, for each first point, calculating a corresponding estimated point using the tested model hypothesis, determining the back-projection error between the estimated point and the second point in the second image, and comparing each back projection error with a threshold. The testing comprises for each first point, determining a correction term based on an estimation of the depth of the first point in the first image and an estimation of the movement between the first and second images, and determining the threshold associated with the first point by using said correction term.

Abstract: A method generates a binary descriptor associated with a given point in a current frame of a succession of video frames obtained by an apparatus such as an image sensor. The method includes determining a pattern of points pairs around said given point in the current frame, and performing intensity comparison processing between the two points of each pair. The apparatus is likely to move in a rotation between the previous frame and the current frame. The method includes processing the pattern of points of the current frame with tridimensional rotation information representative of the apparatus rotation between the previous frame and the current frame and obtained from inertial measurements provided by at least one inertial sensor.

Abstract: Method of estimating a position variation of a motion of an apparatus between a first instant and a second instant, said motion including a rotation of the apparatus and said position variation, said position variation including a position and a velocity, wherein estimating said position variation comprises performing a particles filtering for estimating said position and velocity from the probabilistic-weighted average of the particles, said particles filter using a known estimation of said rotation and being parameterized for taking into account a quality of said rotation estimation.

Abstract: A method determines a movement of an apparatus between capturing first and second images. The method includes testing model hypotheses of the movement by for example a RANSAC algorithm, operating on a set of first points in the first image and assumed corresponding second points in the second image to deliver the best model hypothesis. The testing includes, for each first point, calculating a corresponding estimated point using the tested model hypothesis, determining the back-projection error between the estimated point and the second point in the second image, and comparing each back projection error with a threshold. The testing comprises for each first point, determining a correction term based on an estimation of the depth of the first point in the first image and an estimation of the movement between the first and second images, and determining the threshold associated with the first point by using said correction term.

Abstract: The method includes for each current pair of first and second successive video images determining movement between the two images. The determining includes a phase of testing homography model hypotheses on the movement by a RANSAC type algorithm operating on a set of points in the first image and first assumed corresponding points in the second image so as to deliver one of the homography model hypothesis that defines the movement. The test phase includes a test of first homography model hypotheses of the movement obtained from a set of second points in the first image and second assumed corresponding points in the second image. At least one second homography model hypothesis is obtained from auxiliary information supplied by an inertial sensor and representative of a movement of the image sensor between the captures of the two successive images of the pair.

Abstract: A method estimates an ego-motion of an apparatus between a first image and a second image of a succession of images captured by the apparatus, in a SLAM type algorithm containing a localization part including the ego-motion estimating and a mapping part. The ego-motion comprises a 3D rotation of the apparatus and a position variation of the apparatus in the 3D space, and the ego-motion estimating comprises performing a first part and performing a second part after having performed the first part, the first part including estimating the 3D rotation of the apparatus and the second part including, the 3D rotation having been estimated, estimating the position variation of the apparatus in the 3D space.

Abstract: Method of estimating a position variation of a motion of an apparatus between a first instant and a second instant, said motion including a rotation of the apparatus and said position variation, said position variation including a position and a velocity, wherein estimating said position variation comprises performing a particles filtering for estimating said position and velocity from the probabilistic-weighted average of the particles, said particles filter using a known estimation of said rotation and being parameterized for taking into account a quality of said rotation estimation.

Abstract: A method determines a movement of an apparatus between capturing first and second images. The method includes testing model hypotheses of the movement by for example a RANSAC algorithm, operating on a set of first points in the first image and assumed corresponding second points in the second image to deliver the best model hypothesis. The testing includes, for each first point, calculating a corresponding estimated point using the tested model hypothesis, determining the back-projection error between the estimated point and the second point in the second image, and comparing each back projection error with a threshold. The testing comprises for each first point, determining a correction term based on an estimation of the depth of the first point in the first image and an estimation of the movement between the first and second images, and determining the threshold associated with the first point by using said correction term.

Abstract: A method generates a binary descriptor associated with a given point in a current frame of a succession of video frames obtained by an apparatus such as an image sensor. The method includes determining a pattern of points pairs around said given point in the current frame, and performing intensity comparison processing between the two points of each pair. The apparatus is likely to move in a rotation between the previous frame and the current frame. The method includes processing the pattern of points of the current frame with tridimensional rotation information representative of the apparatus rotation between the previous frame and the current frame and obtained from inertial measurements provided by at least one inertial sensor.

Abstract: Methods of processing a frame in a video sequence of digital images are provided. The methods include determining a global motion vector for the frame relative to a previous frame in the sequence, deriving a jitter function from the global motion vector, the jitter function including an estimate of undesired motion of the frame relative to the previous frame, and determining whether the frame is blurred above a first predetermined threshold. If the frame is blurred above the first predetermined threshold, stabilizing the frame using the jitter function and applying a deblur function to the frame.

Abstract: The method includes for each current pair of first and second successive video images determining movement between the two images. The determining includes a phase of testing homography model hypotheses on the movement by a RANSAC type algorithm operating on a set of points in the first image and first assumed corresponding points in the second image so as to deliver one of the homography model hypothesis that defines the movement. The test phase includes a test of first homography model hypotheses of the movement obtained from a set of second points in the first image and second assumed corresponding points in the second image. At least one second homography model hypothesis is obtained from auxiliary information supplied by an inertial sensor and representative of a movement of the image sensor between the captures of the two successive images of the pair.

Abstract: A method for detecting faces in an image having a plurality of picture elements, each having a plurality of color components in a predetermined color space, includes determining an extended range for color component values, in the color space, in which a skin tone area is likely to be detected, defining intervals for the color component values, in the color space, covering at least part of the extended range, and scanning each of the intervals to detect a skin tone area. If a skin tone area is detected, the method includes selecting the intervals in which a skin tone area is detected, defining candidate limited ranges for color component values, in the color space, from the selected intervals, performing face detection on a skin tone area in at least some of the candidate limited ranges, and selecting a chosen candidate limited range based on the number of faces detected.

Abstract: A control panel comprising a cover that is equipped with at least one zone for detecting tactile contact forming a control button, in which a force sensor comprising a pressure-sensitive zone is arranged between the cover and the support so as to detect actuation of the control button, the force of the tactile contact being transmitted axially to the sensitive zone via a spacer plate made of an elastically compressible material that is inserted between the sensor and the cap, characterized in that the internal surface of the cap comprises a series of protruding elements that are distributed over the internal surface of the cap opposite the sensitive zone so as to form a number of regularly distributed zones of localized overpressure.

Abstract: There is described a method for facial features detection in a picture frame containing a skin tone area, comprising dividing (12) the skin tone area into a number of parts; and for each part of the skin tone area, constructing (14) a luminance map, constructing an edge map by extracting (18) edges from the luminance map, defining (20) an edge magnitude threshold, building (22) a binary map from the edge map by keeping only the edges having a magnitude beyond the defined edge magnitude threshold and eliminating the others; and then extracting (24) facial features from the built binary map. An inter-related facial features detector is further described.

Abstract: Methods of processing a frame in a video sequence of digital images are described, the methods comprising: determining a global motion vector for the frame relative to a previous frame in the sequence; deriving a jitter function from the global motion vector, the jitter function comprising an estimate of undesired motion of the frame relative to the previous frame; determining whether the frame is blurred above a first predetermined threshold; and if so, stabilising the frame using the jitter function and applying a deblur function to the frame.

Abstract: Control panel including a cover mounted on a support that is provided with a tactile pressing detection zone in which a force sensor that includes a pressure-sensitive zone is arranged behind a detection zone between the cover and the support so as to produce an electrical control signal when a user applies a determined tactile pressing force to the detection zone. The tactile pressing force is transmitted axially (X1) towards the sensitive zone via a spacer made of elastically compressible material interposed between the sensor and the cover. The spacer includes at least one compressible portion that defines a transversal top surface that bears against the cover and a transversal bottom surface that bears against the sensitive zone of the sensor. The top surface has an area smaller than the area of the bottom surface.

Abstract: Pressure sensitive transducer assembly that includes a force sensing resistor. The force sensing resistor includes: first and second substrates; at least a first and a second electrically conductive traces on the inner surface of the first substrate including interdigitated fingers defining a sensitive area; and a resistive layer facing the sensitive area. The force sensing resistor includes an auxiliary trace on the inner surface of the first substrate connecting the first trace to the second trace through a constant resistance that is not dependent on the force applied to the substrates. The constant resistance being of a value largely greater than the value of the variable resistance which can be measured indirectly between the fingers when an external force is applied to the substrates. A system and a control method are also proposed.

Abstract: A method for producing a panoramic image based on a video sequence includes merging a target image of a video sequence with a panoramic pan made up of images of the video sequence. The method includes evaluating the luminance difference between the target image and the last image used for the panoramic pan, determining the size of a zone the two images will be merged as a function of the evaluated luminance difference, and correcting the luminance of the pixels situated in the merging zone.