Iee International Electronics and Engineering S.a.

Bruno Mirbach

Thomas Solignac

Frederic Garcia Becerro

Frédéric Grandidier

Cedric Schockaert

Romuald Ginhoux

Marta Castillo Franco

Martin Boguslawski

Laurent Lamesch

Harald Clos

Roberto Orsello

Djamila Aouada

Pandu Devarakota

Frederic Garcia

A method for depth measurement with a time-of-flight camera using amplitude-modulated continuous light by acquiring for each of a plurality of pixels of a sensor array of the camera at least one sample sequence having at least four amplitude samples (A, A, A, A) at a sampling frequency higher than a modulation frequency of the amplitude-modulated continuous light. The method further includes: determining for each sample sequence of each pixel a confidence value (C) indicating a degree of correspondence of the amplitude samples (A, A, A, A) with a sinusoidal time evolution of the amplitude; and determining for each of a plurality of binning areas, each of which comprises a plurality of pixels, a binned depth value (D) based on the amplitude samples (A, A, A, A) of sample sequence of pixels from the binning area, wherein the contribution of a sample sequence to the binned depth value (D) depends on its confidence value (C).

Method for depth measurement with a time-of-flight camera using amplitude-modulated continuous light

A method for corrected depth measurement with a time-of-flight camera using amplitude-modulated continuous light. In order to enable an accurate and efficient depth measurement with a time-of-flight camera, the method includes, for each of a plurality of pixels of a sensor array of the camera: acquiring with the camera a raw depth value rfor the pixel; and automatically calculating a ground truth value raccording to: r=g(r&#x2212;c)+c, to correct a systematic depth error of the raw depth value r, wherein cis a pixel-dependent first offset, g is a pixel-independent first function and cis a pixel-independent second offset.

Method for corrected depth measurement with a time-of-flight camera using amplitude-modulated continuous light

A method and system for real-time motion artifact handling and noise removal for time-of-flight (ToF) sensor images. The method includes: calculating values of a cross correlation function c(&#x3c4;) at a plurality of temporally spaced positions or phases from sent (s(t)) and received (r(t)) signals, thereby deriving a plurality of respective cross correlation values [c(&#x3c4;), c(&#x3c4;), c(&#x3c4;), c(&#x3c4;)]; deriving, from the plurality of cross correlation values [c(&#x3c4;), c(&#x3c4;), c(&#x3c4;), c(&#x3c4;)], a depth map D having values representing, for each pixel, distance to a portion of an object upon which the sent signals (s(t)) are incident; deriving, from the plurality of cross correlation values [c(&#x3c4;), c(&#x3c4;), c(&#x3c4;), c(&#x3c4;)], a guidance image (I; I'); and generating an output image D&#x2032; based on the depth map D and the guidance image (I; I&#x2032;), the output image D&#x2032; comprising an edge-preserving and smoothed version of depth map D, the edge-preserving being from guidance image (I; I&#x2032;).

Method and system for real-time motion artifact handling and noise removal for ToF sensor images

A camera system comprises a 3D TOF camera for acquiring a camera-perspective range image of a scene and an image processor for processing the range image. The image processor contains a position and orientation calibration routine implemented therein in hardware and/or software, which position and orientation calibration routine, when executed by the image processor, detects one or more planes within a range image acquired by the 3D TOF camera, selects a reference plane among the at least one or more planes detected and computes position and orientation parameters of the 3D TOF camera with respect to the reference plane, such as, e.g., elevation above the reference plane and/or camera roll angle and/or camera pitch angle.

3D time-of-flight camera system and position/orientation calibration method therefor

A method for determining a distance comprises: providing at least two phase measurements made with modulated light of different modulation wavelengths, each phase measurement being indicative of the distance up to an integer multiple of a respective modulation wavelength; providing a set of possible wraparound count combinations; for each one of the possible wraparound count combinations, calculating a combination of unwrapped phase hypotheses corresponding to the at least two phase measurements; and selecting a most plausible combination of unwrapped phase hypotheses among the combinations of unwrapped phase hypotheses and calculating the distance based upon the selected most plausible combination of unwrapped phase hypotheses.

Distance determination method

A method and system for real-time noise removal and image enhancement of high-dynamic range (HDR) images. The method includes receiving an HDR input image I and operating processing circuitry for (i) applying a first edge-preserving filter (e.g. guided filter) to the input image I, thereby generating a first image component Band a first set of linear coefficients &#x101;; (ii) applying a second edge-preserving filter (e.g. guided filter) to the input image I, thereby generating a second image component Band a second set of linear coefficients &#x101;; (iii) generating a plausibility mask P from a combination of the first set of linear coefficients &#x101;and the second set of linear coefficients &#x101;, the plausibility mask P indicating spatial detail within the input image I; and (iv) generating an output image O based on first image component B, the second image component Band the plausibility mask P.

Method and system for real-time noise removal and image enhancement of high-dynamic range images

A method for enhancing a depth image of a scene, comprises calculating an enhanced depth image by blending a first filtered depth image with a second filtered depth image or with the original depth image. The blending is achieved by application of a blending map, which defines, for each pixel, a contribution to the enhanced depth image of the corresponding pixel of the first filtered depth image and of the corresponding pixel of either the second filtered depth image or the original depth image. For pixels in the depth image containing no depth value or an invalid depth value, the blending map defines a zero contribution of the corresponding pixel of the second filtered depth image and a 100% contribution of the corresponding pixel of the first filtered image.

Depth image enhancement method

A dynamic reference range image generation method comprises providing a reference range image, to be dynamically updated, composed of pixels, each of which contains a reference range value. An acquired range image is provided, the pixels of which contain each a measured range value, the measured range values being updated at a predetermined rate. Pixels of the acquired range image containing an invalid measured range value are accordingly marked. The measured range value of each pixel of the acquired range image not marked as containing an invalid measured range value is compared with the reference range value of the corresponding pixel of the reference range image. The reference range value of that pixel of the reference range image is updated e.g. to the measured range value or to an average of the measured range value and one or more prior measured range values if a) the measured range value is considered less than the reference range value and has remained substantially constant for a first time period, or if b) the measured range value is considered greater than the reference range value and has remained substantially constant for a second time period smaller than the first time period. If neither of conditions a) and b) is fulfilled, the reference range value is kept substantially constant instead.

Real-time dynamic reference image generation for range imaging system

A method for contamination detection in a time-of-flight (TOF) range imaging system is described, wherein the TOF range imaging system receives light reflected from a scene, through an optical interface, on an imager sensor having an array of pixels, and wherein distance information and amplitude information are determined for the sensor pixels. The presence of contamination on the optical interface is determined on the basis of amplitude information determined for the sensor pixels.

Method for contamination detection in a TOF range camera

A method for matching the pixels (--) of a first range image of a scene () as seen from a first point of sight () with pixels (--) of a second range image of the scene as seen from a second point of sight () comprises the following steps: providing the first range image as a grid of source pixels (), on which the scene is mapped in accordance with a first projection associated with the first point of sight, wherein each source pixel has a point in the scene projected thereon in accordance with the first projection and has associated therewith a range value determined for that point in the scene; providing a grid of target pixels () for the second range image and a second projection associated with the second point of sight; and for each one of the target pixels, a) determining which source pixel would have the same point (P, P) in the scene projected thereon in accordance with the first projection as the target pixel would have projected thereon in accordance with the second projection if the imaged scene were a planar surface at a certain surface distance from the first point of sight; b) determining a depth coordinate of the point in the scene that the source pixel determined in step a) actually has projected thereon in accordance with the first projection; c) if the depth coordinate is greater than a threshold, which is itself greater than the surface distance, repeating steps a), b) and c) with an increased surface distance at step a) and an increased threshold at step c), and else associating the target pixel with the source pixel determined in step a).

Range image pixel matching method

A method of recording 3D images of a scene based on the time-of-flight principle is described. The method includes illuminating a scene by emitting light carrying an intensity modulation, imaging the scene onto a pixel array using an optical system, detecting, in each pixel, intensity-modulated light reflected from the scene onto the pixel and determining, for each pixel, a distance value based on the phase of light detected in the pixel. The determination of the distance values includes a phase-sensitive de-convolution of the scene imaged onto the pixel array such that phase errors induced by light spreading in the optical system are compensated for.

Recording of 3D images of a scene with phase de-convolution

Method for determining the position of an object point in a scene from a digital image thereof acquired through an optical system is presented. The image comprises a set of image points corresponding to object points and the position of the object points are determined by means of predetermined vectors associated with the image points. The predetermined vector represents the inverted direction of a light ray in the object space that will produce this image point through the optical system comprising all distortion effects of the optical system.

Method for determining the position of an object from a digital image

For assigning a test pattern to a class chosen from a predefined set of classes, the class membership probability for the test pattern is calculated as well as the confidence interval for the class membership probability based upon a number of training patterns in a neighborhood of the test pattern in the feature space. The number of training patterns in the neighborhood of the test pattern is obtained from computing a convolution of a density function of the training patterns with a Gaussian smoothing function centered on the test pattern, where the density function of the training patterns is represented as a mixture of Gaussian functions. The convolution of the smoothing function and the mixture of Gaussian functions can be expressed analytically.

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Konz, Germany

Bruno Mirbach