Abstract: The present disclosure generally pertains to time-of-flight simulation data training circuitry, configured to: obtain time-of-flight camera model data based on a time-of-flight camera model modelling a real time-of-flight camera of a real time-of-flight camera type; obtain real time-of-flight data from at least one real time-of-flight camera of the real time-of-flight camera type; determine a difference between the real time-of-flight data and the time-of-flight camera model data; and update, based on the determined difference, the time-of-flight camera model for generating simulated time-of-flight data representing the real time-of-flight camera.

Abstract: An information processing apparatus (10) includes a first acquisition unit (111), a second acquisition unit (111), and an estimation unit (112). The first acquisition unit (111) acquires first sensing information obtained by capturing an image of a subject using a first sensor (20A) including a single-plate image sensor. The second acquisition unit (111) acquires second sensing information obtained by sensing a direction different from a direction of the first sensor (20A) using second sensor (20B). The estimation unit (112) estimates the spectral reflectance of the subject based on the first sensing information and the second sensing information.

Abstract: An image processing device has circuitry, which is configured to obtain image data, the image data being generated on the basis of a non-linear mapping defining a mapping between an object plane and an image plane; and to process the image data by applying a kernel of an artificial network to the image data based on the non-linear mapping.

Abstract: The present disclosure generally pertains to a polarization imaging portion having a plurality of imaging elements, wherein each imaging element is configured to convert light into an electric signal, the polarization imaging portion further including: a set of event sensors configured to detect an event being indicative of an intensity change of the light; a set of polarization filters; and a set of tunable polarizers configured to adjust a polarization of the light in response to an electric signal, wherein each polarization filter of the set of polarization filters is associated with a respective tunable polarizer of the set of tunable polarizers, thereby configuring a set of tunable polarization filters, such that the set of tunable polarization filters is associated with the set of event sensors.

Abstract: The present disclosure generally pertains to time-of-flight simulation data training circuitry, configured to: obtain time-of-flight camera model data based on a time-of-flight camera model modelling a real time-of-flight camera of a real time-of-flight camera type; obtain real time-of-flight data from at least one real time-of-flight camera of the real time-of-flight camera type; determine a difference between the real time-of-flight data and the time-of-flight camera model data; and update, based on the determined difference, the time-of-flight camera model for generating simulated time-of-flight data representing the real time-of-flight camera.

Abstract: An apparatus comprising a spectral filter (26) with a variable spectral filter transmission (FT(?)); an event-based imaging sensor (27) configured to produce measurement events (y1, y2, . . . , yn) which correspond to a change in a filter response (Y(t)) that is generated by an observed spectrum (X(?)), and a processor (28) configured to control the filter transmission (FT(?)) of the spectral filter (26) so that it sweeps over wavelength (?) with time (t), and to generate an estimation (X*(?)) of the observed spectrum (X(?)) based on the measurement events (y1, y2, . . . , yn) that correspond to the filter response (Y(t)) and based on the filter transmission (FT(?)).

Abstract: An image processing device has circuitry, which is configured to obtain image data, the image data being generated on the basis of a non-linear mapping defining a mapping between an object plane and an image plane; and to process the image data by applying a kernel of an artificial network to the image data based on the non-linear mapping.

Abstract: An apparatus comprising a spectral filter (26) with a variable spectral filter transmission (FT(?)); an event-based imaging sensor (27) configured to produce measurement events (y1, y2, . . . , yn) which correspond to a change in a filter response (Y(t)) that is generated by an observed spectrum (X(?)), and a processor (28) configured to control the filter transmission (FT(?)) of the spectral filter (26) so that it sweeps over wavelength (?) with time (t), and to generate an estimation (X*(?)) of the observed spectrum (X(?)) based on the measurement events (y1, y2, . . . , yn) that correspond to the filter response (Y(t)) and based on the filter transmission (FT(?)).

Abstract: A video coding apparatus for encoding a compressive sensing signal has a processor. The processor obtains a compressive sensing sampling matrix; and captures the compressive sensing signal representing image data based on the compressive sensing sampling matrix, wherein the compressive sensing sampling matrix is non-uniform varied.

Abstract: A right image and a left image is provided. Then a disparity estimation map relating to both images is provided and a left subtitle detection map for the left image and a right subtitle detection map for the right image are generated. Each map indicates subtitle areas within an image. For said subtitle areas and based on said disparity estimation map a subtitle disparity value for X and Y directions common for all subtitle areas is determined. Said left and right subtitle maps and said subtitle X and Y disparity values are used in an image interpolation process.

Abstract: A method for stabilizing a first sequence of digital image frames is provided including determining a dominant motion vector of a dominant motion layer of said sequence; determining a first part of the dominant motion vector representative of an intended motion in said sequence; determining a second part of the dominant motion vector representative of an unintended motion in said sequence; and generating a second sequence from the first sequence based on the second part. A corresponding image stabilization unit is provided as well.

Abstract: A video coding apparatus for encoding a compressive sensing signal has a processor. The processor obtains a compressive sensing sampling matrix; and captures the compressive sensing signal representing image data based on the compressive sensing sampling matrix, wherein the compressive sensing sampling matrix is non-uniform varied.

Abstract: The present invention relates to a method for providing reliable motion vectors, comprising the steps of determining motion vectors between two image frames with backward and forward estimation, comparing backward estimation motion vectors and forward estimation motion vectors, and detecting unreliable motion vectors based on the comparison result. The present invention further relates to a system for providing reliable motion vectors.

Abstract: A right image and a left image is provided. Then a disparity estimation map relating to both images is provided and a left subtitle detection map for the left image and a right subtitle detection map for the right image are generated. Each map indicates subtitle areas within an image. For said subtitle areas and based on said disparity estimation map a subtitle disparity value for X and Y directions common for all subtitle areas is determined. Said left and right subtitle maps and said subtitle X and Y disparity values are used in an image interpolation process.

Abstract: A method for stabilizing a first sequence of digital image frames is provided including determining a dominant motion vector of a dominant motion layer of said sequence; determining a first part of the dominant motion vector representative of an intended motion in said sequence; determining a second part of the dominant motion vector representative of an unintended motion in said sequence; and generating a second sequence from the first sequence based on the second part. A corresponding image stabilization unit is provided as well.

Abstract: A method for motion estimation with respect to a sequence of images. Respective updates are generated based on predictor vectors. The updates are distributed over a plurality of tables, which are organized in a plurality of table sets. Only a single table of updates, of a respective selected table set, is applied to a given predictor vector to generate a limited set of candidate vectors only. For a subsequent predictor vector, a further single table of the plurality of tables out of the plurality of table sets is applied.

Abstract: Method for iteratively calibrating a disparity estimation process generating a disparity estimation map relating to a 3D image which consists of at least a right and a left image, the method comprising: estimating a left-to-right image disparity map of a 3D image in horizontal and vertical direction, estimating a right-to-left image disparity map of the 3D image in horizontal and vertical direction, determining a misalignment value between the left and right images on the basis of the disparity maps, feeding back the misalignment value as to be considered in the next estimating the disparity maps of a next 3D image, and repeating the method for the next 3D image to iteratively calibrate the disparity estimation process.

Abstract: A method and apparatus for motion estimation of at least a first and a second image frame by estimating at least one motion vector correlating a portion of pixels of the at least first and second image frame, the first and second image frame being part of an image frame sequence. The at least one motion vector is obtained by a predominant motion detection generating at least one global motion vector based on at least one previously determined motion vector, the previously determined motion vector correlating a portion of pixels of earlier image frames of the image frame sequence, and an estimation estimating the at least one motion vector based on the at least one global motion vector.

Abstract: A method for discriminating textured regions and homogeneous or flat regions in an image. A multi-scale or multi-resolution measurement is performed with respect to the image. Alternatively or additionally, the image is analyzed by using a prior measured noise value and a local variance value in or with respect to the image.

Abstract: From first and second image data descriptive for first and second pictures captured in a first temporal distance to each other, a global motion estimator unit (110) estimates a global motion vector, which is descriptive for sign and amount of a global displacement of image portions that move with respect to a first axis both when the move at the same speed and when they move at different velocities. The global motion vector improves estimation of fast moving objects. The global motion vector estimation may rely on the evaluation of a plurality of one-dimensional profiles.