Abstract: A Hough processor comprises a pre-processor and a Hough transformation unit. The pre-processor is configured to receive a plurality of samples respectively comprising an image and in order to rotate or reflect the image of the respective sample. The Hough transformation unit is configured to collect a predetermined searched pattern in the plurality of samples on the basis of a plurality of versions. The Hough transformation unit comprises a characteristic being dependent on the searched pattern, which is adjustable according to the searched pattern.

Abstract: A 3D image analyzer for the determination of a gaze direction or a line of sight (having a gaze direction vector and a location vector, which e.g. indicates the pupil midpoint and where the gaze direction vector starts) in a 3D room is configured to receive one first set of image data and a further set of image information, wherein the first image contains a pattern, which displays a three-dimensional object from a first perspective into a first image plane, and wherein the further set contains an image having a pattern, which displays the same three-dimensional object from a further perspective into a further image plane, or wherein the further set has an image information and/or a relation between at least two points in the first image and/or at least a position information. The 3D image analyzer has a position calculator and an alignment calculator and calculates therewith a gaze direction in a 3D room.

Abstract: A 2D image analyzer includes an image scaler, an image generator and a pattern finder. The image scaler is configured to scale an image according to a scaling factor. The image generator is configured to produce an overview image including a plurality of copies of the received and scaled image, wherein every copy is scaled about a different scaling factor. Thereby, the respective position can be calculable by an algorithm, which considers a gap between the scaled images in the overview image, a gap of the scaled image towards one or more borders of the overview image and/or other predefined conditions. The pattern finder is configured to perform a feature transformation and classification of the overview image in order to output a position at which an accordance of the searched pattern and the predetermined pattern is maximal. A post-processing unit for smoothening and correcting the position of local maxima in the classified overview image may also be provided.

Abstract: A device for detecting momentary sleep is shown. The device includes a video recording device for video-based monitoring of a person and of an eye area of the person, the video recording device being configured to record a sequence of pictures of the person and of the eye area and to output same to a threshold determination device. The threshold determination device is configured to derive one or more thresholds individually adapted to the person from the sequence of pictures. Moreover, the device includes a threshold evaluator configured to decide, on the basis of the one or more individually adapted thresholds, whether or not the person has momentarily fallen asleep, the one or more individually adapted thresholds being utilized for establishing whether or not the thresholds have been passed within the actual time curve of the eye opening and, thus, whether or not the person has momentarily fallen asleep.

Abstract: A 2D image analyzer includes an image scaler, an image generator and a pattern finder. The image scaler is configured to scale an image according to a scaling factor. The image generator is configured to produce an overview image including a plurality of copies of the received and scaled image, wherein every copy is scaled about a different scaling factor. Thereby, the respective position can be calculable by an algorithm, which considers a gap between the scaled images in the overview image, a gap of the scaled image towards one or more borders of the overview image and/or other predefined conditions. The pattern finder is configured to perform a feature transformation and classification of the overview image in order to output a position at which an accordance of the searched pattern and the predetermined pattern is maximal. A post-processing unit for smoothening and correcting the position of local maxima in the classified overview image may also be provided.

Abstract: A Hough processor comprises a pre-processor and a Hough transformation unit. The pre-processor is configured to receive a plurality of samples respectively comprising an image and in order to rotate or reflect the image of the respective sample. The Hough transformation unit is configured to collect a predetermined searched pattern in the plurality of samples on the basis of a plurality of versions. The Hough transformation unit comprises a characteristic being dependent on the searched pattern, which is adjustable according to the searched pattern.

Abstract: A 3D image analyzer for the determination of a gaze direction or a line of sight (having a gaze direction vector and a location vector, which e.g. indicates the pupil midpoint and where the gaze direction vector starts) in a 3D room is configured to receive one first set of image data and a further set of image information, wherein the first image contains a pattern, which displays a three-dimensional object from a first perspective into a first image plane, and wherein the further set contains an image having a pattern, which displays the same three-dimensional object from a further perspective into a further image plane, or wherein the further set has an image information and/or a relation between at least two points in the first image and/or at least a position information. The 3D image analyzer has a position calculator and an alignment calculator and calculates therewith a gaze direction in a 3D room.

Abstract: A device for analyzing an audio signal to obtain an analysis representation of the audio signal includes a converter for converting the audio signal into a representation representing a neurotransmitter vesicle occurrence in clefts between a plurality of nerve fibers and inner auditory cells of an ear model, and a calculator for calculating a neural activity pattern over time on the plurality of nerve fibers, which results based on the neurotransmitter vesicle occurrence, wherein the neural activity pattern is an analysis representation of the audio signal. The inventive device thus generates an especially suitable analysis representation of an audio signal in which masking effects occurring in the generation of action potentials on nerve fibers are considered. The inventive analysis representation is also suitable for an examination of the audio content and also for controlling cochlea implants.

Abstract: A device for analyzing an audio signal to obtain an analysis representation of the audio signal includes a converter for converting the audio signal into a representation representing a neurotransmitter vesicle occurrence in clefts between a plurality of nerve fibers and inner auditory cells of an ear model, and a calculator for calculating a neural activity pattern over time on the plurality of nerve fibers, which results based on the neurotransmitter vesicle occurrence, wherein the neural activity pattern is an analysis representation of the audio signal. The inventive device thus generates an especially suitable analysis representation of an audio signal in which masking effects occurring in the generation of action potentials on nerve fibers are considered. The inventive analysis representation is also suitable for an examination of the audio content and also for controlling cochlea implants.

Abstract: In a method of extracting a signal identifier from a time signal, the temporal occurrence of signal edges in the time signal is detected (12), wherein a signal edge has a specified temporal length. In addition, the temporal interval between two selected detected signal edges is determined (14). From the temporal interval determined, a frequency value is calculated (16), the frequency value being associated with a time of occurrence of the frequency value in the time signal so as to obtain a coordinate tuple from the frequency value and the time of occurrence for this frequency value. A signal identifier is created from a plurality of coordinate tuples (18), each coordinate tuple including a frequency value and a time of occurrence, which is why the signal identifier includes a sequence of signal identifier values reproducing the temporal form of the time signal. The extracted signal identifier is based on signal edges of the time signal and thus reproduces the temporal form of the time signal.