Kontschieder
Heinz Kontschieder, Graz AT
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20090156966 | MODULAR SENSOR CASSETTE - The invention concerns a sensor cassette that can be inserted into an analyzer comprising a continuous measuring channel for receiving fluidic media and sensory elements for determining chemical and/or physical parameters of the fluidic media. According to the invention the sensor cassette consists of at least two permanently connected but separately manufactured modules which each have a housing and a measuring channel section wherein the measuring channel sections of adjacent modules are connected to the continuous measuring channel by a fluidic coupling and wherein at least one of the connected modules is designed as a sensor module and has a sensor array comprising at least two sensory elements. Furthermore, a memory element is allocated to the sensor cassette on which specific information for the sensor cassette in particular with regard to its construction from the respective modules is stored. | 06-18-2009 |
Peter Kontschieder, Graz AT
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20140307956 | IMAGE LABELING USING GEODESIC FEATURES - Image labeling is described, for example, to recognize body organs in a medical image, to label body parts in a depth image of a game player, to label objects in a video of a scene. In various embodiments an automated classifier uses geodesic features of an image, and optionally other types of features, to semantically segment an image. For example, the geodesic features relate to a distance between image elements, the distance taking into account information about image content between the image elements. In some examples the automated classifier is an entangled random decision forest in which data accumulated at earlier tree levels is used to make decisions at later tree levels. In some examples the automated classifier has auto-context by comprising two or more random decision forests. In various examples parallel processing and look up procedures are used. | 10-16-2014 |
20150248765 | DEPTH SENSING USING AN RGB CAMERA - A method of sensing depth using an RGB camera. In an example method, a color image of a scene is received from an RGB camera. The color image is applied to a trained machine learning component which uses features of the image elements to assign all or some of the image elements a depth value which represents the distance between the surface depicted by the image element and the RGB camera. In various examples, the machine learning component comprises one or more entangled geodesic random decision forests. | 09-03-2015 |
Peter Kontschieder, Cambridge GB
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20160071284 | VIDEO PROCESSING FOR MOTOR TASK ANALYSIS - Video processing for motor task analysis is described. In various examples, a video of at least part of a person or animal carrying out a motor task, such as placing the forefinger on the nose, is input to a trained machine learning system to classify the motor task into one of a plurality of classes. In an example, motion descriptors such as optical flow are computed from pairs of frames of the video and the motion descriptors are input to the machine learning system. For example, during training the machine learning system identifies time-dependent and/or location-dependent acceleration or velocity features which discriminate between the classes of the motor task. In examples, the trained machine learning system computes, from the motion descriptors, the location dependent acceleration or velocity features which it has learned as being good discriminators. In various examples, a feature is computed using sub-volumes of the video. | 03-10-2016 |