Abstract: Apparatus for an automatic identification of medically relevant video elements, the apparatus comprising a data input, configured to receive a data stream of image slices, wherein the data stream of image slices represents a temporal course of a view of image slices defined by a masking strip of video images from a video which has been recorded during a medical surgery on a patient an analysis apparatus configured to analyze the data stream of image slices via an analysis comprising at least one predefined analysis step for the presence of at least one sought-for feature and to output a result of the presence, and a processing device configured to output a start mark which indicates a correspondence between the presence and a position in the data stream of image slices if the result indicates the presence of the sought-for feature. Also, a corresponding method is disclosed.

Abstract: Apparatus for an automatic identification of medically relevant video elements, the apparatus comprising a data input, configured to receive a data stream of image slices, wherein the data stream of image slices represents a temporal course of a view of image slices defined by a masking strip of video images from a video which has been recorded during a medical surgery on a patient an analysis apparatus configured to analyze the data stream of image slices via an analysis comprising at least one predefined analysis step for the presence of at least one sought-for feature and to output a result of the presence, and a processing device configured to output a start mark which indicates a correspondence between the presence and a position in the data stream of image slices if the result indicates the presence of the sought-for feature. Also, a corresponding method is disclosed.

Abstract: Apparatus for an automatic identification of medically relevant video elements, the apparatus including a data input, configured to receive a data stream of image slices, wherein the data stream of image slices represents a temporal course of a view of image slices defined by a masking strip of video images from a video which has been recorded during a medical surgery on a patient an analysis apparatus configured to analyze the data stream of image slices via an analysis including at least one predefined analysis step for the presence of at least one sought-for feature and to output a result of the presence, and a processing device configured to output a start mark which indicates a correspondence between the presence and a position in the data stream of image slices if the result indicates the presence of the sought-for feature. Also, a corresponding method is disclosed.

Abstract: An automated method for the generation of (i) human models comprehensive of shape and joint centers information and/or (ii) subject specific models from multiple video streams is provided. To achieve these objectives, a kinematic model is learnt space from a training data set. The training data set includes kinematic models associated with corresponding morphological models. A shape model is identified as well as one or more poses of the subject. The learnt kinematic model space and the identified shape model are combined to generate a full body model of the subject starting from as few as one-static pose. Further, to generate a full body model of an arbitrary human subject, the learnt kinematic model space and the identified shape model are combined using a parameter set. The invention is applicable for fully automatic markerless motion capture and generation of complete human models.

Abstract: A markerless motion capture system is provided for measurements accurate enough for biomechanical, clinical, sport, entertainment, animation, game and movie, design, ergonomics, surveillance applications. The system has multiple cameras distributed around a viewing volume. The cameras allow for the creation of three-dimensional mesh representations of an object dynamically moving within the viewing volume. A model of the object that incorporates specific morphological and kinematic model information (including soft joint constraints) is then matched to the captured three-dimensional mesh representations. The matching routine aims to embed the model into each of the three-dimensional representations using (i) iterative closest point or simulated annealing algorithms and (ii) using soft joint constraints. This unique combination of routines offers a simple, time-efficient, accurate and thus more meaningful assessment of movements.

Abstract: An automated method for the generation of (i) human models comprehensive of shape and joint centers information and/or (ii) subject specific models from multiple video streams is provided. To achieve these objectives, a kinematic model is learnt space from a training data set. The training data set includes kinematic models associated with corresponding morphological models. A shape model is identified as well as one or more poses of the subject. The learnt kinematic model space and the identified shape model are combined to generate a full body model of the subject starting from as few as one-static pose. Further, to generate a full body model of an arbitrary human subject, the learnt kinematic model space and the identified shape model are combined using a parameter set. The invention is applicable for fully automatic markerless motion capture and generation of complete human models.

Abstract: A markerless motion capture system is provided for measurements accurate enough for biomechanical, clinical, sport, entertainment, animation, game and movie, design, ergonomics, surveillance applications. The system has multiple cameras distributed around a viewing volume. The cameras allow for the creation of three-dimensional mesh representations of an object dynamically moving within the viewing volume. A model of the object that incorporates specific morphological and kinematic model information (including soft joint constraints) is then matched to the captured three-dimensional mesh representations. The matching routine aims to embed the model into each of the three-dimensional representations using (i) iterative closest point or simulated annealing algorithms and (ii) using soft joint constraints. This unique combination of routines offers a simple, time-efficient, accurate and thus more meaningful assessment of movements.