Abstract: An input video sequence from a camera is filtered by a process that comprises detecting temporal tracks of moving image parts from the input video sequence and assigning activity scores to temporal segments of the tracks, using respective predefined track dependent activity score functions for a plurality of different activity types. Based on this, event scores for are computed as a function of time. This computation is controlled by a definition of a temporal sequence of activity types or compound activity types for an event type. Successive intermediate scores are computed, each as a function of time for a respective activity types or compound activity types in the temporal sequence.

Abstract: Identification of ships from camera images is performed by computing sets of reference silhouette images for respective reference ships for use to match the reference silhouette images with a camera image of an unidentified ship. A computer forms the set of the reference silhouette images for a reference ship is performed automatically by a processing system on the basis of at least one silhouette image of the respective reference ship. The computer generates a three dimensional model of the reference ship that represents distances of an outer surface of the reference ship to a plane on which the captured silhouette image has been mapped, the three dimensional model representing the distances as a function of position in said plane. A deck line of the reference ship is detected based on the camera image. The represented distances to the outer surface are set and/or adapted dependent on position in said plane relative to the detected deck line.

Abstract: An input video sequence from a camera is filtered by a process that comprises detecting temporal tracks of moving image parts from the input video sequence and assigning activity scores to temporal segments of the tracks, using respective predefined track dependent activity score functions for a plurality of different activity types. Based on this, event scores for are computed as a function of time. This computation is controlled by a definition of a temporal sequence of activity types or compound activity types for an event type. Successive intermediate scores are computed, each as a function of time for a respective activity types or compound activity types in the temporal sequence.

Abstract: Identification of ships from camera images is performed by computing sets of reference silhouette images for respective reference ships for use to match the reference silhouette images with a camera image of an unidentified ship. A computer forms the set of the reference silhouette images for a reference ship is performed automatically by a processing system on the basis of at least one silhouette image of the respective reference ship. The computer generates a three dimensional model of the reference ship that represents distances of an outer surface of the reference ship to a plane on which the captured silhouette image has been mapped, the three dimensional model representing the distances as a function of position in said plane. A deck line of the reference ship is detected based on the camera image. The represented distances to the outer surface are set and/or adapted dependent on position in said plane relative to the detected deck line.

Abstract: Corrected color components are obtained that correspond to an interpolation between a first and second corrected color component vector, in the first vector the color component are scaled to move a characteristic color component vector towards a target and in the second set the color components are contracted towards a reference determined by part of the color components. A position of the interpolation between the first and second corrected color component vectors is controlled by an interpolation coefficient computed from a ratio of an average value of the at least one of the color component in the input image or one or more reference images with similar content and an average obtained from the other color components of the input image or the or one or more reference images with similar content. The position of the interpolation is moved increasingly toward the second corrected color component vector with decreasing values of said ratio.

Abstract: Descriptions of video segments for use to search or select video segments are generated by using a combination of video based action type detection and image based object detection. Action type detection results in detection of action types from a predetermined set of action types, with little or no information about actors involved in the action. Object detection results in detection of individual objects in the images, with little or no information actions. A set of rules is used that define one or more roles associated with action types and conditions on objects that can fill these roles. Detected action types are used to select rules and detected objects are assigned to roles defined the selected rule. Score values are computed for different assignments of detected objects to the role, as a function of the attribute values of the assigned objects. A combination of the detected action type and the detected objects is selected on the basis of the score values.

Abstract: Corrected color components are obtained that correspond to an interpolation between a first and second corrected color component vector, in the first vector the color component are scaled to move a characteristic color component vector towards a target and in the second set the color components are contracted towards a reference determined by part of the color components. A position of the interpolation between the first and second corrected color component vectors is controlled by an interpolation coefficient computed from a ratio of an average value of the at least one of the color component in the input image or one or more reference images with similar content and an average obtained from the other color components of the input image or the or one or more reference images with similar content. The position of the interpolation is moved increasingly toward the second corrected color component vector with decreasing values of said ratio.

Abstract: Descriptions of video segments for use to search or select video segments are generated by using a combination of video based action type detection and image based object detection. Action type detection results in detection of action types from a predetermined set of action types, with little or no information about actors involved in the action. Object detection results in detection of individual objects in the images, with little or no information actions. A set of rules is used that define one or more roles associated with action types and conditions on objects that can fill these roles. Detected action types are used to select rules and detected objects are assigned to roles defined the selected rule. Score values are computed for different assignments of detected objects to the role, as a function of the attribute values of the assigned objects. A combination of the detected action type and the detected objects is selected on the basis of the score values.

Abstract: The invention relates to a method for detecting a moving object in a sequence of images captured by a moving camera. The method comprises the step of constructing a multiple number of difference images by subtracting image values in corresponding pixels of multiple pairs of images being based on captured images. Further, the method comprises the step of retrieving a moving object by extracting spatial information of pixels in the multiple number of constructed difference images having relatively large image values. In addition, from a pair of images in the construction step an image is a representation of a high resolution image having a higher spatial resolution than original captured images on which the high resolution image is based.

Abstract: A method of improving the resolution of a small moving object in a digital image sequence comprises the steps of: constructing (101) a high-resolution image background model, detecting (102) the moving object using the high-resolution image background model, fitting (103) a model-based trajectory for object registration, and producing (104) a high-resolution object description. The step of producing a high-resolution object description involves an iterative optimisation of a cost function (109) based upon a polygonal model of an edge of the moving object. The cost function is preferably also based upon a high resolution intensity description. The iterative optimisation of the cost function may involve a polygon description parameter and/or an intensity parameter.

Abstract: A method of improving the resolution of a small moving object in a digital image sequence comprises the steps of: constructing (101) a high-resolution image background model, detecting (102) the moving object using the high-resolution image background model, fitting (103) a model-based trajectory for object registration, and producing (104) a high-resolution object description. The step of producing a high-resolution object description involves an iterative optimization of a cost function (109) based upon a polygonal model of an edge of the moving object. The cost function is preferably also based upon a high resolution intensity description. The iterative optimization of the cost function may involve a polygon description parameter and/or an intensity parameter.

Abstract: Turbulence compensated image data is produced by adapting the settings of a deformable mirror (4) or other adaptable optical element that is used to in the formation of images of an object. A series of images produced by the camera (3) is captured with different adaptations the deformable mirror (4). Turbulence is estimated from the series of images. Compensated image data is computed, by compensating the turbulence in at least one image after the at least one image has been captured by the camera, by using the estimated turbulence and a turbulence compensation computation algorithm.

Abstract: A method of improving the resolution of a small moving object in a digital image sequence comprises the steps of: constructing (101) a high-resolution image background model, detecting (102) the moving object using the high-resolution image background model, fitting (103) a model-based trajectory for object registration, and producing (104) a high-resolution object description. The step of producing a high-resolution object description involves an iterative optimisation of a cost function (109) based upon a polygonal model of an edge of the moving object. The cost function is preferably also based upon a high resolution intensity description. The iterative optimisation of the cost function may involve a polygon description parameter and/or an intensity parameter.

Abstract: The invention relates to a method for detecting a moving object in a sequence of images captured by a moving camera. The method comprises the step of constructing a multiple number of difference images by subtracting image values in corresponding pixels of multiple pairs of images being based on captured images. Further, the method comprises the step of retrieving a moving object by extracting spatial information of pixels in the multiple number of constructed difference images having relatively large image values. In addition, from a pair of images in the construction step an image is a representation of a high resolution image having a higher spatial resolution than original captured images on which the high resolution image is based.