Abstract: A temporal sequence of pictures is generated in a method for encoding of a first video stream. To do so, a synchronization signal can be used, which can be derived from a second video stream independently of the first video stream. Alternatively, the encoding of a second video stream independent of the first video stream can be based on the same principle as for the encoding of the first video stream.

Abstract: A temporal sequence of pictures is generated in a method for encoding of a first video stream. To do so, a synchronization signal can be used, which can be derived from a second video stream independently of the first video stream. Alternatively, the encoding of a second video stream independent of the first video stream can be based on the same principle as for the encoding of the first video stream.

Abstract: The invention relates to a suspension shoe for use in a climbing unit having a first climbing component which is in particular a climbing rail, and a second climbing component which is in particular a climbing carriage movable along the climbing rail, the suspension shoe comprising: an anchor for anchoring in a concreting portion of a building, and a suspension part connected to the anchor, wherein the suspension part has a first mount for releasably mounting the first climbing component of the climbing unit, in particular the climbing rail, and a second mount for releasably mounting the second climbing component of the climbing unit, in particular the climbing carriage.

Abstract: Methods for generating a deep neural net and for localizing an object in an input image, the deep neural net, a corresponding computer program product, and a corresponding computer-readable storage medium are provided. A discriminative counting model is trained to classify images according to a number of objects of a predetermined type depicted in each of the images, and a segmentation model is trained to segment images by classifying each pixel according to what image part the pixel belongs to. Parts and/or features of both models are combined to form the deep neural net. The deep neural net is adapted to generate, in a single forward pass, a map indicating locations of any objects for each input image.

Abstract: A temporal sequence of pictures is generated in a method for encoding of a first video stream. To do so, a synchronization signal can be used, which can be derived from a second video stream independently of the first video stream. Alternatively, the encoding of a second video stream independent of the first video stream can be based on the same principle as for the encoding of the first video stream.

Abstract: A formwork and method for casting a concrete structure comprising: a first form element for delimiting a cavity to receive concrete, the first form element having an upper end and a lower end, a first tilt sensor for measuring an actual tilt of the first form element, the first tilt sensor further comprising a first sensor element for measuring the inclination of a lower region of the first longitudinal element and a second sensor element for measuring the inclination of an upper region of the first longitudinal element, and determining a deviation between the inclination of the lower region of the first longitudinal element and the upper region of the first longitudinal element.

Abstract: A temporal sequence of pictures is generated in a method for encoding of a first video stream. To do so, a synchronization signal can be used, which can be derived from a second video stream independently of the first video stream. Alternatively, the encoding of a second video stream independent of the first video stream can be based on the same principle as for the encoding of the first video stream.

Abstract: Methods for generating a deep neural net and for localizing an object in an input image, the deep neural net, a corresponding computer program product, and a corresponding computer-readable storage medium are provided. A discriminative counting model is trained to classify images according to a number of objects of a predetermined type depicted in each of the images, and a segmentation model is trained to segment images by classifying each pixel according to what image part the pixel belongs to. Parts and/or features of both models are combined to form the deep neural net. The deep neural net is adapted to generate, in a single forward pass, a map indicating locations of any objects for each input image.

Abstract: The present embodiments relate to analysing an image. An artificial deep neural net is pre-trained to classify images into a hierarchical system of multiple hierarchical classes. The pre-trained neural net is then adapted for one specific class, wherein the specific class is lower in the hierarchical system than an actual class of the image. The image is then processed by a forward pass through the adapted neural net to generate a processing result. An image processing algorithm is then used to analyse the processing result focused on features corresponding to the specific class.

Abstract: The disclosure relates to a method and an apparatus for analyzing an image using a deep neural net pre-trained for multiple classes. The image is processed by a forward pass through an adapted neural net to generate a processing result. The adapted neural net is adapted from the pre-trained neural net to focus on exactly one selected class. The processing result is then analyzed focused on features corresponding to the selected class using an image processing algorithm. A modified image is generated by removing a manifestation of these features from the image.

Abstract: A method and apparatus for detecting objects of interest in images, the method comprising the steps of supplying (S1) at least one input image to a trained deep neural network, DNN, which comprises a stack of layers; and using at least one deconvolved output of at least one learned filter or combining (S2) deconvolved outputs of learned filters of at least one layer of the trained deep neural network, DNN, to detect the objects of interest in the supplied images.

Abstract: The disclosure relates to a method and an apparatus for analyzing an image using a deep neural net pre-trained for multiple classes. The image is processed by a forward pass through an adapted neural net to generate a processing result. The adapted neural net is adapted from the pre-trained neural net to focus on exactly one selected class. The processing result is then analyzed focused on features corresponding to the selected class using an image processing algorithm. A modified image is generated by removing a manifestation of these features from the image.

Abstract: A temporal sequence of pictures is generated in a method for encoding of a first video stream. To do so, a synchronization signal can be used, which can be derived from a second video stream independently of the first video stream. Alternatively, the encoding of a second video stream independent of the first video stream can be based on the same principle as for the encoding of the first video stream.

Abstract: A formwork and method for casting a concrete structure comprising: a first form element for delimiting a cavity to receive concrete, the first form element having an upper end and a lower end, a first tilt sensor for measuring an actual tilt of the first form element, the first tilt sensor further comprising a first sensor element for measuring the inclination of a lower region of the first longitudinal element and a second sensor element for measuring the inclination of an upper region of the first longitudinal element, and determining a deviation between the inclination of the lower region of the first longitudinal element and the upper region of the first longitudinal element.

Abstract: A temporal sequence of pictures is generated in a method for encoding of a first video stream. To do so, a synchronization signal can be used, which can be derived from a second video stream independently of the first video stream. Alternatively, the encoding of a second video stream independent of the first video stream can be based on the same principle as for the encoding of the first video stream.

Abstract: A climbing formwork and a method for erection of a concrete structure by successively casting a plurality of casting segments, the climbing formwork comprising a first form element for delimiting a cavity to receive concrete for forming an uppermost casting segment, the first form element having upper and lower ends, a support structure to support the first form element, a tilt sensor for measuring a tilt of the first form element, a measuring unit for measuring a horizontal distance between the lower end of the outer surface of the first form element and the upper end of the outer surface of the previous casting segment, a processing unit communicating with the tilt sensor and the measuring unit for calculating a target tilt of the first form element, the processing unit further for determining a deviation between the actual tilt and the target tilt of the first form element.

Abstract: The present embodiments relate to analysing an image. An artificial deep neural net is pre-trained to classify images into a hierarchical system of multiple hierarchical classes. The pre-trained neural net is then adapted for one specific class, wherein the specific class is lower in the hierarchical system than an actual class of the image. The image is then processed by a forward pass through the adapted neural net to generate a processing result. An image processing algorithm is then used to analyse the processing result focused on features corresponding to the specific class.

Abstract: A method and apparatus for detecting objects of interest in images, the method comprising the steps of supplying (S1) at least one input image to a trained deep neural network, DNN, which comprises a stack of layers; and using at least one deconvolved output of at least one learned filter or combining (S2) deconvolved outputs of learned filters of at least one layer of the trained deep neural network, DNN, to detect the objects of interest in the supplied images.

Abstract: A computer device for training a deep neural network is provided. The computer device includes a receiving unit for receiving a two-dimensional input image frame, and a deep neural network for examining the two-dimensional input image frame in view of objects being included in the two-dimensional input image frame. The deep neural network includes a plurality of hidden layers and an output layer representing a decision layer. The computer device includes a training unit for training the deep neural network using transfer learning based on synthetic images for generating a model comprising trained parameters, and an output unit for outputting a result of the deep neural network based on the model.

Abstract: Functional data structure for coding a set of digital images. Each of the images is compressed into at least-one first data stream portion, which comprises a portion of the macroblocks reduced by physical redundancies, and-one second data stream portion, which describes the redundancies. For the intraprediction macroblocks, the first data stream portion can be reduced by color value statements with correlations to color values from rows of pixels outside and at one edge of the intraprediction macroblock and for which, in the case of pixels outside the compressed image, a color value default is assumed. The second data stream can comprise intrapredictors for describing the correlations, with coding of an area which is divided into first areas, each of which is occupied by the macroblocks of one of the digital images, and a second area which spaces apart the first areas and is occupied by default color value pixels.