Abstract: A method and a system for object detection and pose estimation within an input image. A 6-degree-of-freedom object detection and pose estimation is performed using a trained encoder-decoder convolutional artificial neural network including an encoder head, an ID mask decoder head, a first correspondence color channel decoder head and a second correspondence color channel decoder head. The ID mask decoder head creates an ID mask for identifying objects, and the color channel decoder heads are used to create a 2D-to-3D-correspondence map. For at least one object identified by the ID mask, a pose estimation based on the generated 2D-to-3D-correspondence map and on a pre-generated bijective association of points of the object with unique value combinations in the first and the second correspondence color channels is generated.

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 computerized method for facilitating a user's action at a terminal at a terminal location is provided. The method comprising: receiving a request from the user device to perform an action including any one of: collecting winnings derived from a placed bet and placing a bet; receiving from a computer device located at the terminal location, data indicative of a user device's location; determining if the user's device location and the terminal location meet a sufficiently close criterion indicative of an close distance of the user from the terminal, and if in the affirmative, granting the request; thereby enabling the user to collect the winnings or place the bet, only when located within the close distance to the terminal.

Abstract: The disclosure relates to a method how to recover an object from a cluttered image. The disclosure also relates to a computer program product and a computer-readable storage medium including instructions which, when the program is executed by a computer, cause the computer to carry out the acts of the mentioned method. Further, the disclosure relates to methods how to train components of a recognition system for recovering an object from such a cluttered image. In addition, the disclosure relates to such a recognition system.

Abstract: A method and a system for object detection and pose estimation within an input image. A 6-degree-of-freedom object detection and pose estimation is performed using a trained encoder-decoder convolutional artificial neural network including an encoder head, an ID mask decoder head, a first correspondence color channel decoder head and a second correspondence color channel decoder head. The ID mask decoder head creates an ID mask for identifying objects, and the color channel decoder heads are used to create a 2D-to-3D-correspondence map. For at least one object identified by the ID mask, a pose estimation based on the generated 2D-to-3D-correspondence map and on a pre-generated bijective association of points of the object with unique value combinations in the first and the second correspondence color channels is generated.

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: A method for training a generative network that is configured for converting cluttered images into a representation of the synthetic domain and a method for recovering an object from a cluttered image.

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: The disclosure relates to a method how to recover an object from a cluttered image. The disclosure also relates to a computer program product and a computer-readable storage medium including instructions which, when the program is executed by a computer, cause the computer to carry out the acts of the mentioned method. Further, the disclosure relates to methods how to train components of a recognition system for recovering an object from such a cluttered image. In addition, the disclosure relates to such a recognition system.

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: 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: In a method for coding a sequence of digital images, a prediction error between predicted values and the original values of pixels is processed for generating the coded sequence of digital images. A preset prediction mode is an intra-prediction mode based on pixels of a single image and includes, for a region of pixels with reconstructed values in the single image and for a template of an image area, comparing a first patch of pixels in the region that surround a first pixel to be predicted based on the template with several second patches. A predicted value of the first pixel is determined based on the values of one or more second pixels that have the highest similarity described by the similarity measure among all second pixels of the plurality of second pixels in the region.

Abstract: The invention relates to a system for transmitting video data from a server to a client. The system has a first coding unit, which is designed to transmit video data of a first quality from the server to the client in the form of a livestream, and a second coding unit, which is designed to store the video data in a second quality in a storage unit (13) and to transmit the coded video data in the second quality from the storage unit (13) to the client (2) in response to a request signal from the client (2), wherein the second quality is greater than the first quality. The proposed system allows video data to be transmitted from a medical environment, for example an operating room, to an external expert via a network. The video data in the form of a livestream is provided in a low quality and can additionally be provided in a high quality upon request by the expert. The invention further relates to a method for transmitting video data from a server to a client.

Abstract: An encoder encodes a video signal formed of video frames, each including image blocks. The encoder includes a processing unit which calculates at least one high resolution reference image block on the basis of previously encoded image blocks by executing a super resolution algorithm to perform a local motion compensation; and a motion compensation unit which calculates on the basis of the calculated high resolution reference image block a temporal predictor which is subtracted from a current image block of the video signal. Together, the encoder and a corresponding decoder improve the signal quality of a video signal significantly.

Abstract: For each array of pixels, an autoregressive pixel-prediction method is performed based on a weighted sum of reconstructed pixel values of reconstructed pixels in a specific neighborhood region adjacent to the current pixel to be coded. For determining the weights, the pixel values in a specific training region adjacent to the current pixel are taken into account. The coding method is characterized by an appropriate determination of the specific neighborhood region and the specific training region in case that reconstructed pixel values do not exist for all pixels in the neighborhood region and the training region. In such a case, the number of pixels in the neighborhood region is reduced to a number of reconstructed pixels until the ratio between the number of pixels in the training region and the number of pixels in the neighborhood region exceeds a predetermined threshold.

Abstract: Methods and devices transform image data, which are transformed by a compression filter before being compressed and stored in a reference image memory. In an extension, an inverse transformation to that of the compression filter is performed by a decompression filter when image data from the reference memory are read out and decompressed. The methods and devices can be used for image compression methods and image decompression methods that use reference image memories.