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: 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: 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.