Siemens Aktiengesellschaft

Siemens Aktiengsellschaft

Prasendt Investments, LLC

Andreas Hutter

Peter Amon

Jörg Heuer

Ulrich Niedermeier

André Kaup

Gero Bäse

Norbert Oertel

Robert Kutka

Benoit Timmermann

Sanjukta Ghosh

Bernhard Agthe

Jorg Heuer

Andreas Weinlich

Jürgen Pandel

Christian Timmerer

Vinay Damodar Shet

Eugen Wige

Xianjun Sam Zheng

Slobodan Ilic

Xiang Gao

Hermann Hellwagner

Harald Kosch

Andrea Kofler-Vogt

Thomas Rathgen

Ziyan Wu

Maneesh Kumar Singh

Sergey Zakharov

Daniel Labisch

Daniel Peintner

Juergen Pandel

Asa MacWilliams

Thomas Riegel

Sushil Mittal

Ralf Buschmann

Benjamin Planche

Cheng-Hao Kuo

Ivan Shugurov

Jörg Heufr

Michael Ransburg

Johannes Rehm

Gabriel Panis

Marcus Laumer

Andoni Gonzalo

Gert De Nul

Joseph Newman

Mayank Rana

Tobias Schwarze

Norbert Ortel

Gero Base

Jurgen Pandel

Nischita Sudharsan-GRÖBER

Various embodiments include a computer-implemented method for augmenting a physical infrastructure with an information element. The method may include: acquiring data of a spatial environment infrastructure, dimensioned by a coordinate system; defining a second spatial environment as a subspace of the first and dimensioned by its own coordinate system with a first marker item and a second marker item; creating a SLAM map of the second environment with two SLAM marker items; recognizing the SLAM identifiers; aligning the coordinate system of the SLAM map to the coordinate system of the second environment; recognizing a first and a second marker identifier; determining an absolute position and orientation of the second spatial environment relative to the first environment; defining the information element in relation to a component of the infrastructure; determining coordinates of the element in the second environment; and augmenting the physical infrastructure with the information element at the spatial coordinates.

Aligning and augmenting a partial subspace of a physical infrastructure with at least one information element

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.

Dense 6-DoF pose object detector

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.

Methods for generating a deep neural net and for localising an object in an input image, deep neural net, computer program product, and computer-readable storage medium

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.

Object recognition from images using cad models as prior

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.

Method and apparatus for analysing an image

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.

Method and apparatus for analyzing an image

A method and apparatus for detecting objects of interest in images, the method comprising the steps of supplying (S) 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 (S) 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.

Method and apparatus for detecting objects of interest in images

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.

Method for coding sequence of digital images

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.

Method and apparatus for encoding and decoding video signal

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.

Autogressive pixel prediction in the neighborhood of image borders

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.

Compression and decompression of reference images in a video encoder

The embodiments relate to pixel-prediction methods and devices that are based on one of a selection of one out of at least two model functions that provide a prediction function and an adaptive model function that is capable to predict intensity characteristics of reference pixels used for prediction.

Pixel-prediction for compression of visual data

For a compressed video stream formed of a sequence of compressed data packets, each compressed packet is assigned to a frame by processing the undecoded data packet. A frame includes packets. A first plurality of frames is assigned to a first sliding window and a second plurality of frames is assigned to a second sliding window. The first and second sliding windows have different frames. A first window indicator is calculated by determining a first indicator representing an average number of packets per frame within the first window, and/or a second indicator representing size information of the data within the first window. A second window indicator is calculated by determining a third indicator representing an average number of packets per frame within the second window, and/or a fourth indicator representing size information of the data within the second window. The first and the second window indicators are compared.

Method for processing a compressed video stream

A reconstructed image is provided within the framework of predictive coding. The reconstructed image is compressed according to a coding mode of an image coding device and stored in a reference image memory.

Compression and decompression of reference images in video coding device

Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.

Configuration tool for video analytics

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Munich, Germany

Andreas Hutter