Siemens Aktiengesellschaft

Siemens Energy, Inc.

Siemens Mobility Gmbh

Siemens Corporation

Siemens Product Lifecycle Management Software Inc.

Jan Ernst

Ziyan Wu

Terrence Chen

Kuan-Chuan Peng

Ti-chiun Chang

Kevin P Bailey

Shanhui Sun

Stefan Kluckner

Joachim Bamberger

Rameswar Panda

Yakup Genc

Srikrishna Karanam

Arun Innanje

Patrick Reeb

Yunye Gong

Kai Ma

Birgi Tamersoy

Maneesh Kumar Singh

Andrei Szabo

Edward Slavin, Iii

Jeremy Ralph Wiles

Eberhard Ritzhaupt-Kleissl

Oliver Lehmann

Ramesh Nair

Archana Sapkota

Jeremy-Ralph Wiles

Kunpeng Li

Rameswar Rameswar

Deep learning is used to train a neural network for end-to-end prediction of short term (e.g., 20 minutes or less) solar irradiation based on camera images and metadata. The architecture of the neural network includes a recurrent network for temporal considerations. The images and metadata are input at different locations in the neural network. The resulting machine-learned neural network predicts solar irradiation based on camera images and metadata so that a solar plant and back-up power source may be controlled to minimize output power variation.

Solar irradiation prediction using deep learning with end-to-end training

Systems, methods, and computer-readable media are described for determining the orientation of a target object in an image and iteratively reorienting the target object until an orientation of the target object is within an acceptable threshold of a target orientation. Also described herein are systems, methods, and computer-readable media for verifying that an image contains a target object.

Orientation detection in overhead line insulators

Systems, methods, and computer-readable media are described for performing weakly supervised semantic segmentation of input images that utilizes self-guidance on attention maps during training to cause a guided attention inference network (GAIN) to focus attention on an object in an input image in a holistic manner rather than only on the most discriminative parts of the image. The self-guidance is provided jointly by a classification loss function and an attention mining loss function. Extra supervision can also be provided by using a select number pixel-level labeled input images to enhance the semantic segmentation capabilities of the GAIN.

Weakly-supervised semantic segmentation with self-guidance

A method for enhancing facial/object recognition includes receiving a query image, and providing a database of object images, including images relevant to the query image, each image having a first attribute and a second attribute with each of the first attribute and the second attribute having a first state and a second state. The method also includes creating an augmented database by generating a plurality of artificial images for each image in the database, the artificial images cooperating with the image to define a set of images including every combination of the first attribute and the second attribute in each of the first state and the second state, and comparing the query image to the images in the augmented database to find one or more matches.

System and method for enhancing image retrieval by smart data synthesis

Aspects include receiving a request to perform an image classification task in a target domain. The image classification task includes identifying a feature in images in the target domain. Classification information related to the feature is transferred from a source domain to the target domain. The transferring includes receiving a plurality of pairs of task-irrelevant images that each includes a task-irrelevant image in the source domain and in the target domain. The task-irrelevant image in the source domain has a fixed correspondence to the task-irrelevant image in the target domain. A target neural network is trained to perform the image classification task in the target domain. The training is based on the plurality of pairs of task-irrelevant images. The image classification task is performed in the target domain and includes applying the target neural network to an image in the target domain and outputting an identified feature.

Domain adaptation and fusion using weakly supervised target-irrelevant data

A method of expanding a visual learning database in a computer by teaching the computer includes providing a series of training images to the computer wherein each series includes three images with each image falling within a unique image domain and with each image domain representing a possible combination of a first attribute and a second attribute with a first image domain including the first attribute and the second attribute in a first state (X=0, Y=0), a second image domain including the first attribute in a second state and the second attribute in the first state (X=1, Y=0), and a third image domain including the first attribute in the first state and the second attribute in the second state (X=0, Y=1). The method also includes developing within the computer forward generators and reverse generators between the first image domain, the second image domain, the third image domain, and a fourth image domain for which no training image is provided, and applying with the computer the forward generators and reverse generators to single images that fall within one of the first image domain, the second image domain, the third image domain, and a fourth image domain to generate images for the remaining domains to populate a database.

Technique for machine visual learning

Systems and methods are provided for adapting images from different cameras so that a single trained classifier or an analyzer may be used. The classifier or analyzer operates on images that include a particular color distribution or characteristic. A generative network is used to adapt images from other cameras to have a similar color distribution or characteristic for use by the classifier or analyzer. A generative adversarial process is used to train the generative network.

Color adaptation using adversarial training networks

A system and method for visual anomaly localization in a test image includes generating, in plurality of scaled iterations, attention maps for a test image using a trained classifier network, using image-level. A current attention map is generated using an inversion of the classifier network on a condition that a forward pass of the test image in the classifier network detects a first class. One or more attention regions of the current attention map may be extracted and resized as a sub-image. For each scaled iteration, extraction of one or more regions of a current attention map is performed on a condition that the current attention map is significantly different than the preceding attention map. Visual localization of a region for the class in the test image is based on one or more of the attention maps.

Visual localization in images using weakly supervised neural network

The present embodiments relate to automatically estimating a three-dimensional pose of an object from an image captured using a camera with a structured light sensor. By way of introduction, the present embodiments described below include apparatuses and methods for training a system for and estimating a pose of an object from a test image. Training and test images are sampled to generate local image patches. Features are extracted from the local image patches to generate feature databased used to estimate nearest neighbor poses for each local image patch. The closest nearest neighbor pose to the test image is selected as the estimated three-dimensional pose.

Image indexing and retrieval using local image patches for object three-dimensional pose estimation

A method for identifying a feature in a first image comprises establishing an initial database of image triplets, and in a pose estimation processor, training a deep learning neural network using the initial database of image triplets, calculating a pose for the first image using the deep learning neural network, comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network, creating a new set of training data including a plurality of error cases identified in a plurality of input images and retraining the deep learning neural network using the new set of training data. The deep learning neural network may be iteratively retrained with a series of new training data sets. Statistical analysis is performed on a plurality of error cases to select a subset of the error cases included in the new set of training data.

Method and system for efficiently mining dataset essentials with bootstrapping strategy in 6DOF pose estimate of 3D objects

Systems and methods are disclosed for processing an image to detect anomalous pixels. An image classification is received from a trained convolutional neural network (CNN) for an input image with a positive classification being defined to represent detection of an anomaly in the image and a negative classification being defined to represent absence of an anomaly. A backward propagation analysis of the input image for each layer of the CNN generates an attention mapping that includes a positive attention map and a negative attention map. A positive mask is generated based on intensity thresholds of the positive attention map and a negative mask is generated based on intensity thresholds of the negative attention map. An image of segmented anomalous pixels is generated based on an aggregation of the positive mask and the negative mask.

Weakly supervised anomaly detection and segmentation in images

A method for predicting short-term cloud coverage includes a computer calculating an estimated cloud velocity field at a current time value based on sky images. The computer determines a segmented cloud model based on the sky images, a future sun location corresponding to a future time value, and sun pixel locations at the future time value based on the future sun location. Next, the computer applies a back-propagation algorithm to the sun pixel locations using the estimated cloud velocity field to yield propagated sun pixel locations corresponding to a previous time value. Then, the computer predicts cloud coverage for the future sun location based on the propagated sun pixel locations and the segmented cloud model.

Short term cloud coverage prediction using ground-based all sky imaging

A short-term cloud forecasting system includes a cloud segmentation processor that receives image data from images captured by an all sky camera. The cloud segmentation processor calculates a probability for each pixel in an image that the pixel is representative of a cloud. A cloud motion estimation processor calculates a motion vector representing estimated cloud motion calculates weights representing the likelihood that the cloud motion will cause a cloud to occlude the sun at a time in the near future. An uncertainty processor calculates one or more uncertainty indexes that quantify the confidence that a cloud forecast is accurate. Combining the cloud probabilities, the global motion vector and the at least one uncertainty index, in a sun occlusion prediction processor produces a short-term cloud forecast based on the image data that may be used as input to control systems for controlling a power grid.

Short term cloud forecast, improved cloud recognition and prediction and uncertainty index estimation

Method and system for detecting defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect area of potential defect at location on surface of object based on predictable pattern consisting of bright and shadow regions. Kernels are defined corresponding to configurations of dark field illumination sources to enhance detecting potential defect. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypothesis of potential defect at location on surface of object. The hypothesis is classified with a classifier to determine whether the potential defect is true defect. The classifier is trained with training data having characteristics of true defect. The method provides efficient automated detection of micro defects on surface of object.

Method and system for detecting defects on surface of object

Method and system for detecting line defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect areas of potential defects based on predictable pattern consisting of bright and shadow regions. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypotheses of potential defects at locations on surface of object. The hypotheses are classified to determine whether the potential defects are true defects at the locations. Line defect is detected by refining line segments detected on the processed image based on criteria. The criteria include distance from the true defects to the line segments and slops between the true defects and the line segments are less than threshold values.

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Plainsboro, NJ, United States of America

Jan Ernst