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Eckehard Schmidt

Waqas Ahmad Farooqi

Nemrude Verzano

Jonas Lipps

Thomas Fricke

Michael Jaehnisch

Filip Perisic

Ajinkya Patil

Jochim Fibich

Methods, systems, and computer-readable storage media for providing a binary classifier include receiving a biased dataset, the biased data set including a plurality of records, each record being assigned to a class of a plurality of classes, one class including a majority class, performing data engineering on at least a portion of the biased dataset to provide a revised dataset, providing a trained deep autoencoder (DAE) by training a DAE using only records assigned to the majority class from the revised dataset, the trained DAE including a binary classifier that classifies records into one of the majority class and a minority class, validating the trained DAE using validation data that is based on at least a portion of the biased dataset, and providing the trained DAE for production use within a production system.

Generic workflow for classification of highly imbalanced datasets using deep learning

Combined color and depth data for a field of view is received. Thereafter, using at least one bounding polygon algorithm, at least one proposed bounding polygon is defined for the field of view. It can then be determined, using a binary classifier having at least one machine learning model trained using a plurality of images of known objects, whether each proposed bounding polygon encapsulates an object. The image data within each bounding polygon that is determined to encapsulate an object can then be provided to a first object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon. Further, the image data within each bounding polygon that is determined to encapsulate an object is provided to a second object classifier having at least one machine learning model trained using a plurality of images of known objects, to classify the object encapsulated within the respective bounding polygon. A final classification for each bounding polygon is then determined based on the output of the first classifier machine learning model and the output of the second classifier machine learning model.

Object classification in image data using machine learning models

RGB-D data generated by at least one optical sensor for a field of view is received. Thereafter, the RGB-D data is bifurcated into (i) RGB data and (ii) depth data for the field of view. One or more bounding polygons are defined within the depth data that each characterize a window within the field of view encapsulating an object. The RGB data is then cropped using the bounding polygon(s). Image processing can later be applied to the cropped RGB data to identify at least one object therein. Related apparatus, systems, techniques and articles are also described.

Object detection in image data using depth segmentation

Image color data for a field of view is received. Thereafter, color segmentation can be performed on the image color data to define at least one bounding polygon that minimizes an amount of free space within each bounding polygon. The at least one bounding polygon is then used to crop the image color data to result in cropped image color data. Image processing can then be applied to the cropped image color data to identify at least one object therein. Related apparatus, systems, techniques and articles are also described.

Object detection in image data using color segmentation

A system and method of purchasing goods and arranging delivery. The system receives, from a mobile device within a physical store, identifications, made by a customer using the mobile device, of at least two items in the physical store as items to be purchased. The items to be purchased include a first set of items to be delivered and a second set of items not to be delivered. The system receives, from the mobile device, a delivery address for a first set of items. The system receives payment for the first set of items and the second set of items in a single transaction. The system places items corresponding to the first set of items in a delivery pipeline for subsequent physical delivery to the delivery address and authorizes removal of the second set of items from the physical store.

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Walldorf, Germany

Eckehard Schmidt