Sap Se

Jonas Lipps

Eckehard Schmidt

Nemrude Verzano

Waqas Ahmad Farooqi

Thomas Fricke

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.

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Walldorf, Germany

Jonas Lipps