Abstract: A computer implemented method of processing image data by an image processing system comprising a directed acyclic graph of nodes for receiving and processing image data is disclosed. The method comprises: at a compute node in the graph: receiving image data; performing an image processing operation on the image data to produce a compute node output based on the data; and transmitting the compute node output to another node in the graph. The method also comprises, at a control node in the graph: receiving a control node input, wherein the control node input is the image data or is based on the image data; and, if the control node determines that a control condition is satisfied, transmitting the control node input to another node in the graph. A computer implemented image processing system is also disclosed.

Abstract: Provided is machine learning apparatus comprising: a dataset for input to a training procedure of a machine learning model; data capture logic operable to capture from an object at least one datum for inclusion in the dataset; association logic operable to derive an additional characteristic of the object; annotator logic operable in response to the data capture logic and the association logic to create an annotation linking the additional characteristic with the at least one datum; storage logic operable to store the or each datum with an associated annotation in the dataset; and input logic to supply the dataset as machine learning input.

Abstract: A computing network has a sensor, a first processor in a first computing network location, and a second processor in a second computing network location, the second computing network location further from the sensor than the first computing network location. The first processor is configured to receive sensor data from the sensor and configured to operate a first machine learning model to make a first inference based on the sensor data. The second processor is configured to receive the sensor data and to operate a second machine learning model to make a second inference based on the sensor data in response to a trigger. The computing network is configured to collate and process the first and second inferences to make an aggregated inference.

Abstract: System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.

Abstract: A computing network has a sensor, a first processor in a first computing network location, and a second processor in a second computing network location, the second computing network location further from the sensor than the first computing network location. The first processor is configured to receive sensor data from the sensor and configured to operate a first machine learning model to make a first inference based on the sensor data. The second processor is configured to receive the sensor data and to operate a second machine learning model to make a second inference based on the sensor data in response to a trigger. The computing network is configured to collate and process the first and second inferences to make an aggregated inference.

Abstract: There is provided a computer-implemented method of processing object detection data. The method includes receiving, from an object detection system, object detection data comprising a plurality of detection outputs associated with different respective regions of an image, wherein a first detection output of the plurality of detection outputs is associated with a first region of the image and comprises a plurality of received detection characteristics. The method includes processing the first detection output to determine one or more modified detection characteristics of said plurality of received detection characteristics. Processing the first detection output includes retrieving a mapping function and applying the mapping function, where the mapping is dependent upon at least one of the plurality of received detection characteristics.

Abstract: There is provided a computer-implemented method of processing object detection data. The method includes receiving, from an object detection system, object detection data comprising a plurality of detection outputs associated with different respective regions of an image, wherein a first detection output of the plurality of detection outputs is associated with a first region of the image and comprises a plurality of received detection characteristics. The method includes processing the first detection output to determine one or more modified detection characteristics of said plurality of received detection characteristics. Processing the first detection output includes retrieving a mapping function and applying the mapping function, where the mapping is dependent upon at least one of the plurality of received detection characteristics.