Provided are a method and device for training an image processing network, and an image processing method and device. The method for training an image processing network includes following. A reference pixel is determined based on a training image annotated with a truth value. With the reference pixel as a starting point and based on a Markov chain of the training image, cropping probabilities of the image processing network processing the training image are determined. A network parameter value and the cropping probabilities of the image processing network are adjusted based on an output result obtained by the image processing network processing a training cropped area and the truth value, to obtain a trained image processing network. The training cropped area is obtained by cropping the training image based on the cropping probabilities.

A client device is configured to (i) based on initial sensor data and a road network graph maintained on the client device, determine a set of particles corresponding to the road network graph, each particle including a (a) trajectory along the road network graph, (b) position and velocity, and (c) probability, (ii) identify a particle with a highest probability, (iii) based on the identified particle, determine the location of the client device in the road network graph, (iv) after receiving new sensor data, extend the trajectory of each particle, (v) based on the new sensor data, update, for each particle (a) the position and velocity and (b) the probability, (vi) identify a particle from the second updated set of particles with a highest probability, and (vii) based on the identified particle, determine the updated location of the client device in the road network graph.

Methods, devices, and systems for false target detection for airport traffic control are described herein. One device includes a user interface, a memory, and a processor configured to execute executable instructions stored in the memory to receive one or more sensor reports from one or more sensors, aggregate data that corresponds to a particular target from the one or more sensor reports, determine the particular target is a false target responsive to only one of the sensor reports including data that corresponds to the particular target, and display the particular target as a false target on the user interface responsive to determining the particular target is a false target.

Automated analog gauge reading is provided. The method comprises a computer system receiving input of an image and detecting at least one analog gauge in the image. The computer system corrects the orientation of the analog gauge in the image and detects scene text and tick labels on the analog gauge. The computer system determines a position of a pointer on the analog gauge relative to the scene text and outputs a gauge reading value based on an arithmetic progression of tick labels and angle of the pointer with respect to minimum and maximum values on the analog gauge.

A method of visualising a meeting between one or more participants on a display includes, in an electronic processing device, the steps of: determining a plurality of signals, each of the plurality of signals being at least partially indicative of the meeting; generating a plurality of features using the plurality of signals, the features being at least partially indicative of the signals; generating at least one of: at least one phase indicator associated with the plurality of features, the at least one phase indicator being indicative of a temporal segmentation of at least part of the meeting; and at least one event indicator associated with the plurality of features, the at least one event indicator being indicative of an event during the meeting. The method also includes the step of causing a representation indicative of the at least one phase indicator and/or the at least one event indicator to be displayed on the display to thereby provide visualisation of the meeting.