Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

This invention provides a system and method that uses a hybrid model for transportation-based (e.g. maritime) visual event detection of events. In operation, video data is reduced by detecting change and exclusively transmitting images to the deep learning model when changes are detected, or alternatively, based upon a timer that samples at selected intervals. Relatively straightforward deep learning models are used, which operate on sparse individual frames, instead of employing complex deep learning models that operate on multiple frames/videos. This approach reduces the need for specialized models. Independent, rule-based classifiers are used, based on the output of the deep learning model into visual events that, in turn, allows highly specialized events to be constructed. For example, multiple detections can be combined into higher-level single events, and thus, the existence maintenance procedures, cargo activities, and/or inspection rounds can be derived from combining multiple events or multiple detections.

This invention provides a system and method that uses a hybrid model for transportation-based (e.g. maritime) visual event detection of events. In operation, video data is reduced by detecting change and exclusively transmitting images to the deep learning model when changes are detected, or alternatively, based upon a timer that samples at selected intervals. Relatively straightforward deep learning models are used, which operate on sparse individual frames, instead of employing complex deep learning models that operate on multiple frames/videos. This approach reduces the need for specialized models. Independent, rule-based classifiers are used, based on the output of the deep learning model into visual events that, in turn, allows highly specialized events to be constructed. For example, multiple detections can be combined into higher-level single events, and thus, the existence maintenance procedures, cargo activities, and/or inspection rounds can be derived from combining multiple events or multiple detections.

Provided are methods for scalable and realistic camera blockage dataset generation, which can include generating synthetic images depicting a blockage on or near an imaging sensor. The synthetic images may be created by combining one or more chroma key-extracted partial blockage image with one or more background images, the combination of which can provide a scalable blockage dataset. Metadata for each synthetic image can be generated along with the synthetic image, by annotating the portion of the synthetic image represented by the chroma key-extracted partial blockage image as constituting blockage. The synthetic images can be used to increase the accuracy of machine learning models trained to identify blockage by increasing the volume of data available for such training.

A method includes obtaining (such as accessing, receiving, acquiring, etc.), using at least one processor of an electronic device, a machine learning model trained to process input data and generate output data over at least one range of values associated with one or more control variables. The method also includes providing, using the at least one processor, specified input data to the machine learning model and providing, using the at least one processor, one or more specified values of the one or more control variables to the machine learning model. The one or more specified values of the one or more control variables are within the at least one range of values. The method further includes performing inferencing using the machine learning model to process the specified input data and generate specified output data. The inferencing is controlled based on the one or more specified values of the control variable(s).

Methods, systems, and computer-readable storage media for receiving input data including a set of entities of a first type and a set of entities of a second type, providing a set of features based on entities of the first type, the set of features including features expected to be included in entities of the second type, filtering entities of the second type based on the set of features to provide a sub-set of entities of the second type, and generating an output by processing the set of entities of the first type and the sub-set of entities of the second type through a ML model, the output comprising a set of matching pairs, each matching pair in the set of matching pairs comprising an entity of the set of entities of the first type and at least one entity of the sub-set of entities of the second type.

The present invention relates generally to object annotation, specifically to polygonal annotations of objects. Described are methods of annotating an object including steps of receiving an image depicting an object, generating a set of image features using a CNN encoder implemented on one or more computers, and producing a polygon object annotation via a recurrent decoder or a Graph Neural Network. The recurrent decoder may include a recurrent neural network, a graph neural network or a gated graph neural network. A system for annotating an object and a method of training an object annotation system are also described.

A threshing state management system includes an image capture unit 80 that captures an image of a threshed material threshed by a threshing apparatus, a state detection neural network 72 that outputs a threshing processing state in the threshing apparatus based on image input data generated based on the captured image from the image capture unit 80, a parameter determination unit 73 that determines a control parameter of the threshing apparatus based on the threshing processing state, and a threshing control unit TU that controls the threshing apparatus based on the control parameter.

A threshing state management system includes an image capture unit 80 that captures an image of a threshed material threshed by a threshing apparatus, a state detection neural network 72 that outputs a threshing processing state in the threshing apparatus based on image input data generated based on the captured image from the image capture unit 80, a parameter determination unit 73 that determines a control parameter of the threshing apparatus based on the threshing processing state, and a threshing control unit TU that controls the threshing apparatus based on the control parameter.