Model construction in a neural network for object detection
AssigneeScopito ApS
InventorsKen Isobe FALK, Jeanette B. PEDERSEN, Henrik LUNDORFF
Life of this patent
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Abstract
Exemplary computer-implemented method and system can be provided for constructing a model in a neural network for object detection in an unprocessed image, where the construction can be performed based on at least one image training batch. The exemplary model can be constructed by training one or more collective model variables in the neural network to classify the individual annotated objects as a member of an object class. The exemplary model, e.g., in combination with the set of specifications when implemented in a neural network, can perform object detection in an unprocessed image with probability of the object detection.
Claims17
1-15. (canceled)
A computer-implemented method for constructing a model in a neural network for object detection in an unprocessed image, the construction of the model being performed based on at least one image training batch, and the neural network configured with a set of specifications, the method comprising:
- establishing at least one image training batch which comprises at least one training image that includes one or more objects, wherein an individual object of the objects is a member of an object class;
- with a graphical user interface (GUI), displaying a training image from the image training batch;
- and
- iteratively performing:
- a) annotating the one or more objects in the training image via a user interaction so as to generate individually annotated one of more objects,
- b) associating an annotation with the object class for the annotated one or more objects in the training image via the user interaction,
- c) returning a user annotated image training dataset comprising the at least one training image with the annotated one or more objects, each individual one of the one or more annotated objects being associated with the object class;
- [NS-PA1]and
- d) generating the model by training one or more collective model variables in the neural network to classify the individual annotated one or more objects as a member of the object class,
- wherein, the model, together with the set of specifications when implemented in the neural network, is configured to effectuate the object detection in the unprocessed image with a particular probability of the object detection.
A computer-implemented method provided in a neural network for an object detection in an unprocessed image having a particular probability of the object detection, the method comprising:
- providing a generated model, the generation of the model being performed based on at least one image training batch, and the neural network configured with a set of specifications, comprising:
- establishing at least one image training batch which comprises at least one training image that includes one or more objects, wherein an individual object of the objects is a member of an object class;
- with a graphical user interface (GUI), displaying a training image from the image training batch;
- and
- iteratively performing:
- a) annotating the one or more objects in the training image via a user interaction so as to generate individually annotated one of more objects,
- b) associating an annotation with the object class for the annotated one or more objects in the training image via the user interaction,
- c) returning a user annotated image training dataset comprising the at least one training image with the annotated one or more objects, each individual one of the annotated one or more annotated objects associated with the object class, and
- d) generating the model by training one or more collective model variables in the neural network to classify the individual annotated one or more objects as a member of the object class,
- wherein, the model, together with the set of specifications when implemented in the neural network, is configured to effectuate the object detection in the unprocessed image with a particular probability of the object detection;
- establishing at least one unprocessed image batch that comprises at least one unprocessed image to be subject for the object detection;
- with the GUI, displaying one or more unprocessed images with a set of marked objects, each one of the marked objects being associated with the object class;
- performing the object detection in an unprocessed image;
- and
- returning the unprocessed image with the set of marked objects, each of the marked objects being associated with the object class.
Dependencies are inferred from the claim text.