A method and apparatus for processing image data is provided. The method includes the steps of employing a main processing network for classifying one or more features of the image data, employing a monitor processing network for determining one or more confusing classifications of the image data, and spawning a specialist processing network to process image data associated with the one or more confusing classifications.

A method and apparatus for processing image data is provided. The method includes the steps of employing a main processing network for classifying one or more features of the image data, employing a monitor processing network for determining one or more confusing classifications of the image data, and spawning a specialist processing network to process image data associated with the one or more confusing classifications.

Methods and apparatus for applying data analytics such as deep learning algorithms to sensor data. In one embodiment, an electronic device such as a camera apparatus including a deep learning accelerator (DLA) communicative with an image sensor is disclosed, the camera apparatus configured to evaluate unprocessed sensor data from the image sensor using the DLA. In one variant, the camera apparatus provides sensor data directly to the DLA, bypassing image signal processing in order to improve the effectiveness the DLA, obtain DLA results more quickly than using conventional methods, and further allow the camera apparatus to conserve power.

Methods and apparatus for applying data analytics such as deep learning algorithms to sensor data. In one embodiment, an electronic device such as a camera apparatus including a deep learning accelerator (DLA) communicative with an image sensor is disclosed, the camera apparatus configured to evaluate unprocessed sensor data from the image sensor using the DLA. In one variant, the camera apparatus provides sensor data directly to the DLA, bypassing image signal processing in order to improve the effectiveness the DLA, obtain DLA results more quickly than using conventional methods, and further allow the camera apparatus to conserve power.

According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.

A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first portion of labeled associated text reports; and re-training the text report machine learning model using the selected first portion of labeled associated text reports.

An object of the present disclosure is to provide an information processing apparatus, an information processing system, an information processing method, and an information processing program capable of achieving efficient use of training data. An information processing apparatus according to the present disclosure includes: a recognition unit (101) that performs object recognition processing using sensor information acquired by a sensor, the object recognition processing being performed by a first recognizer that has been pretrained; and a training data application determination unit (22d) that determines whether the sensor information is applicable as training data to a second recognizer different from the first recognizer.

A processing system includes a processor with hardware. The processor is configured to perform processing of acquiring a detection target image captured by an endoscope apparatus, controlling the endoscope apparatus based on control information, detecting a region of interest included in the detection target image based on the detection target image for calculating estimated probability information representing a probability of the detected region of interest, identifying the control information for improving the estimated probability information related to the region of interest within the detection target image based on the detection target image, and controlling the endoscope apparatus based on the identified control information.