The invention supports creation of models for recognizing attributes in an image with high accuracy. An image recognition support apparatus includes an image input unit configured to acquire an image, a pseudo label generation unit configured to recognize the acquired image based on a plurality of types of image recognition models and output recognition information, and generate pseudo labels indicating attributes of the acquired image based on the output recognition information, and a new label generation unit configured to generate new labels based on the generated pseudo labels.

A method for operating a dental appliance by providing a dental appliance including at least one motion sensor that provides motion data, the motion sensor is one of an orientation sensor, an acceleration sensor, and an inertial sensor. Further, providing at least one camera associated with the dental appliance that provides image data, and segmenting the image data received from the camera to segment a surface condition using a first machine learning neural network to generate a surface condition segmentation. Then classifying the motion data received from the motion sensor and the image data received from the camera to classify motion and position of the dental appliance with respect to a surface of a user's anatomy, using a second machine learning neural network classifier to generate a surface position comprising at least one of a relational motion classification or a relational position classification. Finally, generating user treatment progress condition and position based on the surface condition and surface position; and communicating the treatment progress condition and position to the user.

Exemplary systems and methods are disclosed for detecting embedded data in a digital image. The system includes a processing device that extracts one or more features from a digital image and analyzes the one or more extracted features in a plurality of steganography analyzers, each steganography analyzer configured to execute a different steganography algorithm. The processing device generates an output data value at each steganography analyzer, the output data value indicating a probability that the digital image includes steganography according to the steganography algorithm of the steganography analyzer. Each output probability value is fed to an ensemble classifier, the ensemble classifier including a neural network in which the output probability values of the plurality of steganography analyzers are ensembled together to generate an output ensemble data value indicating a probability that the digital image includes any steganography according to the steganography algorithms of the steganography analyzers.

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

A pilot may be more stressed during take-off or landing, which is not abnormal. Physiological data of the pilot may be received. Placing the physiological data in context of the current situation may be advantageous in detecting anomalous behaviors of the pilot. A system and method are described. The system and method receive a stream of images from a camera and detect whether the pilot is exhibiting anomalous behavior. The anomalous behavior is further put into context based on the flight state and various avionics information.

Among other things, we describe techniques for automatically selecting data samples for annotation. The techniques use bounding box prediction based on a bounding box score distribution, spatial probability density determined from bounding box sizes and positions and an ensemble score variance determined from outputs of multiple machine learning models to select data samples for annotation. In an embodiment, temporal inconsistency cues are used to select data samples for annotation. In an embodiment, digital map constraints or other map-based data are used to exclude data samples from annotation. In an exemplary application, the annotated data samples are used to train a machine learning model that outputs perception data for an autonomous vehicle application.

Disclosed are a method and an apparatus of processing image. The method includes: obtaining an initial bone segmentation result; and fusing the initial bone segmentation result based on characteristics of and correspondences between a plurality of bone segmentation results in the initial bone segmentation result, to obtain a target bone segmentation result. The initial bone segmentation result includes the plurality of bone segmentation results generated by a plurality of different deep learning models. Methods in the embodiments of the present application can improve precision of a fusion result of the plurality of bone segmentation results.

Computer-implemented methods for detecting objects within digital image data based on color transitions include: receiving or capturing a digital image depicting an object; sampling color information from a first plurality of pixels of the digital image, wherein each of the first plurality of pixels is located in a background region of the digital image; optionally sampling color information from a second plurality of pixels of the digital image, wherein each of the second plurality of pixels is located in a foreground region of the digital image; assigning each pixel a label of either foreground or background using an adaptive label learning process; binarizing the digital image based on the labels assigned to each pixel; detecting contour(s) within the binarized digital image; and defining edge(s) of the object based on the detected contour(s). Corresponding systems and computer program products configured to perform the inventive methods are also described.

An image processing device includes a division circuit that divides an input image and outputs a plurality of divided images, a first processor that performs computation of an object detection model and acquires attribute information including an attribute value of an object included in each of the divided images and a first quadrangular frame surrounding the object as first metadata, a scaling circuit that output an overall image obtained by shrinking the input image, a second processor that performs computation of the object detection model and acquires attribute information including an attribute value of an object included in the overall image and a second quadrangular frame as second metadata, and a third processor that generates third metadata of the input image by combining pieces of the attribute information of the second metadata and pieces of attribute information that are not held in common by the first and second metadata.

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.