Abstract: A system and method for semi-supervised learning of visual recognition networks includes generating an initial set of feature representation training data based on simulated 2D test images of various viewpoints with respect to a target 3D rendering. A feature representation network generates feature representation vectors based on processing of the initial feature representation training data. Keypoint patches are labeled according to a score value based on a series of reference patches of unique viewpoint poses and a test keypoint patch processed through the trained feature representation network. A keypoint detector network learns keypoint detection based on processing of the keypoint detector training data.

Abstract: A method for image processing is provided. The method may include: obtaining an original image of a first style, the original image being generated by a first imaging device; obtaining a target transformation model; and generating a transferred image of a second style by transferring the first style of the original image using the target transformation model. The second style may be substantially similar to a target style of one or more other images generated by a second imaging device. The second style may be different from the first style.

Abstract: Methods and systems for locating one or more target features of a patient. For example, a computer-implemented method includes receiving a first input image; receiving a second input image; generating a first patient representation corresponding to the first input image; generating a second patient representation corresponding to the second input image; determining one or more first features corresponding to the first patient representation in a feature space; determining one or more second features corresponding to the second patient representation in the feature space; joining the one or more first features and the one or more second features into one or more joined features; determining one or more landmarks based at least in part on the one or more joined features; and providing a visual guidance for a medical procedure based at least in part on the information associated with the one or more landmarks.

Abstract: A system for patient positioning is provided. The system may acquire image data relating to a patient holding a posture and a plurality of patient models. Each patient model may represent a reference patient holding a reference posture, and include at least one reference interest point of the referent patient and a reference representation of the reference posture. The system may also identify at least one interest point of the patient from the image data using an interest point detection model. The system may further determine a representation of the posture of the patient based on a comparison between the at least one interest point of the patient and the at least one reference interest point in each of the plurality of patient models.

Abstract: Methods and systems for providing guidance for adjusting a target. For example, a computer-implemented method for providing guidance for adjusting a target includes: receiving, by a neural network, a reference image; receiving, by the neural network, the target image, the target image being related to a position of a target; determining a similarity metric based at least in part on information associated with the reference image and information associated with the target image by the neural network; generating a target attention map corresponding to the target image based at least in part on the similarity metric; outputting the target image and the target attention map; and providing a guidance for adjusting the position of the target based at least in part on the target image and the target attention map.

Abstract: The present disclosure relates to systems and methods for anomaly detection for a medical procedure. The method may include obtaining image data collected by one or more visual sensors via monitoring a medical procedure and a trained machine learning model for anomaly detection. The method may include determining a detection result for the medical procedure based on the image data using the trained machine learning model. The detection result may include whether an anomaly regarding the medical procedure exists. In response to the detection result that the anomaly exists, the method may further include providing feedback relating to the anomaly.

Abstract: A method for learning image representations comprises receiving a pair of images, generating a set of candidate patches in each image, identifying features in each patch, arranging the patches in pairs and comparing a distance between a feature in the first image to a feature in the second image. The pair of patches is labeled as positive or negative based on the comparison of the measured distance to a threshold. Images may be depth images and distance is determined by projecting the features into three-dimensional space. A system for learning representations includes a computer processor configured to receive a pair of images to a Siamese convolutional neural network to generate candidate patches in each image. A sampling layer arranges the patches in pairs and measures distances between features in the patches. Each pair is labeled as positive or negative according to the comparison of the distance to a threshold.

Abstract: The present disclosure relates to systems and methods for scanning a patient in an imaging system. The imaging system may include at least one camera directed at the patient. The systems and methods may obtain a plurality of images of the patient that are captured by the at least one camera. Each of the plurality of images may correspond to one of a series of time points. The systems and methods may also determine a motion of the patient over the series of time points based on the plurality of images of the patient. The systems and methods may further determine whether the patient is ready for scan based on the motion of the patient, and generate control information of the imaging system for scanning the patient in response to determining that the patient is ready for scan.

Abstract: A method for enhancing facial/object recognition includes receiving a query image, and providing a database of object images, including images relevant to the query image, each image having a first attribute and a second attribute with each of the first attribute and the second attribute having a first state and a second state. The method also includes creating an augmented database by generating a plurality of artificial images for each image in the database, the artificial images cooperating with the image to define a set of images including every combination of the first attribute and the second attribute in each of the first state and the second state, and comparing the query image to the images in the augmented database to find one or more matches.

Abstract: The present embodiments relate to automatically estimating a three]dimensional pose of an object from an image captured using a camera with a structured light sensor. By way of introduction, the present embodiments described below include apparatuses and methods for training a system for and estimating a pose of an object from a test image. Training and test images are sampled to generate local image patches. Features are extracted from the local image patches to generate feature databased used to estimate nearest neighbor poses for each local image patch. The closest nearest neighbor pose to the test image is selected as the estimated three]dimensional pose.