An image segmentation method includes: obtaining an image to be segmented containing a target object; performing at least one image feature fusion processing on associated feature points based on the image to be segmented, and extracting global feature information during each image feature fusion processing, wherein the associated feature points are at least two feature points having a location association relation; and determining, based on the global feature information extracted, a segmentation mask for the target object.

A method for detecting an object in an image includes: obtaining an image to be detected; generating a plurality of feature maps based on the image to be detected by a plurality of feature extracting networks in a neural network model trained for object detection, in which the plurality of feature extracting networks are connected sequentially, and input data of a latter feature extracting network in the plurality of feature extracting networks is based on output data and input data of a previous feature extracting network; and generating an object detection result based on the plurality of feature maps by an object detecting network in the neural network model.

A method and system is described which attempts to address the technical problems involved in analyzing images using advanced computer systems and making decisions about the future of a damaged automobile based on the images.

Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive a first image acquired by a first camera and a second image acquired by a second camera onboard the host vehicle; identify a first representation of an object in the first image and a second representation of the object in the second image; input to a first trained model at least a portion of the first image; input to a second trained model at least a portion of the second image; receive the first signature encoding determined by the first trained model and the second signature encoding determined by the second trained model; input to a third trained model the first signature encoding and the second signature encoding; and receive an indicator of a location of the object determined by the third trained model.

The present teaching relates to detecting palm liveness. When an image is received with visual information claimed to represent a palm of a person, an initial region of interests (ROI) is identified from the image that corresponds to the palm and an initial dimension thereof is determined. When the initial dimension is smaller than a specified dimension, the initial ROI is extended in some respective directions to some expansion region with certain expansion dimension to generate an ROI using the visual information in the ROI from the image. A plurality of decisions are obtained with respect to the ROI, each of which is made individually on whether the ROI represents a specific type of fake palm. The decisions are then combined to derive a liveness detection decision on whether the palm captured in the image is live.

A method for detecting content of interest to a user includes obtaining a first data stream indicative of eye movement and/or gaze direction of the user as the user is viewing a scene in a field of view of the user, obtaining a second data stream indicative of visual content in the field of view of the user, determining, based on the first data stream and the second data stream, that content of interest to the user is present in the scene in the field of view of the user, and, in response to determining that content of interest to the user is present in the scene in the field of view of the user, triggering, with the processor, an operation to be performed with respect to the scene in the field of view of the user.

The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium, and the image processing method includes: acquiring an input image; detecting a target area in the input image; and processing the target area, wherein the processing of the target area includes: obtaining a feature map of the target area, rearranging feature blocks in the feature map in a feature space, and obtaining an output image after the target area is processed based on the rearranged feature blocks and the feature map.

Embodiments of the invention provide systems and methods of generating a complete and accurate geometrically optimized environment. Stereo pair images depicting an environment are selected from a plurality of images to generate a Digital Surface Model (DSM). Characteristics of objects in the environment are determined and identified. The geometry of the objects may be determined and fit with polygons and textured facades. By determining the objects, the geometry, and the material from original satellite imagery and from a DSM created from the matching stereo pair point clouds, a complete and accurate geometrically optimized environment is created.

Methods, systems, and computer program products are provided for signature verification. Signature verification may be provided for target signatures using genuine signatures. A signature verification model pipeline may extract features from a target signature and a genuine signature, encode and submit both to a neural network to generate a similarity score, which may be repeated for each genuine signature. A target signature may be classified as genuine, for example, when one or more similarity scores exceed a genuine threshold. A signature verification model may be updated or calibrated at any time with new genuine signatures. A signature verification model may be implemented with multiple trainable neural networks (e.g., for feature extraction, transformation, encoding, and/or classification).

Systems and methods are provided for computer aided phenotyping of fibrosis-related conditions. A digital image indicates presence of collagens in a biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the collagens that is expected to be different for different phenotypes of fibrosis. At least some features are tissue level features that describe macroscopic characteristics of the collagens, morphometric level features that describe morphometric characteristics of the collagens, and texture level features that describe an organization of the collagens. At least some of the plurality of parameters are statistics associated with histograms corresponding to distributions of the associated parameters across at least some of the digital image. At least some of the plurality of parameters are combined to obtain one or more composite scores that quantify a phenotype of fibrosis for the biological tissue sample.