Systems and methods for classifying blood cells in a blood sample are disclosed. A series of frames of the blood sample as it flows through a field of view of an image capture device are captured and analysed. Advantageously, the disclosed systems and methods combine the availability of morphological cell data with the convenience of a flow-through arrangement. The classification results can be used for estimating cell counts in a blood sample.

The present disclosure provides a method and apparatus of obtaining obstacle information, a device and a computer storage medium, wherein the method of obtaining obstacle information comprises: obtaining scenario data synchronously collected by a laser radar device and a camera device; using point cloud top view data in the scenario data to perform obstacle recognition to obtain a point cloud set of candidate obstacles; using fusion features of the scenario data corresponding to the point cloud set of the candidate obstacles to perform obstacle recognition to obtain specific obstacle information. The technical solution according to the present disclosure enables accurate obtainment of specific obstacle information, thereby enhances the sensing capability of the self-driving vehicle and improves safety of the self-driving vehicle.

An image analysis apparatus including a processor configured to: acquire a first fluorescence image indicating an observation target including a plurality of types of cells, each of which has a first region stained by first staining, and a second fluorescence image indicating the observation target in which a second region of a specific cell among the plurality of types of cells is stained by second staining different from the first staining; determine whether or not the first region is included in the second region in a superimposed image obtained by superimposing the first fluorescence image and the second fluorescence image and acquire a first determination result for each of the first regions; and determine the type of cell included in the observation target on the basis of the first determination result.

An apparatus for detecting morphed or averaged images, wherein the morphed or averaged images are synthetically generated images including information from two or more different source images corresponding to two or more subjects. The apparatus may include a feature extraction module for receiving an input image and outputting a set of descriptor feature(s) characteristic of the image and a classifier module configured to allocate the input image either to a first class indicating that the image has been morphed or averaged or a second class indicating that it has not been morphed or averaged, based on the descriptor feature(s). The feature extraction module may include a plurality of neural networks providing complementary descriptor feature(s) to the classifier module. The apparatus further may include a fusion module for combining descriptor feature data from each neural network and transmitting the fused feature data to the classifier module.

This application provides a target detection method and device based on a neural network and training method and device of a neural network for target detection. The target detection method comprises: acquiring a to-be-detected image that contains a target; acquiring first feature information of the to-be-detected image by use of a first neural network, acquiring second feature information of the to-be-detected image by use of a second neural network; combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result by use of the second neural network and based on the combined feature information, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information.

A method for controlling unlocking and related products are provided. An electronic device includes at least one processor and a computer readable storage coupled to the at least one processor. The computer readable storage stores at least one computer executable instruction thereon, which when executed by the at least one processor, cause the at least one processor to carry out actions, including: obtaining a face image; carrying out a group of rough features and a group of fine features from the face image; carrying out a verification operation for the group of rough features and the group of the fine features; carrying out a next unlocking process when verification of the group of rough features and the group of fine features is passed.

The systems, methods, and devices described herein generally relate to achieving accurate and robust motion correction by detecting and accounting for false movements in motion correction systems used in conjunction with medical imaging and/or therapeutic systems. In other words, in some embodiments of the systems, methods, and devices described herein can be configured to detect false movements for motion correction during a medical imaging scan and/or therapeutic procedure, and thereby ensure that such false movements are not accounted for in the motion correction process. Upon detection of false movements, the imaging or therapeutic system can be configured to transiently suppress and/or subsequently repeat acquisitions.

A method and an apparatus for pattern recognition is provided in the present invention, applied to the field of artificial intelligence. The method includes: acquiring a two-dimensional image of a target object and a two-dimensional feature of the target object according to the two-dimensional image of the target object; and acquiring a three-dimensional image of the target object and a three-dimensional feature of the target object according to the three-dimensional image of the target object; identifying the target object according to the two-dimensional feature and the three-dimensional feature of the target object. The method can reduce restrictions on acquiring the image of the target object, for example, reduce the restrictions on the image of the target object in terms of postures, lighting, expressions, make-up and occlusion, thereby improving an accuracy of recognizing the target object and improving a recognition rate and reducing recognition time at the same time.

Provided are systems and methods that perform multi-task and/or multi-sensor fusion for three-dimensional object detection in furtherance of, for example, autonomous vehicle perception and control. In particular, according to one aspect of the present disclosure, example systems and methods described herein exploit simultaneous training of a machine-learned model ensemble relative to multiple related tasks to learn to perform more accurate multi-sensor 3D object detection. For example, the present disclosure provides an end-to-end learnable architecture with multiple machine-learned models that interoperate to reason about 2D and/or 3D object detection as well as one or more auxiliary tasks. According to another aspect of the present disclosure, example systems and methods described herein can perform multi-sensor fusion (e.g., fusing features derived from image data, light detection and ranging (LIDAR) data, and/or other sensor modalities) at both the point-wise and region of interest (ROI)-wise level, resulting in fully fused feature representations.

Mechanisms are provided for implementing a patient complexity classification (PCC) computing system. The PCC computing system receives medical image study data for a patient that comprises one or more medical image data structures and one or more corresponding medical image metadata data structures. A natural language processing engine of the PCC computing system performs natural language processing on the medical image metadata data structure to extract features indicative of at least one of patient or medical image characteristics. A complexity classifier of the PCC computing system evaluates the extracted features to determine a patient complexity indicating a complexity of a medical condition of the patient. Routing logic associated with the PCC computing system routes the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity.