A system and method for human motion detection and tracking are disclosed. In one embodiment, an optical sensing instrument monitors a stage. A memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures a depth frame from the optical sensing instrument. The depth frame may include at each image element first coordinate values including a point related to a distance from the optical sensing instrument. The depth frame is converted into a designated depth frame format, which includes at each image element second coordinate values relative to the depth frame. Probability distribution models are applied to the designated depth frame format to identify a respective plurality of body parts. The position of each of the respective plurality of body parts in the designated depth frame is calculated as is the position of each of the plurality of body parts in the depth frame.

This disclosure relates to systems for assisting surgeons in implanting joint replacement implant components. One aspect provides a system for assisting a surgeon in implanting a joint replacement implant component during a surgery of replacing a joint. The system comprises: an instrument for medullary canal preparation; a video camera to capture image data of the instrument; a computer system to: store a surgical plan; determine a pose of the instrument relative to the bone or the joint based on the image data from the video camera; assess the pose of the instrument against the surgical plan; and provide an indication to the surgeon of a clinical consequence of the pose in relation to the surgical plan.

This disclosure relates to systems for assisting surgeons in implanting joint replacement implant components. One aspect provides a system for assisting a surgeon in implanting a joint replacement implant component during a surgery of replacing a joint. The system comprises: an instrument for medullary canal preparation; a video camera to capture image data of the instrument; a computer system to: store a surgical plan; determine a pose of the instrument relative to the bone or the joint based on the image data from the video camera; assess the pose of the instrument against the surgical plan; and provide an indication to the surgeon of a clinical consequence of the pose in relation to the surgical plan.

An additive manufacturing apparatus including a chamber, a manufacturing table, an imaging device, an image processing device, and a control device, in which a base plate disposed on the manufacturing table includes a first side and a second side, a first camera in the imaging device images a first region to acquire a first image and images a second region to acquire a second image, the image processing device analyzes the first and second images to acquire position information of each side, and the control device calculates a coordinate of an intersection point of the first side and the second side or an intersection point on extended lines of the first side and the second side as a point to be detected.

An additive manufacturing apparatus including a chamber, a manufacturing table, an imaging device, an image processing device, and a control device, in which a base plate disposed on the manufacturing table includes a first side and a second side, a first camera in the imaging device images a first region to acquire a first image and images a second region to acquire a second image, the image processing device analyzes the first and second images to acquire position information of each side, and the control device calculates a coordinate of an intersection point of the first side and the second side or an intersection point on extended lines of the first side and the second side as a point to be detected.

Provided are a device for a digital assay of targets according to an exemplary embodiment of the present disclosure and a method using the same. The digital assay method of targets according to the exemplary embodiment of the present disclosure includes acquiring an image for a plurality of microdroplets, predicting at least one region based on the image for the plurality of microdroplets using an artificial neural network-based prediction model configured to segment at least one region among positive microdroplets, negative microdroplets, and atypical microdroplets, with the image for the plurality of microdroplets as an input, determining a number for the plurality of microdroplets based on the at least one region, and providing quantitative data of targets based on the number for the plurality of microdroplets.

Provided are a device for a digital assay of targets according to an exemplary embodiment of the present disclosure and a method using the same. The digital assay method of targets according to the exemplary embodiment of the present disclosure includes acquiring an image for a plurality of microdroplets, predicting at least one region based on the image for the plurality of microdroplets using an artificial neural network-based prediction model configured to segment at least one region among positive microdroplets, negative microdroplets, and atypical microdroplets, with the image for the plurality of microdroplets as an input, determining a number for the plurality of microdroplets based on the at least one region, and providing quantitative data of targets based on the number for the plurality of microdroplets.

Methods and systems for crowd level estimation are provided. The system includes a plurality of performance modeling modules (206), an input module (202) and a crowd estimation technique integration module. The plurality of performance modeling modules (206) performance model each of a plurality of crowd estimation techniques based on an accuracy thereof at different crowd levels and/or at different locations. The input module (202) receives an image of a crowd. The crowd estimation technique integration module (208) selects one or more of the plurality of crowd estimation techniques in response to the performance modeling of the one or more of the plurality of crowd estimation techniques and an estimated crowd level and/or an estimated location. The crowd estimation technique integration module (208) then estimates a crowd count of the crowd in the received image in accordance with the selected one or more of the plurality of crowd estimation techniques.

Methods and systems for crowd level estimation are provided. The system includes a plurality of performance modeling modules (206), an input module (202) and a crowd estimation technique integration module. The plurality of performance modeling modules (206) performance model each of a plurality of crowd estimation techniques based on an accuracy thereof at different crowd levels and/or at different locations. The input module (202) receives an image of a crowd. The crowd estimation technique integration module (208) selects one or more of the plurality of crowd estimation techniques in response to the performance modeling of the one or more of the plurality of crowd estimation techniques and an estimated crowd level and/or an estimated location. The crowd estimation technique integration module (208) then estimates a crowd count of the crowd in the received image in accordance with the selected one or more of the plurality of crowd estimation techniques.

It is possible to determine a geometric transformation matrix representing geometric transformation between an input image and a template image with high precision. A geometric transformation matrix/inlier estimation section 32 determines a corresponding point group serving as inliers, and estimates the geometric transformation matrix representing the geometric transformation between the input image and the template image. A scatter degree estimation section 34 estimates scatter degree of the corresponding points based on the corresponding point group serving as inliers. A plane tracking convergence determination threshold calculation section 36 calculates a threshold used in convergence determination when iterative update of the geometric transformation matrix in a plane tracking section 38 is performed based on the estimated scatter degree. The plane tracking section 38 iterates update of the geometric transformation matrix so as to minimize a difference in value to pixels of a geometric transformation image obtained by transforming one of the input image and the templated image using the geometric transformation matrix and corresponding pixels until it is determined that convergence has been completed using the calculated threshold.