The present disclosure is directed to identifying shapes in an image. For example, a shape identification system may identify an unknown shape represented by a Bzier path that has at least one Bzier curve. The shape identification system may also identify a stored Bzier path that has at least one stored Bzier curve, for example, in a database of known shapes. Using the Bzier curve of the unknown shape and the stored Bzier curve of the known shape, the shape identification system can determine a transformation matrix that transforms the transforms the Bzier curve of unknown shape to the stored Bzier curve of the known shape. Then, the shape identification system can compare the transformed Bzier curve to the stored Bzier curve to determine whether the unknown shape matches the known shape.

Provided is a method for extracting an image salient curve. The method comprises the following steps: drawing an approximate curve along a salient edge of an image from which a salient curve is to be extracted; obtaining short edges in the image; calculating a harmonic vector field by using the drawn curve as a boundary condition; filtering the short edges in the image by using the harmonic vector field; updating the vector field by using the short edges left in the image as boundary conditions; and obtaining an optimal salient curve of the image by using the energy of a minimized spline curve in the vector field. Also provided is a system for extracting an image salient curve. The image salient curve can ensure the smoothness and a bending characteristic.

A custom arthroplasty guide and a method of manufacturing such a guide are disclosed herein. The method of manufacturing the custom arthroplasty guide includes: a) generating medical imaging slices of the portion of the patient bone; b) identifying landmarks on bone boundaries in the medical imaging slices; c) providing model data including image data associated with a bone other than the patient bone; d) adjusting the model data to match the landmarks; e) using the adjusted model data to generate a three dimensional computer model of the portion of the patient bone; f) using the three dimensional computer model to generate design data associated with the custom arthroplasty guide; and g) using the design data in manufacturing the custom arthroplasty guide.

The present disclosure is directed to identifying shapes in an image. For example, a shape identification system may identify an unknown shape represented by a Bzier path that has at least one Bzier curve. The shape identification system may also identify a stored Bzier path that has at least one stored Bzier curve, for example, in a database of known shapes. Using the Bzier curve of the unknown shape and the stored Bzier curve of the known shape, the shape identification system can determine a transformation matrix that transforms the transforms the Bzier curve of unknown shape to the stored Bzier curve of the known shape. Then, the shape identification system can compare the transformed Bzier curve to the stored Bzier curve to determine whether the unknown shape matches the known shape.

In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operatione.g., Bezier curve fittingto identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniquessuch as clustering and temporal smoothingmay be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarkse.g., lane line, road boundary line, crosswalk, pole, text, etc.may be used by a vehicle to perform one or more operations for navigating an environment.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operatione.g., Bezier curve fittingto identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniquessuch as clustering and temporal smoothingmay be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarkse.g., lane line, road boundary line, crosswalk, pole, text, etc.may be used by a vehicle to perform one or more operations for navigating an environment.

The present disclosure provides a control method, a vehicle, and a storage medium, wherein the control method comprises: determining lane line information according to image information or map information; determining a parking trajectory according to the lane line information; and controlling the vehicle according to the parking trajectory. In the method, the problem that the vehicle, when the autonomous driving system fails, cannot be safely parked is solved; the image information or the map information is taken as auxiliary information for safe parking, lane line information of a road where the vehicle is located is determined according to the image information or the map information, and assisted parking is performed through the lane line information. The parking trajectory is determined through the lane line information, the vehicle is controlled according to the parking trajectory, and the safe parking of the vehicle is achieved.