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.

According to an embodiment, a reading system includes a first extractor and a reader. The first extractor extracts, from an image in which a meter is imaged, a first region surrounded with a first contour, and a second region surrounded with a second contour positioned outward of the first contour. The reader calculates a first indication based on the first region, calculates a second indication based on the second region, calculates a first score relating to the first indication based on the first region, and calculates a second score relating to the second indication based on the second region.

A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

A Shape Based Modeling Segmentation fits generated Bzier curves on to edges parsed from an object in an image, identifies the Bzier curves in predefined proximity having at least one of a geometric relationship and a reporting relationship with other Bzier curves in the predefined proximity; generates MetaBzier curves from the identified Bzier curves; and connects the MetaBzier curves to identify the object in the image.

Provided are a display processing apparatus, method, and program for displaying a region of a detection target object in an image in a manner intelligible to a user even if the contour or boundary of the detection target object is unclear. A transmitting/receiving unit (100) and an image generation unit (102), which function as an image acquisition unit, perform an image acquisition process for sequentially acquiring ultrasound images. A region extraction unit (106) extracts a rectangular region including an organ, which is a detection target object, from an acquired ultrasound image. A curve generation unit (108) generates, in the extracted rectangular region, a curve corresponding to the organ in the rectangular region. An image combining unit (109) combines the ultrasound image and the generated curve corresponding to the organ. A display control unit (110) causes a monitor (18) to display the ultrasound image combined with the curve.

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.

A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

Facilitating analysis of user interface gesture patterns is described. In example implementations, a computing device acquires data that describes a user interface gesture pattern, such as finger movements on a touchscreen, in terms of pixels of a visual display. A repetitive arrangement of polygons, such as a grid of rectangles, is logically overlaid on the visual display. The computing device transforms the pixel-based data into polygon-based data that represents the gesture pattern in terms of polygons traversed by the gesture pattern. The computing device also converts the polygon-based data into text-based data such that the gesture pattern is represented by textual characters, such as a text string. The text string can include, for instance, a list of polygons traversed by the gesture pattern. The text-based data is forwarded to a service that can efficiently analyze relationships that may exist among multiple gesture patterns across multiple devices or end users.

The present invention extends to methods, systems, and computer program products for detecting available parking spaces in a parking environment. Radar systems are utilized to gather data about a parking lot environment. The radar data is provided to a neural network model as an input. Algorithms employing neural networks can be trained to recognize parked vehicles and conflicting data regarding debris, shopping carts, street lamps, traffic signs, pedestrians, etc. The neural network model processes the radar data to estimate parking space boundaries and to approximate the parking space boundaries as splines. The neural network model outputs spline estimations to a vehicle computer system. The vehicle computer system utilizes the spline estimates to detect available parking spaces. The spline estimates are updated as the vehicle navigates the parking environment.

The present invention relates to a system for extracting position information of object in point cloud data by using component comprising a component file creating unit making the component file of prior standardized object; an object position information extraction unit extracting position information of object by using point cloud data collected by LiDAR; and an object outline extraction and position determination unit optimizing object outline of point cloud data by using the component file, and extracting accurate information of standard position from the optimized outline.