Systems and methods are provided for selecting feature points within an image. A plurality of candidate feature points are identified in the image. A plurality of feature points are selected for each of the plurality of candidate feature points, a plurality of sets of representative pixels. For each set of representative pixels, a representative value is determined as one of a maximum chromaticity value and a minimum chromaticity value from the set of representative pixels. A score is determined for each candidate feature point from the representative values for the plurality of sets of representative pixels associated with the candidate feature point. The feature points are selected according to the determined scores for the plurality of candidate feature points.

Systems and methods are provided for selecting feature points within an image. A plurality of candidate feature points are identified in the image. A plurality of feature points are selected for each of the plurality of candidate feature points, a plurality of sets of representative pixels. For each set of representative pixels, a representative value is determined as one of a maximum chromaticity value and a minimum chromaticity value from the set of representative pixels. A score is determined for each candidate feature point from the representative values for the plurality of sets of representative pixels associated with the candidate feature point. The feature points are selected according to the determined scores for the plurality of candidate feature points.

A method of displaying the influence of an aspect of a model includes acquiring a two-dimensional echocardiogram having a variable intensity; relating the two-dimensional echocardiogram to a plurality of mapping points, the mapping points existing in a three-dimensional model space; determining a degree of influence value for a mapping point; and displaying the mapping point with a visual attribute that corresponds to the determined degree of influence value.

A data tuning software application platform relating to the ability to apply maskless lithography patterns to a substrate in a manufacturing process is disclosed in which the application processes graphical objects and configures the graphical objects for partition into a plurality of trapezoids. The trapezoids may be selectively merged in order to minimize the trapezoid count while limiting the loss of edge fidelity.

A method of video coding using block partitioning process including a binary tree partitioning process is disclosed. The block partitioning process is applied to a block of video data to partition the block into final sub-blocks. Coding process comprising prediction process, transform process or both for the block will be applied at the final sub-blocks level. The binary tree partitioning process can be applied to a given block recursively to generate binary tree leaf nodes until a termination condition is met. In another embodiment, the quadtree partitioning process is applied to a block first. The quadtree leaf nodes are further partitioned using the binary tree partitioning process. The quadtree partitioning process can be applied to a given block recursively to generate quadtree leaf nodes until a termination condition is met.

Systems and methods for reducing the bandwidth required to transmit video streams related to faces re described herein. In some aspects, contour information from face recognition technology is captured at a transmitting device and sent to a receiving device. The contour information may be used to reconstruct the face at the receiving device without the need to send an entire video frame of the face.

Systems and methods for reducing the bandwidth required to transmit video streams related to faces re described herein. In some aspects, contour information from face recognition technology is captured at a transmitting device and sent to a receiving device. The contour information may be used to reconstruct the face at the receiving device without the need to send an entire video frame of the face.

Disclosed herein is a method of processing a graph-based signal using a geometric primitive, comprising: specifying the geometric primitive to be used for calculating an edge weight; obtaining a parameter for each of the geometric primitive; calculating an edge weight for each of edges within the image based on the parameter; and encoding the image based on the edge weight.

Disclosed herein is a method of processing a graph-based signal using a geometric primitive, comprising: specifying the geometric primitive to be used for calculating an edge weight; obtaining a parameter for each of the geometric primitive; calculating an edge weight for each of edges within the image based on the parameter; and encoding the image based on the edge weight.

Embodiments relate to methods for efficiently encoding sensor data captured by an autonomous vehicle and building a high definition map using the encoded sensor data. The sensor data can be LiDAR data which is expressed as multiple image representations. Image representations that include important LiDAR data undergo a lossless compression while image representations that include LiDAR data that is more error-tolerant undergo a lossy compression. Therefore, the compressed sensor data can be transmitted to an online system for building a high definition map. When building a high definition map, entities, such as road signs and road lines, are constructed such that when encoded and compressed, the high definition map consumes less storage space. The positions of entities are expressed in relation to a reference centerline in the high definition map. Therefore, each position of an entity can be expressed in fewer numerical digits in comparison to conventional methods.