A method for measuring humidity at long range using simplified equipment includes creating a formula according to a relationship between multiple sets of known optical flow feature vectors and a known humidity. First and second images are obtained, wherein the first image and the second image are captured as being in the same range of capture. A plurality of feature points in the first image is obtained and an optical flow feature vector for each of the feature points according to apparent changes in position of each feature point according to the second image are calculated. The degree of current humidity according to the optical flow feature vectors and the formula is thus obtained.

A method of video processing includes determining a first motion displacement Vx(x,y) at a position (x,y) and a second motion displacement Vy(x,y) at the position (x,y) in a video block coded using an optical flow based method, wherein x and y are fractional numbers, where Vx(x,y) and Vy(x,y) are determined based at least on the position (x,y) and a center position of a basic video block of the video block, and performing a conversion between the video block and a bitstream representation of the current video block using the first motion displacement and the second motion displacement.

An electronic circuit includes a block determinator, a candidate selector, and a motion vector generator to perform motion estimation between images. The block determinator determines a current block corresponding to a current location on an image and candidate blocks corresponding to relative locations with respect to the current location for each recursion for blocks constituting the image. The candidate selector selects some of the candidate blocks. The motion vector generator generates a motion vector for the current block based on one reference patch which is determined from reference patches indicated by candidate motion vectors of the selected candidate blocks. At least one of the relative locations corresponding to the candidate blocks selected in a first recursion is different from each of the relative locations corresponding to the candidate blocks selected in a second recursion following the first recursion.

Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

A method of video processing includes determining a refined prediction sample P′(x,y) at a position (x,y) in a video block by modifying a prediction sample P(x,y) at the position (x,y) with a first gradient component Gx(x, y) in a first direction estimated at the position (x,y) and a second gradient component Gy(x, y) in a second direction estimated at the position (x,y) and a first motion displacement Vx(x,y) estimated for the position (x,y) and a second motion displacement Vy(x,y) estimated for the position (x,y), where x and y are integer numbers, and performing a conversion between the video block and a bitstream representation of the video block using a reconstructed sample value Rec(x,y) at the position (x,y) that is obtained based on the refined prediction sample P′(x,y) and a residue sample value Res(x,y).

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Methods, systems, apparatus and articles of manufacture to identify features within an image are disclosed herein. An example apparatus includes a horizontal cost (HCOST) engine to apply a first row of pixels of a macroblock to an input of a first HCOST unit, the first HCOST unit including a number of difference calculators; and a difference calculator engine to apply corresponding rows of pixels of a search window of a source image to corresponding ones of the number of difference calculators of the first HCOST unit, the corresponding ones of the number of difference calculators to calculate respective sums of absolute difference (SAD) values between (a) the first row of pixels of the macroblock and (b) the corresponding rows of pixels of the search window.

A system and method are arranged to provide recommendations to a user of a vehicle. In one aspect, the vehicle navigates in an autonomous mode and the sensors provide information that is based on the location of the vehicle and output from sensors directed to the environment surrounding the vehicle. In further aspects, both current and previous sensor data is used to make the recommendations, as well as data based on the sensors of other vehicles.