Methods, apparatus and computer program for identifying canopy structures are provided. In one aspect, a method includes receiving a first calculated offset between a start point of a shadow cast by a building and a proximate first vertex of a building footprint of the building, and receiving a second calculated offset between an end point of the shadow cast by the building and a proximate second vertex of the building footprint, the shadow identified from an overhead image of the building, and wherein the building footprint comprises a shadowed side and a non-shadowed side, each running between the start point and the end point of the shadow on a different respective side of the building. The method also includes comparing the offsets to an offset threshold, and in response to both the first and second received offsets exceeding the offset threshold, determining that the building footprint represents a canopy.

According to examples, a system for providing personalized routes may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a user request for a personalized route wherein the user request can include at least a route origin and destination. The processor employs the user request to identify the user and retrieves the user's preferences from different recommender systems. The recommendations output by the recommender systems are ranked based on context data associated with the user request. The geographic locations associated with a predetermined number of top-ranked recommendations are obtained and a personalized route that passes through a maximum number of the geographic locations is built for a display to the user.

According to examples, a system for providing personalized routes may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a user request for a personalized route wherein the user request can include at least a route origin and destination. The processor employs the user request to identify the user and retrieves the user's preferences from different recommender systems. The recommendations output by the recommender systems are ranked based on context data associated with the user request. The geographic locations associated with a predetermined number of top-ranked recommendations are obtained and a personalized route that passes through a maximum number of the geographic locations is built for a display to the user.

An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

The present invention provides a system for identifying individual crowns of individual fruit trees using aerial images. A crown identification device 40 of the present invention includes an identification criterion determination unit 41 and a crown identification unit 42. The identification criterion determination unit 41 includes a first image acquisition section 411 that acquires a first aerial image including a plurality of individual fruit trees in a deciduous period in a fruit farm field, a skeleton extraction section 412 that processes the first aerial image to extract a whole crown skeleton including the plurality of individual fruit trees, a vertex extraction unit 413 that extracts vertexes of each crown skeleton corresponding to each individual fruit tree, and an identification criterion extraction section 414 that extracts a crown candidate region of a minimum polygonal shape including all the vertexes as an identification criterion for each individual fruit tree and extracts a centroid of the crown candidate region. The crown identification unit 42 includes a second image acquisition section 421 that acquires a second aerial image of the fruit tree farm field at the time of identifying a crown at the same scale as the first aerial image, a whole crown extraction section 422 that processes the second aerial image to extract a whole crown image including the plurality of individual fruit trees, and a crown identification section 423 that collates the crown candidate region and the centroid of the identification criterion with the whole crown image to identify a crown region of each individual fruit tree in the second aerial image.

The present invention provides a system for identifying individual crowns of individual fruit trees using aerial images. A crown identification device 40 of the present invention includes an identification criterion determination unit 41 and a crown identification unit 42. The identification criterion determination unit 41 includes a first image acquisition section 411 that acquires a first aerial image including a plurality of individual fruit trees in a deciduous period in a fruit farm field, a skeleton extraction section 412 that processes the first aerial image to extract a whole crown skeleton including the plurality of individual fruit trees, a vertex extraction unit 413 that extracts vertexes of each crown skeleton corresponding to each individual fruit tree, and an identification criterion extraction section 414 that extracts a crown candidate region of a minimum polygonal shape including all the vertexes as an identification criterion for each individual fruit tree and extracts a centroid of the crown candidate region. The crown identification unit 42 includes a second image acquisition section 421 that acquires a second aerial image of the fruit tree farm field at the time of identifying a crown at the same scale as the first aerial image, a whole crown extraction section 422 that processes the second aerial image to extract a whole crown image including the plurality of individual fruit trees, and a crown identification section 423 that collates the crown candidate region and the centroid of the identification criterion with the whole crown image to identify a crown region of each individual fruit tree in the second aerial image.

An article distinguishing system includes: an identification information acquisition unit, an image capture unit that captures an image of an exterior of a target article, a determination unit that derives a degree of matching between the captured image of the target article and exterior image data stored in a storage unit, and determines whether or not the current state is a normal state in which exterior image data for which the degree of matching is a determination threshold value or more is present, and a learning processing unit that executes learning processing if it has been determined by the determination unit that the current state is not the normal state. The learning processing is processing for storing the data of the captured image as new exterior image data in the storage unit in association with the identification information acquired by the identification information acquisition unit.

An article distinguishing system includes: an identification information acquisition unit, an image capture unit that captures an image of an exterior of a target article, a determination unit that derives a degree of matching between the captured image of the target article and exterior image data stored in a storage unit, and determines whether or not the current state is a normal state in which exterior image data for which the degree of matching is a determination threshold value or more is present, and a learning processing unit that executes learning processing if it has been determined by the determination unit that the current state is not the normal state. The learning processing is processing for storing the data of the captured image as new exterior image data in the storage unit in association with the identification information acquired by the identification information acquisition unit.

Disclosed are techniques for improving an advanced driver-assistance system (ADAS) by pre-processing image data. In one embodiment, a method is disclosed comprising receiving one or more image frames captured by an image sensor installed on a vehicle; identifying a position of a skyline in the one or more image frames, the position comprising a horizontal position of the skyline; cropping one or more future image frames based on the position of the skyline, the cropping generating cropped images comprising a subset of the corresponding future image frames; and processing the cropped images at an advanced driver-assistance system (ADAS).