Generating a correction algorithm includes obtaining a model of an eye, the model including a front portion with optics to mimic a cornea and a lens of a human eye, and a rear portion having a generally hemispherical-shaped body to mimic a retina of the human eye. The rear portion includes physical reference lines on an inside surface of the generally hemispherical-shaped body. Images of the model are captured using an image capturing device aimed at the model. Vertices of the physical reference lines are identified according to a given projection technique for displaying generally hemispherical-shaped body in a two-dimensional image in the captured images. Idealized placement of the vertices of the physical reference lines is obtained according to the given projection technique. The result is a correction algorithm used to adjust any pixel of an image of an actual eye in the x-axis and in the y-axis.

A road shape estimation device includes processing circuitry to detect, from received signals of radio waves reflected by an object present around a vehicle, reflection points each indicating a reflection position of each radio wave on the object, to perform classification of reflection points of an object present in a left side area of the vehicle into a first group, and of reflection points of an object present in a right side area of the vehicle into a second group, to perform translation of each reflection point classified into the first group to a right direction of the vehicle, and perform translation of each reflection point classified into the second group to a left direction of the vehicle, and to calculate an approximate curve representing a point cloud including all reflection points after the translation and perform estimation of a shape of the road from the approximate curve.

A road shape estimation device includes processing circuitry to detect, from received signals of radio waves reflected by an object present around a vehicle, reflection points each indicating a reflection position of each radio wave on the object, to perform classification of reflection points of an object present in a left side area of the vehicle into a first group, and of reflection points of an object present in a right side area of the vehicle into a second group, to perform translation of each reflection point classified into the first group to a right direction of the vehicle, and perform translation of each reflection point classified into the second group to a left direction of the vehicle, and to calculate an approximate curve representing a point cloud including all reflection points after the translation and perform estimation of a shape of the road from the approximate curve.

A method of determining a geometrical measurement of a retina of an eye, comprising obtaining a two dimensional representation of at least a portion of the retina of the eye (34), deriving a geometrical remapping which converts the two dimensional representation of the retinal portion to a three dimensional representation of the retinal portion (36), using one or more coordinates of the two dimensional representation of the retinal portion to define the geometrical measurement to be taken of the retina on the two dimensional representation (38), using the geometrical remapping to convert the or each coordinate of the two dimensional representation of the retinal portion to an equivalent coordinate of the three dimensional representation of the retinal portion (40), and using the or each equivalent coordinate of the three dimensional representation of the retinal portion to determine the geometrical measurement of the retina of the eye (42).

A method of determining a geometrical measurement of a retina of an eye, comprising obtaining a two dimensional representation of at least a portion of the retina of the eye (34), deriving a geometrical remapping which converts the two dimensional representation of the retinal portion to a three dimensional representation of the retinal portion (36), using one or more coordinates of the two dimensional representation of the retinal portion to define the geometrical measurement to be taken of the retina on the two dimensional representation (38), using the geometrical remapping to convert the or each coordinate of the two dimensional representation of the retinal portion to an equivalent coordinate of the three dimensional representation of the retinal portion (40), and using the or each equivalent coordinate of the three dimensional representation of the retinal portion to determine the geometrical measurement of the retina of the eye (42).

Some methods of analyzing one or more sections of a central nervous system of a patient comprise, for each of the section(s), from data the includes one or more 3D representations of the section, segmenting each of the 3D representation(s) into ventral and dorsal portions and, for at least one of the 3D representation(s), determining a mean curvature of at least a region of a surface of the dorsal portion and/or a mean curvature of at least a region of a surface of the ventral portion. Each of the section(s) can include at least a portion of a brainstem of the patient.

Some methods of analyzing one or more sections of a central nervous system of a patient comprise, for each of the section(s), from data the includes one or more 3D representations of the section, segmenting each of the 3D representation(s) into ventral and dorsal portions and, for at least one of the 3D representation(s), determining a mean curvature of at least a region of a surface of the dorsal portion and/or a mean curvature of at least a region of a surface of the ventral portion. Each of the section(s) can include at least a portion of a brainstem of the patient.

A system and method for estimating dimensions of an approximately cuboidal object from a 3D image of the object acquired by an image sensor of the vision system processor is provided. An identification module, associated with the vision system processor, automatically identifies a 3D region in the 3D image that contains the cuboidal object. A selection module, associated with the vision system processor, automatically selects 3D image data from the 3D image that corresponds to approximate faces or boundaries of the cuboidal object. An analysis module statistically analyzes, and generates statistics for, the selected 3D image data that correspond to approximate cuboidal object faces or boundaries. A refinement module chooses statistics that correspond to improved cuboidal dimensions from among cuboidal object length, width and height. The improved cuboidal dimensions are provided as dimensions for the object. A user interface displays a plurality of interface screens for setup and runtime operation.

A system and method for estimating dimensions of an approximately cuboidal object from a 3D image of the object acquired by an image sensor of the vision system processor is provided. An identification module, associated with the vision system processor, automatically identifies a 3D region in the 3D image that contains the cuboidal object. A selection module, associated with the vision system processor, automatically selects 3D image data from the 3D image that corresponds to approximate faces or boundaries of the cuboidal object. An analysis module statistically analyzes, and generates statistics for, the selected 3D image data that correspond to approximate cuboidal object faces or boundaries. A refinement module chooses statistics that correspond to improved cuboidal dimensions from among cuboidal object length, width and height. The improved cuboidal dimensions are provided as dimensions for the object. A user interface displays a plurality of interface screens for setup and runtime operation.

The present disclosure discloses user equipment and an image processing method and apparatus, which relate to the field of information technologies and can improve accuracy in determining image depth information. The method includes: first obtaining an original image, then determining, according to the original image and at least two preset edge image blocks, a target blur value corresponding to a pixel in the original image, and finally, determining a depth value corresponding to the pixel in the original image according to the target blur value corresponding to the pixel in the original image. The present invention is applicable to determining of a blur value corresponding to a pixel in an image and determining of a depth value corresponding to the pixel in the image according to the blur value corresponding to the pixel in the image.