1. A perimeter for examining a visual field of an eye of a patient, comprising:
a presentation unit, which presents an examination target at each of various positions around an eye fixation point to which the patient’s eye is visually fixed;
a position determination unit, which determines at least one of positions of an optic papilla, a central fovea and a macula lutea on a fundus of the patient’s eye; and
a control unit, which determines a running state of a nerve fiber bundle on the fundus based on the determined position, and which controls the presentation unit to present and move the examination target based on the determined running state of the nerve fiber bundle.
2. A perimeter according to claim 1, wherein the control unit controls the presentation unit to move the presented examination target along the determined running state.
3. A perimeter according to claim 1, wherein the control unit controls the presentation unit to move the presented examination target in a direction perpendicular to the determined running state.
4. A perimeter according to claim 1, further comprising:
an observation unit, which includes an image pickup device and a display unit, and which displays, on the display unit, a fundus observation image of the patient’s eye obtained by the image pickup device,
wherein the position determination unit determines the positions of the optic papilla and the central fovea or the macula lutea, on the displayed fundus observation image, and
wherein the control unit determines the running state of the nerve fiber bundle on the fundus observation image based on the determined positions.
5. A perimeter according to claim 4, wherein the control unit includes:
a storage unit, which stores fundus image model data that includes a typical optic papilla and a typical central fovea or a typical macula lutea, and that indicates a typical running state of a nerve fiber bundle,
wherein the control unit analyzes a correlation between the fundus observation image and the fundus image model data based on the determined positions to determine the running state of the nerve fiber bundle on the fundus observation image.
6. A perimeter according to claim 4, wherein the control unit displays the determined running state of the nerve fiber bundle on the display unit, while superimposing the determined running state onto the fundus observation image.
7. A perimeter according to claim 1, further comprising:
a photographing unit which includes an image pickup device, and which photographs the fundus of the patient’s eye.
8. A perimeter according to claim 7, wherein the eye fixation point for perimetry is used commonly as an eye fixation point for photographing the fundus.
9. A perirmeter for examining a visual field of an eye of a patient, comprising:
a presentation unit, which presents an examination target at each of various position around an eye fixation point to which the patients eye is visually fixed;
an observation unit, which has an image pickup device and a display unit, and which displays, on the display unit, a fundus observation image of the patients eye obtained by the image pickup device;
a position determination unit, which determines positions of an optic papilla and a central fovea or a macula lutea on the displayed fundus observation image; and
a control unit, which determines a running state of a nerve fiber bundle on the fundus observation image based on the determined positions, and which displays, on the display unit, the determined running state of the nerve fiber bundle, while superimposing the determined running state onto the fundus observation image.
10. A perimeter according to claim 9, wherein the control unit includes:
a storage unit, which stores fundus image model data that includes a typical optic papilla and a typical central fovea or a typical macula lutea, and that indicates a typical running state of a nerve fiber bundle,
wherein the control unit analyzes a correlation between the fundus observation image and the fundus image model data based on the determined positions to determine the running state of the nerve fiber bundle on the fundus observation image.
11. A perimeter according to claim 9, further comprising: a photographing unit which has an image pickup device, and which photographs the fundus of the patient’s eye.
12. A perimeter according to claim 11, wherein the eye fixation target for perimetry is used commonly as an eye fixation target for photographing the fundus.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for operating an object detection system to determine a location of an object, comprising the steps of:
(a) generating a vision track from data provided by a camera, said vision track including data indicative of a locations of the object, wherein a pixel coordinate is generated for each object that is detected from an image;
(b) generating a radar track from data provided by a radar sensor, said radar track including data indicative of the locations of the object, wherein a range and azimuth angle coordinate is generated for each detected object;
(c) generating a matched pair indicative of the location of the object based upon the data from said vision and radar tracks;
(d) using the matched pair, estimating a camera pan angle (\u03c8) and an instantaneous tilt angle (\u03c4) and road geometry, wherein said camera pan angle is defined as the horizontal angle between a camera boresight and a radar boresight, wherein said instantaneous tilt angle is a vertical misalignment between the camera boresight and the longitudinal axis of a planar ground patch under a vehicle, wherein said estimated camera pan angle, instantaneous tilt angle (\u03c4), and road geometry minimize the alignment error between said matched pair, wherein said estimated camera pan angle is used to correct or reprocess each radar track so that a set of pan angle corrected matched pairs, each having a correct pan angle, is generated, and wherein said estimated instantaneous tilt angle (\u03c4) and road geometry comprise linearizing at least one trigonometric equation about a nominal tilt angle (\u03c40) so that said trigonometric equation becomes a linear equation; and
(e) determining the location of the object, said determining including identifying, locating or interpreting the object using the estimates of the pan angle (\u03c8) and the instantaneous tilt angle (\u03c4) and the road geometry, wherein the estimates are used to correct for alignment differences between the camera and the radar sensor while traveling a roadway, and thereby correct for position of the object errors in the vision track and the radar track.
2. The method of claim 1, wherein a vertical curvature (c) of an upcoming road segment represents said road geometry.
3. The method of claim 1, wherein said vision track is provided in terms of a two-dimensional Cartesian coordinate system (pixels (p, q)), and said radar track is provided in terms of a two-dimensional polar coordinate system (range and azimuth angle (R, \u03b8)).
4. The method of claim 1, wherein said pan angle (\u03c8) estimate is filtered with a low-pass filter to provide a filtered pan angle estimate.
5. The method of claim 1, wherein step (d) further includes solving an optimization problem that minimizes the 3-D world coordinate alignment error between said matched pair.
6. The method of claim 5, wherein step (d) further includes utilizing a least squares problem.
7. The method of claim 1, wherein step (d) further includes removing any negligible terms and manipulating said linear equation so that a single linear equation is provided for said matched pair.
8. The method of claim 1, wherein step (d) further includes using said estimated camera pan angle (\u03c8) and said radar track to provide a pan corrected matched pair having a radar track that has been generally corrected for errors attributable to a camera pan angle.
9. The method of claim 8, wherein step (d) further includes using said pan corrected matched pair to estimate an instantaneous tilt angle (\u03c4) and road geometry.
10. The method of claim 1, wherein the vehicle object detection system is a forward looking, radar-cued vision system.
11. A method for operating an object detection system to determine a location of an object, comprising the steps of:
(a) generating a vision track from data provided by a camera, said vision track including data indicative of a locations of the object, wherein a pixel coordinate is generated for each object that is detected from an image;
(b) generating a radar track from data provided by a radar sensor, said radar track including data indicative of the locations of the object, wherein a range and azimuth angle coordinate is generated for each detected object;
(c) generating a matched pair indicative of the location of the object based upon the data from said vision and radar tracks;
(d) estimating a camera pan angle (\u03c8) that best fits said matched pair, wherein said camera pan angle is defined as the horizontal angle between a camera boresight and a radar boresight, wherein said instantaneous tilt angle is a vertical misalignment between the camera boresight and the longitudinal axis of a planar ground patch under a vehicle, wherein said estimated camera pan angle, instantaneous tilt angle (\u03c4), and road geometry minimize the alignment error between said matched pair, wherein said estimated camera pan angle is used to correct or reprocess each radar track so that a set of pan angle corrected matched pairs, each having a correct pan angle, is generated;
(e) using said estimated camera pan angle (\u03c8) and said radar track to provide a pan corrected matched pair(s) having a radar track that has been generally corrected for errors attributable to a camera pan angle, wherein said estimates comprise linearizing at least one trigonometric equation about a nominal tilt angle (\u03c40) so that said trigonometric equation becomes a linear equation;
(f) using said pan corrected matched pair(s) to estimate an instantaneous tilt angle (\u03c4) and road geometry; and
(g) determining the location of the object, said determining including identifying, locating or interpreting the object using the estimates of the pan angle (\u03c8) and the instantaneous tilt angle (\u03c4) and the road geometry, wherein the estimates are used to correct for alignment differences between the camera and the radar sensor while traveling a roadway, and thereby correct for position of the object errors in the vision track and the radar track.
12. A method for operating an object detection system to determine a location of one or more of a plurality of objects, comprising the steps of:
(a) generating a plurality of vision tracks from data provided by a camera, said vision tracks including data indicative of locations of the plurality of objects, wherein a pixel coordinate is generated for each object that is detected from an image;
(b) generating a plurality of radar tracks from data provided by a radar sensor, said radar tracks including data indicative of the locations of the plurality of objects, wherein a range and azimuth angle coordinate is generated for each detected object;
(c) generating a plurality of matched pairs indicative of the locations based upon the data from said vision and radar tracks;
(d) estimating a camera pan angle (\u03c8) that best fits said plurality of matched pairs, wherein said camera pan angle is defined as the horizontal angle between a camera boresight and a radar boresight, wherein said instantaneous tilt angle is a vertical misalignment between the camera boresight and the longitudinal axis of a planar ground patch under a vehicle, wherein said estimated camera pan angle, instantaneous tilt angle (\u03c4), and road geometry minimize the alignment error between said matched pair, wherein said estimated camera pan angle is used to correct or reprocess each radar track so that a set of pan angle corrected matched pairs, each having a correct pan angle, is generated, and wherein said estimated instantaneous tilt angle (\u03c4) and road geometry comprise linearizing at least one trigonometric equation about a nominal tilt angle (\u03c40) so that said trigonometric equation becomes a linear equation;
(e) using said estimated camera pan angle (\u03c8) and said plurality of radar tracks to provide a plurality of pan corrected matched pairs each having a radar track that has been generally corrected for errors attributable to a camera pan angle;
(f) using said plurality of pan corrected matched pairs to generate a least squares problem, said least squares problem treats said estimated camera pan angle (\u03c8) as zero;
(g) linearizing one or more trigonometric equations about a nominal tilt angle (\u03c40) so that said trigonometric equations become linear equations;
(h) solving said least squares problem to estimate both said instantaneous tilt angle (\u03c4) and said vertical curvature (c); and
(i) determining the location of one or more of the objects, said determining including identifying, locating or interpreting objects using the estimates of the pan angle (\u03c8) and the instantaneous tilt angle (\u03c4) and the road geometry, wherein the estimates are used to correct for alignment differences between the camera and the radar sensor while traveling a roadway, and thereby correct for position of the object errors in the vision track and the radar track.