1. A moving obstacle detecting device installed in or on a moving body for detecting moving obstacles in the vicinity of the moving body, the moving obstacle detecting device comprising:
an image capturing unit configured and arranged to capture an image of the surroundings of the moving body;
an optical flow calculating unit configured to find an optical flow corresponding to characteristic points in the image captured with the camera;
a focus of expansion calculating unit configured to calculate focus or foci of expansion of the motion vectors in the optical flow found by the optical flow calculating unit;
a histogram creating unit configured to create a histogram of the distribution of the focus or foci of expansion calculated by the focus of expansion calculating unit;
a peak test unit configured to detect at least one moving obstacle to test peaks in the histogram, said test determining whether or not the peaks correspond to the same object if two or more peaks exist in the histogram created by the histogram creating unit.
2. The moving obstacle detecting device as claimed in claim 1, wherein the histogram creating unit is configured to create the histogram of the distribution of the foci of expansion based on a predetermined axis.
3. The moving obstacle detecting device as claimed in claim 2, wherein the direction of the axis along which the histogram is created is changed in accordance with the location on the moving body where the image capturing unit is installed.
4. The moving obstacle detecting device as claimed in claim 1, wherein the histogram creating unit is configured to create the histogram of the distribution of the foci of expansion along an axis oriented in the transverse direction of the moving body based on the foci of expansion calculated by the focus of expansion calculating unit.
5. The moving obstacle detecting device as claimed in claim 1, wherein the moving body is a vehicle.
6. The moving obstacle detecting device as claimed in claim 5, wherein the optical flow calculating unit is configured to vary the time interval according to which it calculates the motion vectors based on one or more of the following pieces of information: the angle to which the steering wheel of the vehicle is rotated, the yaw rate detected by a yaw rate sensor, the difference between the rotational speeds of the left and right wheels, the status of the turn signal (directional), and information acquired from a car navigation system comprising a road map and a GPS.
7. The moving obstacle detecting device as claimed in claim 5, wherein the focus of expansion calculating unit is configured to vary the time interval according to which it calculates the foci of expansion based on one or more of the following pieces of information: the angle to which the steering wheel of the vehicle is rotated, the yaw rate detected by a yaw rate sensor, the difference between the rotational speeds of the left and right wheels, the status of the turn signal (directional), and information acquired from a car navigation system comprising a road map and a GPS.
8. The moving obstacle detecting device as claimed in claim 5, wherein the peak examining unit is configured to:
use a prescribed risk level to determine if the peaks correspond to the same object when two or more peaks are found to exist in the histogram of the foci of expansion created by the histogram creating unit; and
vary the risk level based on one or more of the following pieces of information: the width of the road on which the vehicle is traveling, the existence or absence of a sidewalk the number of lanes, the time of day during which the vehicle is traveling, the weather at the time when the vehicle is traveling, and information acquired from a car navigation system comprising a road map and a GPS.
9. The moving obstacle detecting device as claimed in claim 8, wherein
the peak examining unit is configured to detect a moving obstacle(s) by determining if the highest peak among the plurality of peaks that exist is of the same distribution as each of the other peak(s).
10. The moving obstacle detecting device as claimed in claim 9, wherein
the peak examining unit is configured to separate the motion vectors of the optical flow into a group of motion vectors whose foci of expansion are included in the highest peak and a group of motion vectors whose foci of expansion are included in other peak(s), generate distributions of the foci of expansion in each of the groups of motion vectors, and calculate the probability that the distributions of the foci of expansion in the two groups of motion vectors occurred within the same population.
11. The moving obstacle detecting device as claimed in claim 10, wherein
the peak examining unit is configured such that when it calculates the probability that the distributions of the foci of expansion in two groups of motion vectors occurred within the same population, it uses the t-test to test the difference between the two groups and determines that the distributions are different when the t-value is larger than a prescribed value.
12. The moving obstacle detecting device as claimed in claim 11, wherein
said prescribed value is set to a smaller value in situations where the number of moving obstacles is large and to a larger value in situations where the number of moving obstacles is small.
13. The moving obstacle detecting device as claimed in claim 12, wherein
the prescribed value is varied based on one or more of the following pieces of information: the width of the road on which the vehicle is traveling, the existence or absence of a sidewalk the number of lanes, the time of day during which the vehicle is traveling, the weather at the time when the vehicle is traveling, and information acquired from a car navigation system comprising a road map and a GPS.
14. A moving obstacle detecting method for detecting moving obstacles existing in the vicinity of a moving body, comprising:
(1) capturing an image of the surroundings of the moving body;
(2) finding an optical flow corresponding to characteristic points in the captured image;
(3) calculating focus or foci of expansion of the motion vectors in the optical flow;
(4) creating a histogram of the distribution of the focus or foci of expansion calculated; and
(5) detecting at least one moving obstacle to test peaks in the created histogram to determine whether or not the peaks correspond to the same object determine if two or more peaks exist in the histogram of the foci of expansion created.
15. The moving obstacle detecting method as claimed in claim 14, wherein the step (4) creates the histogram of the distribution of the foci of expansion based on a predetermined axis.
16. The moving obstacle detecting method as claimed in claim 14, wherein the step (4) creates the histogram of the distribution of the foci of expansion along an axis oriented in the transverse direction of the moving body based on the foci of expansion calculated.
17. The moving obstacle detecting method as claimed in claim 14, wherein the step (2) varies the time interval according to which it calculates the motion vectors based on one or more of the following pieces of information: the angle to which the steering wheel of the moving body is rotated, the yaw rate detected by a yaw rate sensor, the difference between the rotational speeds of the left and right wheels, the status of the turn signal (directional), and information acquired from a car navigation system comprising a road map and a GPS.
18. The moving obstacle detecting method as claimed in claim 14, wherein the step (3) varies the time interval according to which it calculates the foci of expansion based on one or more of the following pieces of information: the angle to which the steering wheel of the moving body is rotated, the yaw rate detected by a yaw rate sensor, the difference between the rotational speeds of the left and right wheels, the status of the turn signal (directional), and information acquired from a car navigation system comprising a road map and a GPS.
19. The moving obstacle detecting method as claimed in claim 14, wherein the step (5) uses a prescribed risk level to determine if the peaks correspond to the same object when two or more peaks are found to exist in the histogram of the foci of expansion created; and
varies the risk level based on one or more of the following pieces of information: the width of the road on which the vehicle is traveling, the existence or absence of a sidewalk the number of lanes, the time of day during which the vehicle is traveling, the weather at the time when the vehicle is traveling, and information acquired from a car navigation system comprising a road map and a GPS.
20. The moving obstacle detecting method as claimed in claim 19, wherein step (5) detects a moving obstacle(s) by determining if the highest peak among the plurality of peaks that exist is of the same distribution as each of the other peak(s).
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
I claim:
1. A method of generating a plurality of simulation results adapted for display on a display device, comprising the steps of:
(a) generating, responsive to a plurality of rock classifications, a plurality of model realizations representing an architecture of a fluvial reservoir; and
(b) generating, responsive to said plurality of model realizations representing said architecture of said fluvial reservoir, a plurality of grids, said plurality of grids representing said simulation results adapted for display on said display device.
2. The method of claim 1, wherein the generating step (a) comprises the step of:
defining, responsive to said plurality of rock classifications, channel belt margins.
3. The method of claim 1, wherein the generating step (a) comprises the step of:
generating a plurality of channel realizations.
4. The method of claim 1, wherein the generating step (a) comprises the step of:
rejecting invalid ones of a plurality of channel realizations thereby generating a plurality of valid channel realizations.
5. The method of claim 1, wherein the generating step (b) comprises the step of:
gridding tops and bases of a plurality of valid channel realizations.
6. The method of claim 1, wherein the generating step (b) comprises the step of:
generating 3-D grids corresponding to gridded tops and bases of a plurality of valid channel realizations, said 3-D grids representing said plurality of model realizations representing said architecture of said fluvial reservoir.
7. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine, to perform method steps for generating a plurality of simulation results adapted for display on a display device, said method steps comprising:
(a) generating, responsive to a plurality of rock classifications, a plurality of model realizations representing an architecture of a fluvial reservoir; and
(b) generating, responsive to said plurality of model realizations representing said architecture of said fluvial reservoir, said plurality of grids, said plurality of grids representing said simulation results adapted for display on said display device.
8. The program storage device of claim 7, wherein the generating step (a) comprises the step of:
defining, responsive to said plurality of rock classifications, channel belt margins.
9. The program storage device of claim 7, wherein the generating step (a) comprises the step of:
generating a plurality of channel realizations.
10. The program storage device of claim 7, wherein the generating step (a) comprises the step of:
rejecting invalid ones of a plurality of channel realizations thereby generating a plurality of valid channel realizations.
11. The program storage device of claim 7, wherein the generating step (b) comprises the step of:
gridding tops and bases of a plurality of valid channel realizations.
12. The program storage device of claim 7, wherein the generating step (b) comprises the step of:
generating 3-D grids corresponding to gridded tops and bases of a plurality of valid channel realizations, said 3-D grids representing said plurality of model realizations representing said architecture of said fluvial reservoir.
13. A system adapted for generating a plurality of simulation results adapted for display on a display device, comprising:
apparatus adapted for generating, responsive to a plurality of rock classifications, a plurality of model realizations representing an architecture of a fluvial reservoir; and
apparatus adapted for generating, responsive to said plurality of model realizations representing said architecture of said fluvial reservoir, a plurality of grids, said plurality of grids representing said simulation results adapted for display on said display device.
14. The system of claim 13, wherein said apparatus adapted for generating said plurality of model realizations representing the architecture of a fluvial reservoir comprises:
apparatus adapted for defining, responsive to said plurality of rock classifications, channel belt margins.
15. The system of claim 13, wherein said apparatus adapted for generating said plurality of model realizations representing the architecture of a fluvial reservoir comprises:
apparatus adapted for generating a plurality of channel realizations.
16. The system of claim 13, wherein said apparatus adapted for generating said plurality of model realizations representing the architecture of a fluvial reservoir comprises:
apparatus adapted for rejecting invalid ones of a plurality of channel realizations thereby generating a plurality of valid channel realizations.
17. The system of claim 13, wherein said apparatus adapted for generating said plurality of grids, which represents the architecture of a fluvial reservoir, comprises:
apparatus adapted for gridding tops and bases of a plurality of valid channel realizations.
18. The system of claim 13, wherein said apparatus adapted for generating said plurality of grids, which represents the architecture of a fluvial reservoir, comprises:
apparatus adapted for generating 3-D grids corresponding to gridded tops and bases of a plurality of valid channel realizations, said 3-D grids representing said plurality of model realizations which further represent said architecture of said fluvial reservoir.
19. A method of generating, responsive to a set of predefined channel belt margins, a plurality of model realizations representing an architecture of a fluvial reservoir, said channel belt margins being predefined in response to a set of rock classifications, comprising the steps of:
generating a plurality of channel realizations in response to the predefined channel belt margins;
rejecting invalid ones of said plurality of channel realizations thereby generating a plurality of valid channel realizations; and
gridding tops and bases of said plurality of valid channel realizations,
said plurality of model realizations being generated in response to the gridding of the tops and bases of said plurality of valid channel realizations.
20. The method of claim 19, wherein the step of generating a plurality of channel realizations further comprises the steps of:
setting input parameters; and
generating said plurality of channel realizations in response to the predefined channel belt margins.
21. The method of claim 19, wherein the step of gridding the tops and bases of said plurality of valid channel realizations comprises the steps of:
gridding the tops and bases of said plurality of valid channel realizations; and
simulating scour by surface truncation,
said plurality of model realizations being generated in response to the simulating step.
22. A program storage device readable by a machine tangibly embodying a program of instructions executable by the machine to perform method steps for generating, responsive to a set of predefined channel belt margins, a plurality of model realizations representing an architecture of a fluvial reservoir, said channel belt margins being predefined in response to a set of rock classifications, said method steps comprising:
generating a plurality of channel realizations in response to the predefined channel belt margins;
rejecting invalid ones of said plurality of channel realizations thereby generating a plurality of valid channel realizations; and
gridding tops and bases of said plurality of valid channel realizations.
23. The program storage device of claim 22, wherein the step of generating a plurality of channel realizations further comprises the steps of:
setting input parameters; and
generating said plurality of channel realizations in response to the predefined channel belt margins.
24. The program storage device of claim 22, wherein the step of gridding the tops and bases of said plurality of valid channel realizations comprises the steps of:
gridding the tops and bases of said plurality of valid channel realizations; and
simulating scour by surface truncation.