Image recognition device and method, and robot device
First Claim
1. An image recognition apparatus which compares an object image containing a plurality of objects with a model image containing a model to be detected and extracts the model from the object image, the apparatus comprising:
- feature point extracting means for extracting a feature point from each of the object image and the model image;
feature quantity retention means for extracting and retaining, as a feature quantity, a density gradient direction histogram at least acquired from density gradient information in a neighboring region at the feature point in each of the object image and the model image;
feature quantity comparison means for comparing each feature point of the object image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
model attitude estimation means for detecting the presence or absence of the model on the object image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the feature quantity comparison means itinerantly shifts one of the density gradient direction histograms of feature points to be compared in density gradient direction to find distances between the density gradient direction histograms and generates the candidate-associated feature point pair by assuming a shortest distance to be a distance between the density gradient direction histograms.
1 Assignment
0 Petitions
Accused Products
Abstract
In an image recognition apparatus (1), feature point extraction sections (10a) and (10b) extract feature points from a model image and an object image. Feature quantity retention sections (11a) and (11b) extract a feature quantity for each of the feature points and retain them along with positional information of the feature points. A feature quantity comparison section (12) compares the feature quantities with each other to calculate the similarity or the dissimilarity and generates a candidate-associated feature point pair having a high possibility of correspondence. A model attitude estimation section (13) repeats an operation of projecting an affine transformation parameter determined by three pairs randomly selected from the candidate-associated feature point pair group onto a parameter space. The model attitude estimation section (13) assumes each member in a cluster having the largest number of members formed in the parameter space to be an inlier. The model attitude estimation section (13) finds the affine transformation parameter according to the least squares estimation using the inlier and outputs a model attitude determined by this affine transformation parameter.
73 Citations
19 Claims
-
1. An image recognition apparatus which compares an object image containing a plurality of objects with a model image containing a model to be detected and extracts the model from the object image, the apparatus comprising:
-
feature point extracting means for extracting a feature point from each of the object image and the model image;
feature quantity retention means for extracting and retaining, as a feature quantity, a density gradient direction histogram at least acquired from density gradient information in a neighboring region at the feature point in each of the object image and the model image;
feature quantity comparison means for comparing each feature point of the object image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
model attitude estimation means for detecting the presence or absence of the model on the object image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the feature quantity comparison means itinerantly shifts one of the density gradient direction histograms of feature points to be compared in density gradient direction to find distances between the density gradient direction histograms and generates the candidate-associated feature point pair by assuming a shortest distance to be a distance between the density gradient direction histograms. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 15)
-
-
11. An image recognition apparatus which compares an object image containing a plurality of objects with a model image containing a model to be detected and extracts the model from the object image, the apparatus comprising:
-
feature point extracting means for extracting a feature point from each of the object image and the model image;
feature quantity retention means for extracting and retaining a feature quantity in a neighboring region at the feature point in each of the object image and the model image;
feature quantity comparison means for comparing each feature point of the object image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
model attitude estimation means for detecting the presence or absence of the model on the object image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the model attitude estimation means repeatedly projects an affine transformation parameter determined from three randomly selected candidate-associated feature point pairs onto a parameter space and finds an affine transformation parameter to determine a position and an attitude of the model based on an affine transformation parameter belonging to a cluster having the largest number of members out of clusters formed on a parameter space. - View Dependent Claims (12, 13, 14)
-
-
16. An image recognition method which compares an object image containing a plurality of objects with a model image containing a model to be detected and extracts the model from the object image, the method comprising:
-
a feature point extracting step of extracting a feature point from each of the object image and the model image;
a feature quantity retention step of extracting and retaining, as a feature quantity, a density gradient direction histogram at least acquired from density gradient information in a neighboring region at the feature point in each of the object image and the model image;
a feature quantity comparison step of comparing each feature point of the object image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
a model attitude estimation step of detecting the presence or absence of the model on the object image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the feature quantity comparison step itinerantly shifts one of the density gradient direction histograms of feature points to be compared in density gradient direction to find distances between the density gradient direction histograms and generates the candidate-associated feature point pair by assuming a shortest distance to be a distance between the density gradient direction histograms.
-
-
17. An image recognition method which compares an object image containing a plurality of objects with a model image containing a model to be detected and extracts the model from the object image, the method comprising:
-
a feature point extracting step of extracting a feature point from each of the object image and the model image;
a feature quantity retention step of extracting and retaining a feature quantity in a neighboring region at the feature point in each of the object image and the model image;
a feature quantity comparison step of comparing each feature point of the object image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
a model attitude estimation step of detecting the presence or absence of the model on the object image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the model attitude estimation step repeatedly projects an affine transformation parameter determined from three randomly selected candidate-associated feature point pairs onto a parameter space and finds an affine transformation parameter to determine a position and an attitude of the model based on an affine transformation parameter belonging to a cluster having the largest number of members out of clusters formed on a parameter space.
-
-
18. An autonomous robot apparatus capable of comparing an input image with a model image containing a model to be detected and extracting the model from the input image, the apparatus comprising:
-
image input means for imaging an outside environment to generate the input image;
feature point extracting means for extracting a feature point from each of the input image and the model image;
feature quantity retention means for extracting and retaining, as a feature quantity, a density gradient direction histogram at least acquired from density gradient information in a neighboring region at the feature point in each of the input image and the model image;
feature quantity comparison means for comparing each feature point of the input image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
model attitude estimation means for detecting the presence or absence of the model on the input image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the feature quantity comparison means itinerantly shifts one of the density gradient direction histograms of feature points to be compared in density gradient direction to find distances between the density gradient direction histograms and generates the candidate-associated feature point pair by assuming a shortest distance to be a distance between the density gradient direction histograms.
-
-
19. An autonomous robot apparatus capable of comparing an input image with a model image containing a model to be detected and extracting the model from the input image, the apparatus comprising:
-
image input means for imaging an outside environment to generate the input image;
feature point extracting means for extracting a feature point from each of the input image and the model image;
feature quantity retention means for extracting and retaining a feature quantity in a neighboring region at the feature point in each of the input image and the model image;
feature quantity comparison means for comparing each feature point of the input image with each feature point of the model image and generating a candidate-associated feature point pair having similar feature quantities; and
model attitude estimation means for detecting the presence or absence of the model on the input image using the candidate-associated feature point pair and estimating a position and an attitude of the model, if any, wherein the model attitude estimation means repeatedly projects an affine transformation parameter determined from three randomly selected candidate-associated feature point pairs onto a parameter space and finds an affine transformation parameter to determine a position and an attitude of the model based on an affine transformation parameter belonging to a cluster having the largest number of members out of clusters formed on a parameter space.
-
Specification