US 9,424,461 B1Granted

Object recognition for three-dimensional bodies

AssigneeAmazon Technologies, Inc.

InventorsChang Yuan, Geoffrey Scott Heller, Oleg Rybakov, Sharadh Ramaswamy, Jim Oommen Thomas

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Abstract

Various embodiments utilize two-dimensional (“2D”) and three-dimensional (“3D”) object features for purposes such as object recognition and/or image matching. For example, a user can capture an image (e.g., still images or video) of an object and can receive information about items that are determined to match the object. For example, the image can be analyzed to detect visual features (e.g., corners, edges, etc.) of the object and the detected visual features can be combined to generate a combined visual feature vector which can be used for object recognition, image matching, or other such purposes. Other approaches utilize the image to generate a 3D model of the object represented in the image, which can be used to determine at least one object or types of objects that match the object represented in the image.

Claims17

Dependent claims

A computer device, comprising:

at least one processor;
a camera;
and
memory including instructions that, when executed by the at least one processor, cause the computing device to:
receive an image acquired by the camera, the image including a representation of an object;
analyze the image to identify a plurality of feature points;
generate, based at least in part upon the plurality of feature points, a plurality of visual feature vectors representative of the object;
determine a distinctive score for each of the plurality of visual feature vectors, the distinctive score based at least in part on indispensable information that is useful in identifying the object, wherein the indispensable information represents frequently occurring features;
select a subset of the plurality of visual feature vectors having a distinctive score equal or above a distinctive score threshold;
generate a compact combined visual feature vector using the selected subset of the plurality of visual feature vectors;
compare the compact combined visual feature vector to one or more stored vectors of a set of stored vectors, each of the set of stored vectors corresponding to a respective type of object;
determine a matching stored vector having a respective similarity score that at least meets a matching threshold;
and
identify, based at least in part on the matching stored vector, at least one respective type of object represented in the image.

11 more elements

A computer implemented method, comprising:

receiving an image acquired by a camera of a computing device, the image including a representation of an object;
determining a plurality of visual feature vectors representative of the object;
generating a combined visual feature vector using at least a first subset of the plurality of visual feature vectors;
determining a distinctive score for each visual feature vector of the first subset of the plurality of visual feature vectors, the distinctive score based at least in part on indispensable information that is useful in identifying the object, wherein the indispensable information represents frequently occurring features;
and
selecting a second subset of the plurality of visual feature vectors having the distinctive score above or equal to a distinctive score threshold;
and
generating a compact combined visual feature vector using the second subset of the plurality of visual feature vectors, wherein the compact combined visual feature vector is configured to identify at least one object type associated with the representation of the object.

5 more elements

A computing device, comprising:

a processor;
a camera;
and
memory including instructions that, when executed by the at least one processor, cause the computing device to:
receive an image captured by the camera, the image including a representation of an object;
determine a plurality of visual feature vectors representative of the object;
generate a combined visual feature vector using at least a first subset of the plurality of visual feature vectors;
determine a distinctive score associated with the first subset of the plurality of visual feature vectors, the distinctive score based at least in part on indispensable information that is useful in identifying the object, wherein the indispensable information represents frequently occurring features;
select a second subset of the plurality of visual feature vectors having the distinctive score above or equal to a distinctive score threshold;
and
generate a compact combined visual feature vector using the second subset of the plurality of visual feature vectors, wherein the compact combined visual feature vector is configured to identify at least one object type associated with the representation of the object.

8 more elements

Dependencies are inferred from the claim text.