# US 11,907,340 B2 · Image positioning device and method

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- Number: US 11,907,340 B2
- Document ID: US11907340B2
- Status: Granted
- Kind: B2
- Application number: 17/389,623
- Filed: 2021-07-30
- Published: 2022-11-17
- Granted: 2024-02-20
- Earliest claimed: 2021-05-12
- Assignees: Quanta Computer Inc.
- Applicants: Quanta Computer Inc.
- Inventors: Kai-Ju Cheng; Yu-Hsun Chen; Yu-Cheng Chien; Chin-Yuan Ting; Shao-Ang Chen
- Web page: https://patentgrep.com/patent/US11907340B2

## CPC

- G06F 18/2431: Multiple classes
- G06V 20/50: Context or environment of the image
- G06F 18/23213: with fixed number of clusters, e.g. K-means clustering
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06T 7/73: using feature-based methods
- G06V 10/40: Extraction of image or video features
- G06V 10/762: using clustering, e.g. of similar faces in social networks
- G06V 20/49: {Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes}
- G06T 2207/10016: Video; Image sequence
- G06T 2207/20084: Artificial neural networks [ANN]
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

An image positioning device is provided in the invention. The image positioning device includes a calculation circuit and a positioning circuit. The calculation circuit obtains a plurality of images from an image capturing device and classifies the images into a plurality of main groups based on a first algorithm, wherein each main group corresponds to a different area and the calculation circuit classifies the images of each main group into a plurality of sub-groups based on a feature of each image of each main group and a clustering algorithm. The positioning circuit is coupled to the calculation circuit. The positioning circuit positions each sub-group in the area corresponding to each main group based on the relative position relationship between each sub-group of each main group.

## Claims

### Claim 1

An image positioning device, comprising:
a calculation circuit, wherein the calculation circuit obtains a plurality of images from an image capturing device and classifies the images into a plurality of main groups based on a first algorithm, wherein each main group corresponds to a different area and the calculation circuit classifies the images of each main group into a plurality of sub-groups based on a feature of each image of each main group and a clustering algorithm;
a positioning circuit, coupled to the calculation circuit, wherein the positioning circuit positions each sub-group in the area corresponding to each main group based on a relative position relationship between each sub-group of each main group; and
a feature capturing circuit, coupled to the calculation circuit and sorting the images in each main group based on a time sequence of each image, obtaining a first relative moving distance between each image and its previous image in each main group based on a second algorithm, and obtaining a second relative moving distance between each image and the first image in each main group based on the first relative moving distance corresponding to each image in each main group, wherein the second relative moving distance of each image is taken as the feature of each image.

### Claim 2 (depends on 1)

The image positioning device of claim 1, wherein the first algorithm is a deep learning algorithm or a triangle positioning algorithm.

### Claim 3 (depends on 1)

The image positioning device of claim 1, wherein the second algorithm is an image comparing algorithm or an IMU algorithm.

### Claim 4 (depends on 1)

The image positioning device of claim 1, wherein the positioning circuit classifies the images in each main group into the plurality of sub-groups based on the second relative moving distance of each image of each main group and the clustering algorithm.

### Claim 5 (depends on 4)

The image positioning device of claim 4, wherein the positioning circuit obtains the relative position relationship between each sub-group in each main group based on the second relative moving distance of each image of each main group and positions each sub-group based on the relative position relationship.

### Claim 6 (depends on 1)

The image positioning device of claim 1, wherein the clustering algorithm is a k-means algorithm.

### Claim 7

An image positioning method, applied to an image positioning device, comprising:
obtaining, by a calculation circuit of the image positioning device, a plurality of images from an image capturing device;
classifying, by the calculation circuit, the images into a plurality of main groups based on a first algorithm, wherein each main group corresponds to a different area;
classifying, by the calculation circuit, the images of each main group into a plurality of sub-groups based on a feature of each image of each main group and a clustering algorithm; and
positioning, by a positioning circuit of the image positioning device, each sub-group in the area corresponding to each main group based on a relative position relationship between each sub-group of each main group,
wherein the method further comprises:
sorting, by a feature capturing circuit of the image positioning device, the images in each main group based on a time sequence of each image;
obtaining, by the feature capturing circuit, a first relative moving distance between each image and its previous image in each main group based on a second algorithm; and
obtaining, by the feature capturing circuit, a second relative moving distance between each image and the first image in each main group based on the first relative moving distance corresponding to each image in each main group, wherein the second relative moving distance of each image is taken as the feature of each image.

### Claim 8 (depends on 7)

The image positioning method of claim 7, wherein the first algorithm is a deep learning algorithm or a triangle positioning algorithm.

### Claim 9 (depends on 7)

The image positioning method of claim 7, wherein the second algorithm is an image comparing algorithm or an IMU algorithm.

### Claim 10 (depends on 7)

The image positioning method of claim 7, further comprising:
classifying, by the positioning circuit, the images in each main group into the plurality of sub-groups based on the second relative moving distance of each image of each main group and the clustering algorithm.

### Claim 11 (depends on 10)

The image positioning method of claim 10, further comprising:
obtaining, by the positioning circuit, the relative position relationship between each sub-group in each main group based on the second relative moving distance of each image of each main group and positions each sub-group based on the relative position relationship.

### Claim 12 (depends on 7)

The image positioning method of claim 7, wherein the clustering algorithm is a k-means algorithm.