# US 12,762,828 B1 · Object detection system determining object boundary edges from Penrose gradient data and related methods

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- Number: US 12,762,828 B1
- Document ID: US12762828B1
- Status: Granted
- Kind: B1
- Application number: 18/471,427
- Filed: 2023-09-21
- Granted: 2026-10-13
- Earliest claimed: 2023-09-21
- Assignees: Eagle Technology, LLC
- Applicants: Eagle Technology, LLC
- Inventors: Michael Mulford; Christopher Mullarkey; Julian Cuevas Paniagua; Stephanie Bertrand; Bernard V. Brower; Kevin Watson; Ronald Thomas Kneusel
- Web page: https://patentgrep.com/patent/US12762828B1

## CPC

- G06V 10/443: {by matching or filtering}
- G06T 7/13: Edge detection
- G06T 7/168: involving transform domain methods
- G06T 7/74: {involving reference images or patches}
- G06V 10/764: using classification, e.g. of video objects
- G06T 2207/20048: Transform domain processing
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

An object detection system may include a memory and a processor cooperating with the memory to determine gradient data from image data corresponding to a scene, with the scene including an object therein, and process the gradient data using a Penrose transform to generate Penrose gradient data including a plurality of peaks corresponding to the object. The processor may also perform edge filtering on the Penrose gradient data to determine boundary edges corresponding to the object based upon the plurality of peaks, determine a classification of the object based upon the determined boundary edges using an object classification model, and output the object classification.

## Claims

### Claim 1

An object detection system comprising:
a memory and a processor cooperating with the memory to
determine gradient data from image data corresponding to a scene, the scene including an object therein,
process the gradient data using a Penrose transform to generate Penrose gradient data including a plurality of peaks corresponding to the object,
perform edge filtering on the Penrose gradient data to determine boundary edges corresponding to the object based upon the plurality of peaks,
determine a classification of the object based upon the determined boundary edges using an object classification model, and
output the object classification.

### Claim 2 (depends on 1)

The object detection system of claim 1 wherein the scene comprises a geospatial scene, and wherein the processor is configured to determine a geospatial location of the object from the boundary edges and output the geospatial location with the object classification.

### Claim 3 (depends on 1)

The object detection system of claim 1 wherein the boundary edges define a centroid point for the object.

### Claim 4 (depends on 1)

The object detection system of claim 1 wherein the edge filtering comprises a primary edge filtering to determine first boundary edges, and a secondary edge filtering to determine second boundary edges smaller than the first boundary edges.

### Claim 5 (depends on 1)

The object detection system of claim 1 wherein the plurality of peaks are arranged in symmetrical pairs corresponding to bi-laterally symmetrical portions of the object.

### Claim 6 (depends on 1)

The object detection system of claim 1 wherein the Penrose transform comprises a first Penrose transform; and
wherein the processor further processes the gradient data using a second Penrose transform based upon generation of a linear signature from the first Penrose transform.

### Claim 7 (depends on 1)

The object detection system of claim 1 wherein the gradient data comprises gradient in-phase and quadrature-phase (GIQ) data.

### Claim 8 (depends on 7)

The object detection system of claim 7 wherein the GIQ data comprises GIQ magnitude data.

### Claim 9 (depends on 1)

The object detection system of claim 1 wherein the processor is configured to convert synthetic aperture radar (SAR) data to the image data prior to determining the gradient data therefrom.

### Claim 10 (depends on 1)

The object detection system of claim 1 wherein the object classification model comprises at least one of a National Imagery Interpretability Rating Scale (NIIRS) model and an image quality evaluation (IQE) model.

### Claim 11

An object detection system comprising:
a memory and a processor cooperating with the memory to
determine gradient in-phase and quadrature-phase (GIQ) data from image data corresponding to a geospatial scene, the scene including an object therein,
process the GIQ data using a Penrose transform to generate Penrose GIQ data including a plurality of peaks corresponding to the object,
perform edge filtering on the Penrose GIQ data to determine boundary edges corresponding to the object based upon the plurality of peaks,
determine a classification of the object based upon the determined boundary edges using an object classification model,
determine a geospatial location of the object from the boundary edges and output the geospatial location with the object classification, and
output the object classification and geospatial location.

### Claim 12 (depends on 11)

The object detection system of claim 11 wherein the boundary edges define a centroid point for the object.

### Claim 13 (depends on 11)

The object detection system of claim 11 wherein the edge filtering comprises a primary edge filtering to determine first boundary edges, and a secondary edge filtering to determine second boundary edges smaller than the first boundary edges.

### Claim 14 (depends on 11)

The object detection system of claim 11 wherein the plurality of peaks are arranged in symmetrical pairs corresponding to bi-laterally symmetrical portions of the object.

### Claim 15 (depends on 11)

The object detection system of claim 11 wherein the Penrose transform comprises a first Penrose transform; and wherein the processor further processes the gradient data using a second Penrose transform based upon generation of a linear signature from the first Penrose transform.

### Claim 16

An object detection method comprising:
using a processor and a memory associated therewith to
determine gradient data from image data corresponding to a scene, the scene including an object therein,
process the gradient data using a Penrose transform to generate Penrose gradient data including a plurality of peaks corresponding to the object,
perform edge filtering on the Penrose gradient data to determine boundary edges corresponding to the object based upon the plurality of peaks,
determine a classification of the object based upon the determined boundary edges using an object classification model, and
output the object classification.

### Claim 17 (depends on 16)

The method of claim 16 wherein the scene comprises a geospatial scene, and further comprising using the processor to determine a geospatial location of the object from the boundary edges and output the geospatial location with the object classification.

### Claim 18 (depends on 16)

The method of claim 16 wherein the boundary edges define a centroid point for the object.

### Claim 19 (depends on 16)

The method of claim 16 wherein the edge filtering comprises a primary edge filtering to determine first boundary edges, and a secondary edge filtering to determine second boundary edges smaller than the first boundary edges.

### Claim 20 (depends on 16)

The method of claim 16 wherein the plurality of peaks are arranged in symmetrical pairs corresponding to bi-laterally symmetrical portions of the object.

### Claim 21 (depends on 16)

The method of claim 16 wherein the Penrose transform comprises a first Penrose transform; and further comprising using the processor to process the gradient data using a second Penrose transform based upon generation of a linear signature from the first Penrose transform.

### Claim 22 (depends on 16)

The method of claim 16 wherein the gradient data comprises gradient in-phase and quadrature-phase (GIQ) data.