View source for 17932478. DETECTING AND RECOVERING PATTERNS IN DIGITAL RASTER IMAGES simplified abstract (ADOBE INC.)

=DETECTING AND RECOVERING PATTERNS IN DIGITAL RASTER IMAGES=


==Organization Name==

[[:Category:ADOBE INC.|ADOBE INC.]]


[[Category:ADOBE INC.]]

==Inventor(s)==

[[:Category:Tarun Beri of Noida (IN)|Tarun Beri of Noida (IN)]][[Category:Tarun Beri of Noida (IN)]]

[[:Category:Vineet Agarwal of Guwahati (IN)|Vineet Agarwal of Guwahati (IN)]][[Category:Vineet Agarwal of Guwahati (IN)]]

[[:Category:Matthew Fisher of San Francisco CA (US)|Matthew Fisher of San Francisco CA (US)]][[Category:Matthew Fisher of San Francisco CA (US)]]

==DETECTING AND RECOVERING PATTERNS IN DIGITAL RASTER IMAGES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17932478 titled 'DETECTING AND RECOVERING PATTERNS IN DIGITAL RASTER IMAGES

==Simplified Explanation==

The disclosed system automatically detects and reconstructs patterns in digital images by comparing neighborhood descriptors of pixels, utilizing non-maximum suppression to reduce the set of similar pixels, determining pattern cells within the image, and modifying boundaries to include pattern objects.

* The system compares neighborhood descriptors of pixels to identify structurally similar pixels in digital images.
* Non-maximum suppression is used to reduce the set of similar pixels to collinear pixels within the image.
* The system determines if a group of similar pixels define the boundaries of a pattern cell forming a rectangular grid pattern.
* Boundaries of detected pattern cells are modified to include pattern objects via a sliding window corresponding to the pattern cell.

== Potential Applications ==
This technology can be applied in various fields such as image processing, computer vision, pattern recognition, and machine learning.

== Problems Solved ==
1. Automatic detection and reconstruction of patterns in digital images.
2. Efficient reduction of similar pixels to collinear pixels for pattern recognition.

== Benefits ==
1. Improved accuracy in pattern detection and reconstruction.
2. Time-saving in analyzing digital images for patterns.
3. Enhanced efficiency in pattern recognition tasks.

== Potential Commercial Applications ==
Optical character recognition (OCR) software, image editing tools, security systems for pattern recognition, and quality control in manufacturing processes can benefit from this technology.

== Possible Prior Art ==
One possible prior art could be the use of edge detection algorithms in image processing to identify patterns in digital images.

== Unanswered Questions ==
=== How does the system handle noise or variations in the digital images that may affect pattern detection accuracy? ===
The system may need to incorporate noise reduction techniques or adaptive algorithms to account for variations in the images.

=== Can the system detect and reconstruct complex patterns beyond rectangular grid patterns? ===
Further research and development may be needed to enhance the system's capabilities in detecting and reconstructing more intricate patterns in digital images.


==Original Abstract Submitted==

Methods, systems, and non-transitory computer readable storage media are disclosed for automatically detecting and reconstructing patterns in digital images. The disclosed system determines structurally similar pixels of a digital image by comparing neighborhood descriptors that include the structural context for neighborhoods of the pixels. In response to identify structurally similar pixels of a digital image, the disclosed system utilizes non-maximum suppression to reduce the set of structurally similar pixels to collinear pixels within the digital image. Additionally, the disclosed system determines whether a group of structurally similar pixels define the boundaries of a pattern cell that forms a rectangular grid pattern within the digital image. The disclosed system also modifies a boundary of a detected pattern cell to include a human-perceived pattern object via a sliding window corresponding to the pattern cell.



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