System and method for symbol detection

Patent metadata

Field	Value
Publication number	US-12524637-B2
Application number	US-202418746961-A
Country	US
Kind code	B2
Filing date	Jun 18, 2024
Priority date	Jun 18, 2024
Publication date	Jan 13, 2026
Grant date	Jan 13, 2026

Abstract

An optical symbol detector has an input operative to receive a captured image frame. The detector includes a feature extractor engine coupled to the input to produce a downscaled descriptor map based on the captured image frame, the descriptor map including a convolutional map of blocks of the captured image frame. The detector further includes a coarse estimator engine coupled to an output of the feature extractor engine to produce a detection indication and localization information of an optical pattern indicative of a machine-readable symbol based on the downscaled descriptor map.

First claim

What is claimed is:

1 . An optical symbol detector, comprising:
an input operative to receive a captured image frame; a feature extractor engine coupled to the input to produce a downscaled descriptor map based on the captured image frame, the downscaled descriptor map including a convolutional map of blocks of the captured image frame; and a coarse estimator engine coupled to an output of the feature extractor engine to produce a detection indication and localization information of an optical pattern indicative of a machine-readable symbol based on the downscaled descriptor map, wherein the coarse estimator engine includes a trained decision tree cascaded with a trained neural network.

2 . The optical symbol detector of claim 1 , wherein the feature extractor engine is operative to:
partition the image frame into blocks; and for each block:
compute a downscaled descriptor based on an S-transform operation to produce a vector representative of that block; and
compute a convolutional descriptor based on the vector of the block convolved with a kernel that includes vectors of neighboring blocks;
wherein the downscaled descriptor map comprises a set of convolutional descriptors of the blocks.

3 . The optical symbol detector of claim 2 , wherein the blocks are non-overlapping.

4 . The optical symbol detector of claim 2 , wherein the blocks are square.

5 . The optical symbol detector of claim 2 , wherein the blocks are 8×8 pixels.

6 . The optical symbol detector of claim 2 , wherein in the computation of the convolutional descriptor, for each block, the kernel includes vectors representing neighboring blocks along each side of that block.

7 . The optical symbol detector of claim 6 , wherein the kernel is 3×3 blocks.

8 . The optical symbol detector of claim 1 , wherein the feature extractor engine is arranged to computed multiple downscaled descriptors of the descriptor map in parallel and independently of one another.

9 . The optical symbol detector of claim 1 , wherein the feature extractor engine is further operative to perform contrast normalization of the convolutional map to produce a contrast-normalized feature map that is input to the coarse estimator engine.

10 . The optical symbol detector of claim 1 , wherein the coarse estimator engine includes a plurality of computation paths, each computation path corresponding to a detectable pattern, wherein each computation path includes a decision tree trained to detect the corresponding pattern, cascaded with a neural network trained to detect the corresponding pattern.

11 . The optical symbol detector of claim 10 , wherein the localization information includes location, horizontal vector, and vertical vector information.

12 . The optical symbol detector of claim 1 , wherein the trained neural network includes a set of output nodes trained to produce localization information of the corresponding detection pattern.

13 . The optical symbol detector of claim 1 , further comprising:
a validation and fine estimator engine having a first input coupled to an output of the coarse estimator engine, and a second input coupled to the input operative to receive the captured image frame, wherein the validation and fine estimator engine further includes:
a neural network trained to extract a portion of the captured image frame received via the second input that corresponds to the optical pattern based on the first input, and to determine whether that portion corresponds to a known pattern type.

14 . A method for operating an optical symbol detector, comprising:
receiving a captured image frame; autonomously computationally producing a downscaled descriptor map based on the captured image frame, the downscaled descriptor map including a convolutional map of blocks of the captured image frame; and autonomously computationally producing a detection indication and localization information of an optical pattern indicative of a machine-readable symbol based on the downscaled descriptor map; wherein producing the downscaled descriptor map includes:
partitioning the image frame into blocks; and
for each block:
computing a downscaled descriptor based on an S-transform operation to produce a vector representative of that block; and
computing a convolutional descriptor based on the vector of the block convolved with a kernel that includes vectors of neighboring blocks;
wherein the downscaled descriptor map comprises a set of convolutional descriptors of the blocks.

15 . The method of claim 14 , wherein in producing the downscaled descriptor map, the blocks are non-overlapping and square.

16 . The method of claim 14 , wherein in computing the convolutional descriptor, for each block, the kernel includes vectors representing neighboring blocks along each side of that block.

17 . The method of claim 14 , wherein producing the downscaled descriptor map further includes performing contrast normalization of the convolutional map to produce a contrast-normalized feature map.

18 . The method of claim 14 , wherein producing the detection indication and localization information of the optical pattern includes processing a plurality of computation paths, each computation path corresponding to a detectable pattern, wherein each computation path includes a decision tree trained to detect the corresponding pattern, cascaded with a neural network trained to detect the corresponding pattern.

19 . The method of claim 14 , further comprising:
extracting a portion of the captured image frame that corresponds to the optical pattern and determining whether that portion corresponds to a known pattern type.

Assignees

• Datalogic Usa Inc
• Datalogic IP Tech Srl

Inventors

• Santi Stefano
• Prihoda Thomas

Classifications

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T5/20

  using local operators · CPC title

• G06V10/44

  Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components · CPC title

• G06V10/771

  Feature selection, e.g. selecting representative features from a multi-dimensional feature space · CPC title

• G06T7/11

  Region-based segmentation · CPC title