PATENT CLAIM ANALYSIS

Application Number: 15746568
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-09
Patent Classification: ["382", "165000"]

Abstract:
The application discloses a method and an apparatus for recognizing RGB-D objects based on adaptive similarity measure of dense matching item, wherein the method can include at least the following steps: convolution neural network features of a to-be-queried object and a reference object are extracted; dense matching is carried out on the reference object and the to-be-queried object on the basis of the convolution neural network features fused with RGB and depth information; similarity between the reference object and the to-be-queried object is measured according to a dense matching result; and the to-be-queried object is classified based on the similarity between the reference object and the to-be-queried object. With the embodiments of the present application, at least in part, the technical problem of how to improve the robustness of object recognition is solved.

Claim (Index 3):
Method according to  claim 2 , wherein the data terms specifically include: D i \ue8a0 ( t i ) = \u03b8 \u00b7 [ f rgb \ue8a0 ( p i | I r ) - f rgb \ue8a0 ( p i + t i | I q ) ] + ( 1 - \u03b8 ) \u00b7 [ f depth \ue8a0 ( p i | I r ) - f depth \ue8a0 ( p i \ue89e + t i | I q ) ] . ; wherein f rgb (p i |I q ), f rgb (p i |I r ) refer to the convolution neural network features at the i-th pixel point in the RGB images of the to-be-queried object and the reference object respectively; f depth (p i |I q ),f depth (p i |I r ) refer to extracted depth features; \u03b8 refers to a fusion coefficient of RGB and depth information.

Metadata:
- Claim Count in Document: 3.0
- Percentile: 86.0
- Lexical Diversity: 2.05797
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16067819', '15038325', '15783908', '12968796', '11365273']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3356938434490113
- 35 USC 102 Novelty (BERT): 0.5121691465317605
- Combined Prediction Score: 0.3533413737572862
- Mean Citation Score: 190.103838
- Max Citation Score: 225.36264
- Similarity Product: 126.10252204377652

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test