PATENT CLAIM ANALYSIS

Application Number: 15751872
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-08
Patent Classification: ["396", "089000"]

Abstract:
A depth estimation method for a monocular image based on a multi-scale CNN and a continuous CRF is disclosed in this invention. A CRF module is adopted to calculate a unary potential energy according to the output depth map of a DCNN, and the pairwise sparse potential energy according to input RGB images. MAP (maximum a posteriori estimation) algorithm is used to infer the optimized depth map at last. The present invention integrates optimization theories of the multi-scale CNN with that of the continuous CRF. High accuracy and a clear contour are both achieved in the estimated depth map; the depth estimated by the present invention has a high resolution and detailed contour information can be kept for all objects in the scene, which provides better visual effects.

Claim (Index 5):
The depth estimation method, as recited in  claim 1 , wherein in the step (3), the solution to maximize P(Y|I) is achieved by: Y ~ = argmax Y \ue89e P \ue8a0 ( Y | I ) = A - 1 \ue89e Z A = E + D - S wherein Z is a depth value after up-sampling and boundary completion an output of the third stack of the DCNN with bilinear interpolation, D is a diagonal matrix with diagonal elements d ii =\u03a3 j s ij , S is a similarity matrix whose element at a row i and a column j is s ij = \u2211 ij \ue89e ( y i - y j ) 2 \ue89e \u2003 [ w ij \ue89e \ue89e 1 \ue89e exp \ue8a0 ( - \uf605 p i - p j \uf606 2 2 \ue89e \u03c3 ij \ue89e \ue89e 1 2 - \uf605 c i - c j \uf606 2 2 \ue89e \u03c3 ij \ue89e \ue89e 2 2 ) + w ij \ue89e \ue89e 2 \ue89e exp \ue8a0 ( - \uf605 p i - p j \uf606 2 2 \ue89e \u03c3 ij \ue89e \ue89e 3 2 ) ] , E is an N\u00d7N unit matrix.

Metadata:
- Claim Count in Document: 11.0
- Percentile: 88.0
- Lexical Diversity: 1.64286
- Patent Class: 396.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['16067819', '15406504', '15400233', '15755556', '14194931']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6886947084762118
- 35 USC 102 Novelty (BERT): 0.5099641315670339
- Combined Prediction Score: 0.670821650785294
- Mean Citation Score: 215.05326
- Max Citation Score: 262.94138
- Similarity Product: 191.9062130831337

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test