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 6):
The depth estimation method, as recited in  claim 1 , wherein the CRF model parameters w ij1 , w ij2 , \u03c3 ij1 , \u03c3 ij2  and \u03c3 ij3  are obtained by: integrating the CRF into the DCNN, and optimizing with an SGD method, wherein loss is calculated by comparing a CRF output with a ground truth logarithmic depth map; or independently optimizing a CRF module by using cross validation, wherein optimized parameters are searched by cycling from large step sizes to small step sizes in a certain range this time the DCNN parameters are fixed.

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): 0.6881114793521661
- 35 USC 102 Novelty (BERT): 0.5132225778963221
- Combined Prediction Score: 0.6706225892065817
- Mean Citation Score: 215.05326
- Max Citation Score: 262.94138
- Similarity Product: 181.78049081003545

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