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 4):
The depth estimation method, as recited in  claim 3 , wherein a loss function L used in the first stage during training is: L = 1 N \ue89e \u2211 i \ue89e d i 2 - 1 2 \ue89e N 2 \ue89e ( \u2211 i \ue89e d i ) 2 + 1 N \ue89e \u2211 i \ue89e [ ( \u2207 x \ue89e d i ) 2 + ( \u2207 y \ue89e d i ) 2 ] wherein d i =lgx i \u2212lgx i * , x i  and x i * are a predicted depth value and a ground truth of the No. i effective pixel respectively, \u2207 x d i , and \u2207 y d i  are a horizontal gradient and a vertical gradient of d i  respectively.

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): 0.6870409695053424
- 35 USC 102 Novelty (BERT): 0.5191917548788142
- Combined Prediction Score: 0.6702560480426896
- Mean Citation Score: 215.05326
- Max Citation Score: 262.94138
- Similarity Product: 163.22124040864583

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