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 3):
The depth estimation method, as recited in  claim 1 , wherein the first stack of the DCNN is an Imagenet pre-trained network, which adopts first seven layers of an Alexnet module or first fifteen layers of a VGG16 module; the second stack is formed by five convolution layers, first four of the five convolution layers are proceeded with pooling, a last of the five convolution layers has a linear output; wherein an input of a first convolution layer of the five convolution layers is the normalized sample images obtained in the step (1), and an input of a second convolution layer of the five convolution layers is a feature formed by concatenating an up-sampled output of the first stack with a pooling output of the first convolution layer of the five convolution layers; all inputs of a later convolution layers is an output of a previous convolution layer thereof; the third stack is formed by four convolution layers, first three thereof are proceeded with pooling, a last of the four convolution layers has a linear output; wherein the input of a first convolution layer of the four convolution layers is the normalized sample images obtained in the step (1), and an input of a second convolution layer of the four convolution layers is a feature formed by concatenating an up-sampled output of the second stack with a pooling output of the first convolution layer of the four convolution layers; all inputs of a later convolution layer is an output of a previous convolution layer thereof.

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): 0.687630443301556
- 35 USC 102 Novelty (BERT): 0.5159068185773562
- Combined Prediction Score: 0.670458080829136
- Mean Citation Score: 215.05326
- Max Citation Score: 262.94138
- Similarity Product: 173.43645281406398

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