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 1):
A depth estimation method for a monocular image based on a multi-scale CNN (convolution neural network) and a continuous CRF (conditional random field), comprising steps of:\n (1) obtaining a preset amount of sample images, and processing the sample images with data enhancement and normalization in sequence; (2) training a DCNN (deep convolution neural network) with normalized sample images by two stages, wherein the DCNN comprises a first stack, a second stack and a third stack; the first stack and the second stack are trained by a first stage, and the third stack is trained by a second stage; and (3) for an input image I whose depth is to be estimated, establishing a conditional probability distribution function P(Y|I) related to the input image I and a depth map Y thereof according to a DCNN output: \ue89e P \ue8a0 ( Y | I ) = 1 Z \ue8a0 ( I ) \ue89e exp \ue8a0 ( - E \ue8a0 ( Y , I ) ) \ue89e Z \ue8a0 ( I ) = \u222b Y \ue89e exp \ue8a0 ( - E \ue8a0 ( Y , I ) ) \ue89e dY E \ue8a0 ( Y , I ) = \u2211 i \ue89e ( z i - y i ) 2 + \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 ) ] wherein Z(I) is a partition function corresponding to the input image I, E(Y,I) is an energy function related to the input image I and the depth map Y thereof, z i  is a predicted logarithmic depth value of a No. i effective pixel, y i  and y j  are true logarithmic depth values of the No. i effective pixel and a No. j effective pixel respectively, p i  and p j  are coordinate values of the No. i effective pixel and the No. j effective pixel respectively, c i  and c j  are LAB color values of the No. i effective pixel and the No. j effective pixel respectively; w ij1 , w ij2 , \u03c3 ij1 , \u03c3 ij2  and \u03c3 ij3  are all CRF model parameters; i and j are both natural numbers and 1\u2264i\u2264N, 1\u2264j\u2264N, N is a total effective pixel number; then finding a solution to maximize P(Y|I), and converting a calculated depth map \u0176 from a logarithmic space into a linear space, so as to obtain a depth map with a high accuracy and with object contour detail reserved.

Metadata:
- Claim Count in Document: 11.0
- Percentile: 88.0
- Lexical Diversity: 1.64286
- Patent Class: 396.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['16067819', '15406504', '15400233', '15755556', '14194931']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6638951532718157
- 35 USC 102 Novelty (BERT): 0.5010006284686863
- Combined Prediction Score: 0.6476057007915028
- Mean Citation Score: 215.05326
- Max Citation Score: 262.94138
- Similarity Product: 212.6130385898709

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