PATENT CLAIM ANALYSIS

Application Number: 16199283
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-03
Patent Classification: ["348", "159000"]

Abstract:
An approach for re-identifying an object in a test image is presented. Similarity measures between the test image and training images captured by a first camera are determined. The similarity measures are based on Bhattacharyya distances between feature representations of an estimated background region of the test image and feature representations of background regions of the training images. A transformed test image based on the Bhattacharyya distances has a brightness that is different from the test image's brightness and matches a brightness of training images captured by a second camera. An appearance of the transformed test image resembles an appearance of a capture of the test image by the second camera. Another image included in test images captured by the second camera is identified as being closest in appearance to the transformed test image and another object in the identified other image is a re-identification of the object.

Claim (Index 6):
The method of  claim 1 , further comprising steps of:\n during the training mode, the computer determining brightness transfer functions (BTFs) H i j  by determining H j \u22121 (H i (B i )), which is an inverted cumulative histogram, so that B j =H i j (B i ), which transfers a brightness value B i  in an object O i  to a corresponding brightness value B j  in an object O j , wherein object O i  is included in each first training image and object O j  is included in second training images captured by the second camera; based on the Bhattacharyya distances, the computer determining weights of the first training images captured by the first camera, the weights corresponding to the BTFs; based on the Bhattacharyya distances, the computer determining a weighted brightness transfer function (WBTF) by weighting the BTFs by the corresponding weights and combining the weighted BTFs; the computer receiving a threshold distance; the computer receiving K training images captured by the first camera; the computer determining a first background region of the first test image; the computer determining respective second background regions of each of the K training images captured by the first camera; the computer determining first feature representations in the first background region of the first test image; and the computer determining respective second feature representations in each of the second background regions, wherein the step of determining the measures of similarity includes determining K respective Bhattacharyya distances between the first feature representations in the first background region of the first test image and each of the second feature representations, wherein the step of determining the BTFs is performed after the step of determining the K Bhattacharyya distances and includes selecting K BTFs from a plurality of BTFs, so that the K BTFs correspond to the K Bhattacharyya distances and so that each of the K Bhattacharyya distances is less than or equal to the threshold distance, wherein the step of determining the weights includes determining a matching cost \u03b1 k  corresponding to each of the K BTFs, wherein k=1, . . . , K, and wherein the step of determining the WBTF includes weighting each BTF of the K BTFs by the corresponding \u03b1 k  and determining a linear combination of the weighted K BTFs.

Metadata:
- Claim Count in Document: 44.0
- Percentile: 98.0
- Lexical Diversity: 2.5
- Patent Class: 348.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15471595', '13647645', '15634499', '14790323', '10966769']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5869427619485139
- 35 USC 102 Novelty (BERT): 0.5857102589259983
- Combined Prediction Score: 0.5868195116462624
- Mean Citation Score: 298.33274
- Max Citation Score: 467.33093
- Similarity Product: 463.4152558605665

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

Dataset: test