DE102021205034A1 - Trainieren eines maschinenlernfähigen modells zur schätzung des relativen objektmassstabs - Google Patents
Trainieren eines maschinenlernfähigen modells zur schätzung des relativen objektmassstabs Download PDFInfo
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- DE102021205034A1 DE102021205034A1 DE102021205034.4A DE102021205034A DE102021205034A1 DE 102021205034 A1 DE102021205034 A1 DE 102021205034A1 DE 102021205034 A DE102021205034 A DE 102021205034A DE 102021205034 A1 DE102021205034 A1 DE 102021205034A1
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
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- G—PHYSICS
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Program-controlled manipulators
- B25J9/16—Program controls
- B25J9/1694—Program controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33034—Online learning, training
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/35—Nc in input of data, input till input file format
- G05B2219/35074—Display object, recognition of geometric forms
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/20—Control system inputs
- G05D1/22—Command input arrangements
- G05D1/221—Remote-control arrangements
- G05D1/227—Handing over between remote control and on-board control; Handing over between remote control arrangements
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Medical Informatics (AREA)
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- General Health & Medical Sciences (AREA)
- Geometry (AREA)
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- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
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Priority Applications (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102021205034.4A DE102021205034A1 (de) | 2021-05-18 | 2021-05-18 | Trainieren eines maschinenlernfähigen modells zur schätzung des relativen objektmassstabs |
| US17/662,013 US12125228B2 (en) | 2021-05-18 | 2022-05-04 | Training a machine learnable model to estimate relative object scale |
| CN202210533429.1A CN115375908A (zh) | 2021-05-18 | 2022-05-17 | 训练机器可学习模型以估计相对对象尺度 |
| JP2022080909A JP2022177826A (ja) | 2021-05-18 | 2022-05-17 | オブジェクトの相対スケールを推定する機械学習可能なモデルのトレーニング |
| KR1020220060783A KR20220156470A (ko) | 2021-05-18 | 2022-05-18 | 상대적 객체 스케일을 추정하기 위한 머신 학습가능 모델의 훈련 |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102021205034.4A DE102021205034A1 (de) | 2021-05-18 | 2021-05-18 | Trainieren eines maschinenlernfähigen modells zur schätzung des relativen objektmassstabs |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| DE102021205034A1 true DE102021205034A1 (de) | 2022-11-24 |
Family
ID=83898785
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| DE102021205034.4A Pending DE102021205034A1 (de) | 2021-05-18 | 2021-05-18 | Trainieren eines maschinenlernfähigen modells zur schätzung des relativen objektmassstabs |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US12125228B2 (https=) |
| JP (1) | JP2022177826A (https=) |
| KR (1) | KR20220156470A (https=) |
| CN (1) | CN115375908A (https=) |
| DE (1) | DE102021205034A1 (https=) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116089428A (zh) * | 2023-01-05 | 2023-05-09 | 长城汽车股份有限公司 | 一种比例尺显示方法、系统、电子设备、车辆及存储介质 |
| US12614406B2 (en) * | 2023-07-11 | 2026-04-28 | Capital One Services, Llc | Systems and methods for applying scale factors to image objects |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8625902B2 (en) | 2010-07-30 | 2014-01-07 | Qualcomm Incorporated | Object recognition using incremental feature extraction |
| DE102018206848A1 (de) * | 2018-05-03 | 2019-11-07 | Robert Bosch Gmbh | Verfahren und Vorrichtung zum Ermitteln eines Tiefeninformationsbilds aus einem Eingangsbild |
| JP7003972B2 (ja) * | 2019-06-11 | 2022-01-21 | トヨタ自動車株式会社 | 距離推定装置、距離推定方法及び距離推定用コンピュータプログラム |
| EP3965009A1 (en) | 2020-09-08 | 2022-03-09 | Robert Bosch GmbH | Device and method for training a scale-equivariant convolutional neural network |
-
2021
- 2021-05-18 DE DE102021205034.4A patent/DE102021205034A1/de active Pending
-
2022
- 2022-05-04 US US17/662,013 patent/US12125228B2/en active Active
- 2022-05-17 JP JP2022080909A patent/JP2022177826A/ja active Pending
- 2022-05-17 CN CN202210533429.1A patent/CN115375908A/zh active Pending
- 2022-05-18 KR KR1020220060783A patent/KR20220156470A/ko active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| CN115375908A (zh) | 2022-11-22 |
| KR20220156470A (ko) | 2022-11-25 |
| JP2022177826A (ja) | 2022-12-01 |
| US12125228B2 (en) | 2024-10-22 |
| US20220375113A1 (en) | 2022-11-24 |
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