Jaklič et al., 2016 - Google Patents
Automatic digitization of pluviograph strip chartsJaklič et al., 2016
View PDF- Document ID
- 4414607375159681317
- Author
- Jaklič A
- Šajn L
- Derganc G
- Peer P
- Publication year
- Publication venue
- Meteorological Applications
External Links
Snippet
An algorithm for automatic digitization of pluviograph strip charts is presented. The rainfall signal is incrementally extracted from the scanned image of a strip chart by combining the moving average method and the curve edge following method. The mechanical properties of …
- 238000004422 calculation algorithm 0 abstract description 36
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm
- G01F23/22—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm by measurement of physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water
- G01F23/26—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm by measurement of physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring variations of capacity or inductance of capacitors or inductors arising from the presence of liquid or fluent solid material in the electric or electromagnetic fields
- G01F23/261—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm by measurement of physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring variations of capacity or inductance of capacitors or inductors arising from the presence of liquid or fluent solid material in the electric or electromagnetic fields for discrete levels
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm
- G01F23/0061—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm characterised by the level signal processing means
- G01F23/0069—Indicating or measuring liquid level, or level of fluent solid material, e.g. indicating in terms of volume, indicating by means of an alarm characterised by the level signal processing means particular electronic circuits for digital processing equipment
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Castillo et al. | Comparing the accuracy of several field methods for measuring gully erosion | |
Dethier et al. | Toward improved accuracy of remote sensing approaches for quantifying suspended sediment: Implications for suspended‐sediment monitoring | |
Yin et al. | How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review | |
Cenci et al. | Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management | |
Padhee et al. | Spatio-temporal reconstruction of MODIS NDVI by regional land surface phenology and harmonic analysis of time-series | |
Estornell et al. | Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area | |
Che Ros et al. | Homogeneity and trends in long-term rainfall data, Kelantan River Basin, Malaysia | |
Ma et al. | Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California | |
Duong et al. | Single and two epoch analysis of ICESat full waveform data over forested areas | |
Wang et al. | Reconstruction of satellite chlorophyll-a data using a modified DINEOF method: a case study in the Bohai and Yellow seas, China | |
Stauffer et al. | Ensemble postprocessing of daily precipitation sums over complex terrain using censored high-resolution standardized anomalies | |
Steinmann et al. | Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data | |
Jaklič et al. | Automatic digitization of pluviograph strip charts | |
Zhang et al. | Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging | |
Jalili Pirani et al. | Geostatistical and deterministic methods for rainfall interpolation in the Zayandeh Rud basin, Iran | |
van Oort et al. | Spatial variability in classification accuracy of agricultural crops in the Dutch national land-cover database | |
Shamsoddini et al. | Improving lidar-based forest structure mapping with crown-level pit removal | |
Hamzehpour et al. | Spatial prediction of soil salinity using kriging with measurement errors and probabilistic soft data | |
Debella-Gilo | Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM | |
Dutra et al. | The extreme forecast index at the seasonal scale | |
Wei et al. | Detecting damaged buildings using a texture feature contribution index from post-earthquake remote sensing images | |
Holmlund et al. | Constraining 135 years of mass balance with historic structure-from-motion photogrammetry on Storglaciären, Sweden | |
Herzog et al. | Measuring zero water level in stream reaches: A comparison of an image‐based versus a conventional method | |
MacLean et al. | Requirements for labelling forest polygons in an object-based image analysis classification | |
Borgogno Mondino et al. | How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study |