Computer Vision and Machine Learning with RGB-D Sensors
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham ; s.l.
Springer International Publishing
2014
Imprint: Springer |
| Schriftenreihe: | Advances in Computer Vision and Pattern Recognition
|
| Schlagworte: | |
| Online-Zugang: | DE-522 DE-634 DE-Aug4 DE-M347 DE-92 DE-859 DE-861 DE-473 DE-355 DE-703 DE-20 DE-29 DE-739 Volltext Inhaltsverzeichnis Abstract |
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Datensatz im Suchindex
| _version_ | 1819713677904838656 |
|---|---|
| adam_text | COMPUTER VISION AND MACHINE LEARNING WITH RGB-D SENSORS
/
: 2014
TABLE OF CONTENTS / INHALTSVERZEICHNIS
PART I: SURVEYS
3D DEPTH CAMERAS IN VISION: BENEFITS AND LIMITATIONS OF THE HARDWARE
A STATE-OF-THE-ART REPORT ON MULTIPLE RGB-D SENSOR RESEARCH AND ON
PUBLICLY AVAILABLE RGB-D DATASETS
PART II: RECONSTRUCTION, MAPPING AND SYNTHESIS
CALIBRATION BETWEEN DEPTH AND COLOR SENSORS FOR COMMODITY DEPTH CAMERAS
DEPTH MAP DENOISING VIA CDT-BASED JOINT BILATERAL FILTER
HUMAN PERFORMANCE CAPTURE USING MULTIPLE HANDHELD KINECTS
HUMAN CENTERED 3D HOME APPLICATIONS VIA LOW-COST RGBD CAMERAS
MATCHING OF 3D OBJECTS BASED ON 3D CURVES
USING SPARSE OPTICAL FLOW FOR TWO-PHASE GAS FLOW CAPTURING WITH MULTIPLE
KINECTS
PART III: DETECTION, SEGMENTATION AND TRACKING
RGB-D SENSOR-BASED COMPUTER VISION ASSISTIVE TECHNOLOGY FOR VISUALLY
IMPAIRED PERSONS
RGB-D HUMAN IDENTIFICATION AND TRACKING IN A SMART ENVIRONMENT
PART IV: LEARNING-BASED RECOGNITION
FEATURE DESCRIPTORS FOR DEPTH-BASED HAND GESTURE RECOGNITION
HAND PARSING AND GESTURE RECOGNITION WITH A COMMODITY DEPTH CAMERA
LEARNING FAST HAND POSE RECOGNITION
REAL TIME HAND-GESTURE RECOGNITION USING RGB-D SENSOR
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
COMPUTER VISION AND MACHINE LEARNING WITH RGB-D SENSORS
/
: 2014
ABSTRACT / INHALTSTEXT
THE COMBINATION OF HIGH-RESOLUTION VISUAL AND DEPTH SENSING, SUPPORTED
BY MACHINE LEARNING, OPENS UP NEW OPPORTUNITIES TO SOLVE REAL-WORLD
PROBLEMS IN COMPUTER VISION. THIS AUTHORITATIVE TEXT/REFERENCE PRESENTS
AN INTERDISCIPLINARY SELECTION OF IMPORTANT, CUTTING-EDGE RESEARCH ON
RGB-D BASED COMPUTER VISION. DIVIDED INTO FOUR SECTIONS, THE BOOK OPENS
WITH A DETAILED SURVEY OF THE FIELD, FOLLOWED BY A FOCUSED EXAMINATION
OF RGB-D BASED 3D RECONSTRUCTION, MAPPING AND SYNTHESIS. THE WORK
CONTINUES WITH A SECTION DEVOTED TO NOVEL TECHNIQUES THAT EMPLOY DEPTH
DATA FOR OBJECT DETECTION, SEGMENTATION AND TRACKING, AND CONCLUDES WITH
EXAMPLES OF ACCURATE HUMAN ACTION INTERPRETATION AIDED BY DEPTH SENSORS.
TOPICS AND FEATURES: DISCUSSES THE CALIBRATION OF COLOR AND DEPTH
CAMERAS, THE REDUCTION OF NOISE ON DEPTH MAPS, AND METHODS FOR CAPTURING
HUMAN PERFORMANCE IN 3D REVIEWS A SELECTION OF APPLICATIONS WHICH USE
RGB-D INFORMATION TO RECONSTRUCT HUMAN FIGURES, EVALUATE ENERGY
CONSUMPTION, AND OBTAIN ACCURATE ACTION CLASSIFICATION PRESENTS AN
INNOVATIVE APPROACH FOR 3D OBJECT RETRIEVAL, AND FOR THE RECONSTRUCTION
OF GAS FLOW FROM MULTIPLE KINECT CAMERAS DESCRIBES AN RGB-D COMPUTER
VISION SYSTEM DESIGNED TO ASSIST THE VISUALLY IMPAIRED, AND ANOTHER FOR
SMART-ENVIRONMENT SENSING TO ASSIST ELDERLY AND DISABLED PEOPLE EXAMINES
THE EFFECTIVE FEATURES THAT CHARACTERIZE STATIC HAND POSES, AND
INTRODUCES A UNIFIED FRAMEWORK TO ENFORCE BOTH TEMPORAL AND SPATIAL
CONSTRAINTS FOR HAND PARSING PROPOSES A NEW CLASSIFIER ARCHITECTURE FOR
REAL-TIME HAND POSE RECOGNITION, AND A NOVEL HAND SEGMENTATION AND
GESTURE RECOGNITION SYSTEM RESEARCHERS AND PRACTITIONERS WORKING IN
COMPUTER VISION, HCI AND MACHINE LEARNING WILL FIND THIS TO BE A
MUST-READ TEXT. THE BOOK ALSO SERVES AS A USEFUL REFERENCE FOR GRADUATE
STUDENTS STUDYING COMPUTER VISION, PATTERN RECOGNITION OR MULTIMEDIA
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Shao, Ling |
| author_GND | (DE-588)142907162 (DE-588)142052833 |
| author_facet | Shao, Ling |
| author_role | aut |
| author_sort | Shao, Ling |
| author_variant | l s ls |
| building | Verbundindex |
| bvnumber | BV042034660 |
| classification_rvk | ST 330 |
| collection | ZDB-2-SCS |
| ctrlnum | (OCoLC)885833533 (DE-599)BVBBV042034660 |
| dewey-full | 004 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-3-319-08651-4 |
| format | Electronic eBook |
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| id | DE-604.BV042034660 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T04:12:03Z |
| institution | BVB |
| isbn | 9783319086514 |
| language | English |
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| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer International Publishing Imprint: Springer |
| record_format | marc |
| series2 | Advances in Computer Vision and Pattern Recognition |
| spellingShingle | Shao, Ling Computer Vision and Machine Learning with RGB-D Sensors Maschinelles Sehen (DE-588)4129594-8 gnd Informatik (DE-588)4026894-9 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4026894-9 (DE-588)4033447-8 |
| title | Computer Vision and Machine Learning with RGB-D Sensors |
| title_auth | Computer Vision and Machine Learning with RGB-D Sensors |
| title_exact_search | Computer Vision and Machine Learning with RGB-D Sensors |
| title_full | Computer Vision and Machine Learning with RGB-D Sensors edited by Ling Shao, Jungong Han, Pushmeet Kohli, Zhengyou Zhang |
| title_fullStr | Computer Vision and Machine Learning with RGB-D Sensors edited by Ling Shao, Jungong Han, Pushmeet Kohli, Zhengyou Zhang |
| title_full_unstemmed | Computer Vision and Machine Learning with RGB-D Sensors edited by Ling Shao, Jungong Han, Pushmeet Kohli, Zhengyou Zhang |
| title_short | Computer Vision and Machine Learning with RGB-D Sensors |
| title_sort | computer vision and machine learning with rgb d sensors |
| topic | Maschinelles Sehen (DE-588)4129594-8 gnd Informatik (DE-588)4026894-9 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Maschinelles Sehen Informatik Künstliche Intelligenz |
| url | https://doi.org/10.1007/978-3-319-08651-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027476066&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027476066&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT shaoling computervisionandmachinelearningwithrgbdsensors AT hanjungong computervisionandmachinelearningwithrgbdsensors AT kohlipushmeet computervisionandmachinelearningwithrgbdsensors AT zhangzhengyu computervisionandmachinelearningwithrgbdsensors |