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The Photogrammetric Record
Resumen/Descripción – provisto por la editorial en inglés
The Photogrammetric Record is an international journal containing original, independently and rapidly refereed articles that reflect modern advancements in photogrammetry, 3D imaging, computer vision, and other related non-contact fields. All aspects of the measurement workflow are relevant, from sensor characterisation and modelling, data acquisition, processing algorithms and product generation, to novel applications. The journal provides a record of new research which will contribute both to the advancement of photogrammetric knowledge and to the application of techniques in novel ways. It also seeks to stimulate debate though correspondence, and carries reviews of recent literature from the wider geomatics discipline.Palabras clave – provistas por la editorial
Photogrammetry; laser scanning; lidar; range imaging; optical metrology; GIS; remote sensing; digita
Disponibilidad
| Institución detectada | Período | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | desde ene. 1953 / hasta dic. 2023 | Wiley Online Library |
Información
Tipo de recurso:
revistas
ISSN impreso
0031-868X
ISSN electrónico
1477-9730
Editor responsable
John Wiley & Sons, Inc. (WILEY)
País de edición
Reino Unido
Fecha de publicación
1953-
Tabla de contenidos
doi: 10.1111/phor.12430
A comparative study on deep‐learning methods for dense image matching of multi‐angle and multi‐date remote sensing stereo‐images
Hessah Albanwan; Rongjun Qin
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12431
Road Curbs Extraction from Mobile Laser Scanning Point Clouds with Multidimensional Rotation‐Invariant Version of the Local Binary Pattern Features
Xinjiang Ma; Dongjie Yue; Rufei Liu; Ruisheng Wang; Shaolin Zhu; Minye Wang; Jiayong Yu
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12429
Intelligent video surveillance using enhanced deep belief based multilayered convolution neural network classification techniques
Nattar Kannan Kaliappan; Saravanan Thapasimuthu Rajeswari; Prabakar Dakshinamoorthy; Nirmalraj Sundararaju; Ramesh Sundar
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12446
Small object detection leveraging density‐aware scale adaptation
Yu Wan; Zhaohong Liao; Jia Liu; Weiwei Song; Hong Ji; Zhi Gao
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12448
A histogram‐based sampling method for point cloud registration
Osman Ervan; Hakan Temeltas
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12453
Learning‐based encoded target detection on iteratively orthorectified images for accurate fisheye calibration
Haonan Dong; Jian Yao; Ye Gong; Li Li; Shaosheng Cao; Yuxuan Li
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12455
Real‐time mosaic of multiple fisheye surveillance videos based on geo‐registration and rectification
Jiongli Gao; Jun Wu; Mingyi Huang; Gang Xu
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12454
Automatic calibration of terrestrial laser scanners using intensity features
Jing Qiao; Tomislav Medic; Andreas Baumann‐Ouyang
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12456
A survey on conventional and learning‐based methods for multi‐view stereo
Elisavet Konstantina Stathopoulou; Fabio Remondino
<jats:title>Abstract</jats:title><jats:p>3D reconstruction of scenes using multiple images, relying on robust correspondence search and depth estimation, has been thoroughly studied for the two‐view and multi‐view scenarios in recent years. Multi‐view stereo (MVS) algorithms aim to generate a rich, dense 3D model of the scene in the form of a dense point cloud or a triangulated mesh. In a typical MVS pipeline, the robust estimations for the camera poses along with the sparse points obtained from structure from motion (SfM) are used as input. During this process, the depth of generally every pixel of the scene is to be calculated. Several methods, either conventional or, more recently, learning‐based have been developed for solving the correspondence search problem. A vast amount of research exists in the literature using local, global or semi‐global stereomatching approaches, with the PatchMatch algorithm being among the most popular and efficient conventional ones in the last decade. Yet, and despite the widespread evolution of the algorithms, yielding complete, accurate and aesthetically pleasing 3D representations of a scene remains an open issue in real‐world and large‐scale photogrammetric applications. This work aims to provide a concrete survey on the most widely used MVS methods, investigating underlying concepts and challenges. To this end, the theoretical background and relative literature are discussed for both conventional and learning‐based approaches, with a particular focus on close‐range 3D reconstruction applications.</jats:p>
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible
doi: 10.1111/phor.12459
Adaptive region aggregation for multi‐view stereo matching using deformable convolutional networks
Han Hu; Liupeng Su; Shunfu Mao; Min Chen; Guoqiang Pan; Bo Xu; Qing Zhu
<jats:title>Abstract</jats:title><jats:p>Deep‐learning methods have demonstrated promising performance in multi‐view stereo (MVS) applications. However, it remains challenging to apply a geometrical prior on the adaptive matching windows to achieve efficient three‐dimensional reconstruction. To address this problem, this paper proposes a learnable adaptive region aggregation method based on deformable convolutional networks (DCNs), which is integrated into the feature extraction workflow for MVSNet method that uses coarse‐to‐fine structure. Following the conventional pipeline of MVSNet, a DCN is used to densely estimate and apply transformations in our feature extractor, which is composed of a deformable feature pyramid network (DFPN). Furthermore, we introduce a dedicated offset regulariser to promote the convergence of the learnable offsets of the DCN. The effectiveness of the proposed DFPN is validated through quantitative and qualitative evaluations on the BlendedMVS and Tanks and Temples benchmark datasets within a cross‐dataset evaluation setting.</jats:p>
Palabras clave: Earth and Planetary Sciences (miscellaneous); Computers in Earth Sciences; Computer Science Applications; Engineering (miscellaneous).
Pp. No disponible