Plant species identification using computer vision techniques: a systematic literature review
J Wäldchen, P Mäder - Archives of computational methods in engineering, 2018 - Springer
Species knowledge is essential for protecting biodiversity. The identification of plants by
conventional keys is complex, time consuming, and due to the use of specific botanical …
conventional keys is complex, time consuming, and due to the use of specific botanical …
Content-based image retrieval: A review of recent trends
IM Hameed, SH Abdulhussain… - Cogent Engineering, 2021 - Taylor & Francis
With the availability of internet technology and the low-cost of digital image sensor,
enormous amount of image databases have been created in different kind of applications …
enormous amount of image databases have been created in different kind of applications …
Pointconv: Deep convolutional networks on 3d point clouds
Unlike images which are represented in regular dense grids, 3D point clouds are irregular
and unordered, hence applying convolution on them can be difficult. In this paper, we extend …
and unordered, hence applying convolution on them can be difficult. In this paper, we extend …
Prediction of aerodynamic flow fields using convolutional neural networks
An approximation model based on convolutional neural networks (CNNs) is proposed for
flow field predictions. The CNN is used to predict the velocity and pressure field in unseen …
flow field predictions. The CNN is used to predict the velocity and pressure field in unseen …
Dynamic graph cnn for learning on point clouds
Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …
PointNet: Deep learning on point sets for 3D classification and segmentation
RQ Charles, H Su, M Kaichun… - 2017 IEEE conference …, 2017 - ieeexplore.ieee.org
Point cloud is an important type of geometric data structure. Due to its irregular format, most
researchers transform such data to regular 3D voxel grids or collections of images. This …
researchers transform such data to regular 3D voxel grids or collections of images. This …
Pointnet: Deep learning on point sets for 3d classification and segmentation
Point cloud is an important type of geometric data structure. Due to its irregular format, most
researchers transform such data to regular 3D voxel grids or collections of images. This …
researchers transform such data to regular 3D voxel grids or collections of images. This …
Attentional shapecontextnet for point cloud recognition
We tackle the problem of point cloud recognition. Unlike previous approaches where a point
cloud is either converted into a volume/image or represented independently in a …
cloud is either converted into a volume/image or represented independently in a …
ConvPoint: Continuous convolutions for point cloud processing
A Boulch - Computers & Graphics, 2020 - Elsevier
Point clouds are unstructured and unordered data, as opposed to images. Thus, most
machine learning approach developed for image cannot be directly transferred to point …
machine learning approach developed for image cannot be directly transferred to point …
Lpd-net: 3d point cloud learning for large-scale place recognition and environment analysis
Point cloud based place recognition is still an open issue due to the difficulty in extracting
local features from the raw 3D point cloud and generating the global descriptor, and it's even …
local features from the raw 3D point cloud and generating the global descriptor, and it's even …