Object representation and comparison inferred from its medial axis

2002 ◽  
Author(s):  
S. Fernandez Vidal ◽  
E. Bardinet ◽  
G. Malandain ◽  
S. Damas ◽  
N. Perez de la Blanca Capilla
Keyword(s):  
Object Representation ◽  
Medial Axis

scholarly journals The medial axis as a robust model of object representation

2016 ◽  
Vol 16 (12) ◽  
pp. 169 ◽  
Author(s):  
Vladislav Ayzenberg ◽  
Sami Yousif ◽  
Stella Lourenco
Keyword(s):  
Object Representation ◽  
Medial Axis ◽  
Robust Model

scholarly journals MAT-Net: Medial Axis Transform Network for 3D Object Recognition

2019 ◽  
Author(s):  
Jianwei Hu ◽  
Bin Wang ◽  
Lihui Qian ◽  
Yiling Pan ◽  
Xiaohu Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Representation ◽  
Nearest Neighbor ◽  
Medial Axis ◽  
Fixed Number ◽  
Learning Performance ◽  
Medial Axis Transform ◽  
K Nearest Neighbor ◽  
Global Features ◽  
Local Feature Extraction

3D deep learning performance depends on object representation and local feature extraction. In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT). Different from K-Nearest-Neighbor method which extracts local features by a fixed number of neighbors, our MAT-Net exploits effective modules Group-MAT and Edge-Net to process topological structure. Experimental results illustrate that MAT-Net demonstrates competitive or better performance on 3D shape recognition than state-of-the-art methods, and prove that MAT representation has excellent capacity in 3D deep learning, even in the case of low resolution.

scholarly journals Common spatiotemporal processing of visual features shapes object representation

2018 ◽  
Author(s):  
Paolo Papale ◽  
Monica Betta ◽  
Giacomo Handjaras ◽  
Giulia Malfatti ◽  
Luca Cecchetti ◽  
...  
Keyword(s):  
Object Representation ◽  
Temporal Dynamics ◽  
Medial Axis ◽  
Human Subjects ◽  
Physical World ◽  
Stimulus Onset ◽  
Low Level ◽  
Semantic Categories ◽  
Spatial Frequencies ◽  
Feature Processing

AbstractBiological vision relies on representations of the physical world at different levels of complexity. Relevant features span from simple low-level properties, as contrast and spatial frequencies, to object-based attributes, as shape and category. However, how these features are integrated into coherent percepts is still debated. Moreover, these dimensions often share common biases: for instance, stimuli from the same category (e.g., tools) may have similar shapes. Here, using magnetoencephalography, we revealed the temporal dynamics of feature processing in human subjects attending to pictures of items pertaining to different semantic categories. By employing Relative Weights Analysis, we mitigated collinearity between model-based descriptions of stimuli and showed that low-level properties (contrast and spatial frequencies), shape (medial-axis) and category are represented within the same spatial locations early in time: 100-150ms after stimulus onset. This fast and overlapping processing may result from independent parallel computations, with categorical representation emerging later than the onset of low-level feature processing, yet before shape coding. Categorical information is represented both before and after shape also suggesting a role for this feature in the refinement of categorical matching.

Sign in / Sign up

Export Citation Format

Share Document