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.

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

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

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