GitHub topics: shape-descriptor

Repositories

meetps/EE-702

Project codes for EE702 Computer Vision.

Language: Python - Size: 67.3 MB - Last synced at: 3 days ago - Pushed at: over 8 years ago - Stars: 13 - Forks: 4

pvnieo/GeomFmaps_pytorch

A minimalist pytorch implementation of: "Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence"

Language: Python - Size: 13.7 KB - Last synced at: 10 days ago - Pushed at: over 1 year ago - Stars: 26 - Forks: 2

jrzaurin/Shoe-Shape-Classifier

Using the shape context algorithm to classify shoe shapes

Language: Python - Size: 1.37 MB - Last synced at: 9 days ago - Pushed at: almost 8 years ago - Stars: 23 - Forks: 16

pvnieo/SURFMNet-pytorch

A pytorch implementation of: "Unsupervised Deep Learning for Structured Shape Matching"

Language: Python - Size: 18 MB - Last synced at: 10 days ago - Pushed at: almost 4 years ago - Stars: 16 - Forks: 2

ShyBoy233/EllipticFourierDescriptor

Python implementation and interpretation of "Elliptic Fourier Features of a Closed Contour" with enhanced start point and rotation normalization methods.

Language: Python - Size: 12.8 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 1 - Forks: 0

bartvbl/libShapeDescriptor

A library containing GPU implementations of a number of 3D shape descriptors, along with some useful utilities.

Language: Cuda - Size: 5.32 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 11 - Forks: 3

nipy/mindboggle

Automated anatomical brain label/shape analysis software (+ website)

Language: Python - Size: 36.9 MB - Last synced at: 12 months ago - Pushed at: about 4 years ago - Stars: 142 - Forks: 54

zeynepturkmen/Digital-Image-Analysis-Assignments

Sabanci University CS419 (Digital Image and Video Analysis) Assignments

Language: Jupyter Notebook - Size: 34.1 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Morphological categorization of neurons in order to explore their functional features has drawn significant attention over past few decades. The enormous complexity in the structure of neurons poses a real challenge in the identification and analysis of similar and dissimilar neuronal cells. Existing methodologies often carry out strutural and geometrical simplifications, which substantially changes the morphological statistics. Using digitally-reconstructed neurons, we extend the work of Path2Path as ElasticP2P, which seamlessly integrates the graph-theoretic and differential-geometric frameworks. By decomposing a neuron into a set of paths, we derive graph metrics, which are path concurrence and path hierarchy. Next, we model each path as an elastic string to compute the geodesic distance between the paths of a pair of neurons. Later, we formulate the problem of finding the distance between two neurons as a path assignment problem with a cost function combining the graph metrics and the geodesic deformation of paths.

Language: Matlab - Size: 19.5 KB - Last synced at: over 1 year ago - Pushed at: about 7 years ago - Stars: 1 - Forks: 0