546 citations · 740 across the 23 of their papers we have counts for
8 papers · 2 filters
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
Ertunc Erdil, Sinan Yıldırım, Müjdat Çetin +1
Segmenting images of low quality or with missing data is a challenging problem. Integrating statistical prior information about the shapes to be segmented can improve the segmentat…
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
Ting Liu, Miaomiao Zhang, Mehran Javanmardi +2
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use…
Dendritic Spine Shape Analysis: A Clustering Perspective
Muhammad Usman Ghani, Ertunc Erdil, Sumeyra Demir Kanik +6
Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step toward…
Disjunctive Normal Level Set: An Efficient Parametric Implicit Method
Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen
Level set methods are widely used for image segmentation because of their capability to handle topological changes. In this paper, we propose a novel parametric level set method ca…
Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes
Fitsum Mesadi, Tolga Tasdizen
Decomposition of shapes into (approximate) convex parts is essential for applications such as part-based shape representation, shape matching, and collision detection. In this pape…
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
Effective convolutional neural networks are trained on large sets of labeled data. However, creating large labeled datasets is a very costly and time-consuming task. Semi-supervise…
Mutual Exclusivity Loss for Semi-Supervised Deep Learning
Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that…
Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation
Mehran Javanmardi, Mehdi Sajjadi, Ting Liu +1
We introduce a novel unsupervised loss function for learning semantic segmentation with deep convolutional neural nets (ConvNet) when densely labeled training images are not availa…