Search Results for author: Aleksandar Zlateski

Found 9 papers, 2 papers with code

Large-scale image segmentation based on distributed clustering algorithms

no code implementations21 Jun 2021 Ran Lu, Aleksandar Zlateski, H. Sebastian Seung

Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions.

Chunking Semantic Segmentation

L3 Fusion: Fast Transformed Convolutions on CPUs

no code implementations4 Dec 2019 Rati Gelashvili, Nir Shavit, Aleksandar Zlateski

Fast convolutions via transforms, either Winograd or FFT, had emerged as a preferred way of performing the computation of convolutional layers, as it greatly reduces the number of required operations.

PZnet: Efficient 3D ConvNet Inference on Manycore CPUs

no code implementations18 Mar 2019 Sergiy Popovych, Davit Buniatyan, Aleksandar Zlateski, Kai Li, H. Sebastian Seung

Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks.

On the Importance of Label Quality for Semantic Segmentation

no code implementations CVPR 2018 Aleksandar Zlateski, Ronnachai Jaroensri, Prafull Sharma, Frédo Durand

We investigate the relationship between the quality of labels and the performance of ConvNets for semantic segmentation.

Semantic Segmentation

ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs

no code implementations17 Jun 2016 Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung

Other things being equal, processing a larger image tends to increase throughput, because fractionally less computation is wasted on the borders of the image.

Object Detection Semantic Segmentation

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction

no code implementations NeurIPS 2015 Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.

Boundary Detection Object Recognition

ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines

2 code implementations22 Oct 2015 Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung

Applying Brent's theorem to the task dependency graph implies that linear speedup with the number of processors is attainable within the PRAM model of parallel computation, for wide network architectures.

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

2 code implementations NeurIPS 2015 Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan, H. Sebastian Seung

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.

Boundary Detection

Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph

no code implementations1 May 2015 Aleksandar Zlateski, H. Sebastian Seung

We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, and then merges basins with a modified, size-dependent version of single linkage clustering.

Semantic Segmentation

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