Carlos Joaquin Becker

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A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

Imaging modalities such as Electron Microscopy (EM) and Light Microscopy (LM) can now deliver high-quality, high-resolution image stacks of neural structures. Though these imaging modalities can be used to analyze a variety of components that are critical ...

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

We present a new approach for the automated segmentation of synapses in image stacks acquired by Electron Microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifie ...

Electron and Light Microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While Machine Learning algorithms can be used to help ...

Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time. While highly effective, they remain challenging to tune and their overall performance depends on the availability of large validation sets to esti ...

We present a new approach for the automated segmentation of excitatory synapses in image stacks acquired by electron microscopy. We rely on a large set of image features specifically designed to take spatial context into account and train a classifier that ...

We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypothe ...

Automatic visual detection of instruments in minimally invasive surgery (MIS) can significantly augment the procedure experience for operating clinicians. In this paper, we present a novel technique for detecting surgical instruments by constructing a robu ...

Carlos Joaquin Becker

Chattez avec Graph Search

Non-Linear Domain Adaptation with Boosting

Towards Scalable Segmentation of 3D Image Stacks

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Learning Context Cues for Synapse Segmentation

Domain Adaptation for Microscopy Imaging

Fast Object Detection with Entropy-Driven Evaluation

Learning Context Cues for Synapse Segmentation in EM Volumes

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

Supervised Feature Learning for Curvilinear Structure Segmentation