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rizu-me's Projects

3dptv icon 3dptv

3D Particle Tracking Velocimetry software is now called OpenPTV see http://www.openptv.net

defocus-sim icon defocus-sim

Simulation to quantify the accuracy of single molecule orientation measurements by defocused wide-field microscopy

dep_model icon dep_model

This is an example of the implementation of the control-oriented model for dielectrophoretical force and torque for arbitrarily shaped objects polarized by an arbitrary electric field.

depth-from-defocus-matlab icon depth-from-defocus-matlab

This is a Matlab implementation of Depth from Defocus using your mobile phone. In addition I use a number of techniques such as graph cuts and matting laplacian to improve the results

depth_from_focus icon depth_from_focus

Conventional Depth from Focus(DfF) estimation with slight focus variations in image sequences

dmenet icon dmenet

[CVPR2019] Official TensorFlow Implementation for "Deep Defocus Map Estimation using Domain Adaptation"

labelimg icon labelimg

🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images

misaligned-dfd icon misaligned-dfd

MATLAB implementation of depth from defocus algorithm for misaligned photos

mr icon mr

particle tracking and microrheology toolkit

multi-object-portion-tracking-in-4d-fluorescence-microscopy-imagery-with-deep-feature-maps icon multi-object-portion-tracking-in-4d-fluorescence-microscopy-imagery-with-deep-feature-maps

#2019 CVPR workshop published paper. Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps. 3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches objects’ portions using an extended search. Experimental results confirm that the proposed method achieves 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy than the state-of-art methods

online-cv icon online-cv

A minimal Jekyll Theme to host your resume (CV)

personal-website icon personal-website

Code that'll help you kickstart a personal website that showcases your work as a software developer.

prog4comp icon prog4comp

Resources for the book "Programming for Computations" by S. Linge and H. P. Langtangen

python-for-bioimage-analysis icon python-for-bioimage-analysis

This is the repository for a Python bioimage analysis course which establishes the fundamentals of image analysis in the context of biological imaging.

rizwen.github.io icon rizwen.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

surfcut icon surfcut

Fiji macro for cutting confocal stacks at various depths relative to surface signal

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