Search Results for "image algorithm"

CRF-RNN is a deep neural architecture that integrates fully connected Conditional Random Fields (CRFs) with Convolutional Neural Networks (CNNs) by reformulating mean-field CRF inference as a Recurrent Neural Network. This fusion enables end-to-end training via backpropagation for semantic image segmentation tasks, eliminating the need for separate, offline post-processing steps. Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of objects. Currently we have trained this model to recognize 20 classes. This software allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you can send them to us. ...

...It computes synaptic signals from automatically segmented regions of interest and detects peaks that represent vesicle fusion events, thus, pre-synaptic activity. ISAD is based on MWA, which is a continuous wavelet transform based algorithm that employs multiple wavelets and is published as: Sokoll, S., Tönnies, K., and Heine, M. Detection of Spontaneous Vesicle Release at Individual Synapses Using Multiple Wavelets in a CWT-Based Algorithm. Med Image Comput Comput Assist Interv (MICCAI). 2012;15(Pt 1):165-72 ISAD is written in MATLAB and comes with a graphical user interface.