Zobrazeno 1 - 10
of 170
pro vyhledávání: '"Estrada, Rolando"'
Autor:
Khanal, Aashis, Estrada, Rolando
Accurate measurements of abnormalities like Artery-Vein ratio and tortuosity in fundus images is an actively researched task. Most of the research seems to compute such features independently. However, in this work, we have devised a fully automated
Externí odkaz:
http://arxiv.org/abs/2301.01791
The generalisation performance of a convolutional neural networks (CNN) is majorly predisposed by the quantity, quality, and diversity of the training images. All the training data needs to be annotated in-hand before, in many real-world applications
Externí odkaz:
http://arxiv.org/abs/2212.14658
We present a fully automatic, graph-based technique for extracting the retinal vascular topology -- that is, how different vessels are connected to each other -- given a single color fundus image. Determining this connectivity is very challenging bec
Externí odkaz:
http://arxiv.org/abs/2202.02382
Autor:
Estrada, Rolando Jose
Tree-like structures are fundamental in nature. A wide variety of two-dimensional imaging techniques allow us to image trees. However, an image of a tree typically includes spurious branch crossings and the original relationships of ancestry among ed
Externí odkaz:
http://hdl.handle.net/10161/8074
The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of the optic disc in a color fundus image is straight
Externí odkaz:
http://arxiv.org/abs/2110.00512
Autor:
Mousavi, Mehdi, Estrada, Rolando
Transparent objects are a very challenging problem in computer vision. They are hard to segment or classify due to their lack of precise boundaries, and there is limited data available for training deep neural networks. As such, current solutions for
Externí odkaz:
http://arxiv.org/abs/2107.11008
Neurons in real brains are enormously complex computational units. Among other things, they're responsible for transforming inbound electro-chemical vectors into outbound action potentials, updating the strengths of intermediate synapses, regulating
Externí odkaz:
http://arxiv.org/abs/2011.07035
Machine learning requires data, but acquiring and labeling real-world data is challenging, expensive, and time-consuming. More importantly, it is nearly impossible to alter real data post-acquisition (e.g., change the illumination of a room), making
Externí odkaz:
http://arxiv.org/abs/2007.06153
In many applications, data is easy to acquire but expensive and time-consuming to label prominent examples include medical imaging and NLP. This disparity has only grown in recent years as our ability to collect data improves. Under these constraints
Externí odkaz:
http://arxiv.org/abs/2007.02196
Autor:
Khanal, Aashis, Estrada, Rolando
Segmenting the retinal vasculature entails a trade-off between how much of the overall vascular structure we identify vs. how precisely we segment individual vessels. In particular, state-of-the-art methods tend to under-segment faint vessels, as wel
Externí odkaz:
http://arxiv.org/abs/1903.07803