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Found 9 papers, 0 papers with code
OdontoAI: A human-in-the-loop labeled data set and an online platform to boost research on dental panoramic radiographs
In a novel online platform, called OdontoAI, created to work as task central for this novel data set, we released 4, 000 images, from which 2, 000 have their labels publicly available for model fitting.
Attention-based fusion of semantic boundary and non-boundary information to improve semantic segmentation
To the best of our knowledge, ours is the first work to show that boundary detection can improve semantic segmentation when fused through a semantic fusion gate (attention model).
Information Ranking Using Optimum-Path Forest
The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing.
On estimating gaze by self-attention augmented convolutions
In our experiments, results showed a decrease of the average angular error by 2. 38% when compared to state-of-the-art methods on the MPIIFaceGaze data set, and a second-place on the EyeDiap data set.
Rotation-invariant shipwreck recognition with forward-looking sonar
To deal with that problem, we propose a novel trainable method to detect and recognize (identify) specific target objects under the sea with a forward-looking sonar.
Classification of glomerular hypercellularity using convolutional features and support vector machine
To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.
ISEC: Iterative over-Segmentation via Edge Clustering
Several image pattern recognition tasks rely on superpixel generation as a fundamental step.
Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives
This review presents an in-depth study of the literature on segmentation methods applied in dental imaging.
How far did we get in face spoofing detection?
The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks.
