Sentence examples for vessel representation from inspiring English sources

Though generic vessel representation performs reasonably well, trained deep models which focus on specific attributes are significantly better in attribute categorization.

Verification performance is slightly lower for end-to-end learning -based representation compared to the 4144-dimensional output-based vessel representation.

Next, we train a multi-class classifier, using both the generic vessel representation (combined with a nonlinear SVM) and also specific deep representations (softmax classifier) for each attribute.

In Fig. 9, the recognition performances are illustrated for each vessel type and by using each generic vessel representation as feature vectors.

In order to achieve these objectives, we both test the use of our powerful 4144-dimensional output-based generic vessel representation and also employ specific attribute-based deep representations.

Here, we utilize the learned generic vessel representations as feature vectors for vessels.

Here, the goal is to classify images of 3965 vessels in IMO testing set by the use of generic vessel representations learned on images of IMO training set.

We perform content based vessel retrieval (CBIR), using Euclidean (L 2) and chi-squared (χ 2) distances as the similarity metric for four different vessel representations.

Random chance for recognition is also depicted in the figure in order to prove the success of the presented generic marine vessel representations.

However, not belonging to our concept of an ALS-conformed use of ICA duplex sonography, with the long axis plane, experts can assess vessel diameter, representation of vascular wall (intima-media thickness), ultrasound morphology, plaques, luminal narrowing of wall deposits and other internal pathologies, such as dissections.

The main thrust of this web system is the proposed VesselMap methodology, a 2D representation of vessel structures that provides an overview of all query results without any visual occlusions.