1,990 research outputs found
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
Visual identification by signature tracking
We propose a new camera-based biometric: visual signature identification. We discuss the importance of the parameterization of the signatures in order to achieve good classification results, independently of variations in the position of the camera with respect to the writing surface. We show that affine arc-length parameterization performs better than conventional time and Euclidean arc-length ones. We find that the system verification performance is better than 4 percent error on skilled forgeries and 1 percent error on random forgeries, and that its recognition performance is better than 1 percent error rate, comparable to the best camera-based biometrics
Tree decomposition and parameterized algorithms for RNA structure-sequence alignment including tertiary interactions and pseudoknots
We present a general setting for structure-sequence comparison in a large
class of RNA structures that unifies and generalizes a number of recent works
on specific families on structures. Our approach is based on tree decomposition
of structures and gives rises to a general parameterized algorithm, where the
exponential part of the complexity depends on the family of structures. For
each of the previously studied families, our algorithm has the same complexity
as the specific algorithm that had been given before.Comment: (2012
Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping
Tese de mestrado integrado, Engenharia BiomĂ©dica e BiofĂsica (BiofĂsica MĂ©dica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiĂŞnciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria.
Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT).
Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the
Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT
template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT
scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated
atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas
alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline
was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that
presented sub-optimal results. The first one was based on the similarity metric of the registration of every
scan to the study-specific CT template, the second aimed to identify any scans with regions that were
completely collapsed post registration, and the final one identified scans with a significant volume of
intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence
and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted,
along with a bias assessment of the BLAST-CT tool.
Our results show that the constructed pipeline is able to successfully localise TBI lesions across
the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each
brain region to be inversely correlated with the lesion volume within that region. No considerable bias
was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice
scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and
Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male
and female patients within a specific age range was caused by the discrepancy in lesion volume presented
by the scans included in each sample
- …