4,264 research outputs found
Scale Invariant Interest Points with Shearlets
Shearlets are a relatively new directional multi-scale framework for signal
analysis, which have been shown effective to enhance signal discontinuities
such as edges and corners at multiple scales. In this work we address the
problem of detecting and describing blob-like features in the shearlets
framework. We derive a measure which is very effective for blob detection and
closely related to the Laplacian of Gaussian. We demonstrate the measure
satisfies the perfect scale invariance property in the continuous case. In the
discrete setting, we derive algorithms for blob detection and keypoint
description. Finally, we provide qualitative justifications of our findings as
well as a quantitative evaluation on benchmark data. We also report an
experimental evidence that our method is very suitable to deal with compressed
and noisy images, thanks to the sparsity property of shearlets
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
This paper proposes a computational approach for analysis of strokes in line
drawings by artists. We aim at developing an AI methodology that facilitates
attribution of drawings of unknown authors in a way that is not easy to be
deceived by forged art. The methodology used is based on quantifying the
characteristics of individual strokes in drawings. We propose a novel algorithm
for segmenting individual strokes. We designed and compared different
hand-crafted and learned features for the task of quantifying stroke
characteristics. We also propose and compare different classification methods
at the drawing level. We experimented with a dataset of 300 digitized drawings
with over 80 thousands strokes. The collection mainly consisted of drawings of
Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of
representative works of other artists. The experiments shows that the proposed
methodology can classify individual strokes with accuracy 70%-90%, and
aggregate over drawings with accuracy above 80%, while being robust to be
deceived by fakes (with accuracy 100% for detecting fakes in most settings)
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
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Modeling of the Acute Toxicity of Benzene Derivatives by Complementary QSAR Methods
A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for
fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure-
Activity Relationship (QSAR) models, either using or not using molecular descriptors,
respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the
molecular structure, described through an appropriate graphical tool (variable-size labeled
rooted ordered trees) by defining suitable representation rules. The input trees are encoded by
an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity
training examples. Owing to the use of a flexible encoding approach, the model is
target invariant and does not need a priori definition of molecular descriptors. The results
obtained in this study were analyzed together with those of a model based on molecular
descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression
selection of descriptors (CROMRsel). The comparison revealed interesting similarities that
could lead to the development of a combined approach, exploiting the complementary
characteristics of the two approaches
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