20,333 research outputs found
Face segregation and recognition by cortical multi-scale line and edge coding
Models of visual perception are based on image representations in
cortical area V1 and higher areas which contain many cell layers for feature
extraction. Basic simple, complex and end-stopped cells provide input for line,
edge and keypoint detection. In this paper we present an improved method for
multi-scale line/edge detection based on simple and complex cells. We illustrate
the line/edge representation for object reconstruction, and we present models for
multi-scale face (object) segregation and recognition that can be embedded into
feedforward dorsal and ventral data streams (the “what” and “where” subsystems)
with feedback streams from higher areas for obtaining translation, rotation
and scale invariance
Object Edge Contour Localisation Based on HexBinary Feature Matching
This paper addresses the issue of localising object
edge contours in cluttered backgrounds to support robotics
tasks such as grasping and manipulation and also to improve
the potential perceptual capabilities of robot vision systems. Our
approach is based on coarse-to-fine matching of a new recursively
constructed hierarchical, dense, edge-localised descriptor,
the HexBinary, based on the HexHog descriptor structure first
proposed in [1]. Since Binary String image descriptors [2]–
[5] require much lower computational resources, but provide
similar or even better matching performance than Histogram
of Orientated Gradient (HoG) descriptors, we have replaced
the HoG base descriptor fields used in HexHog with Binary
Strings generated from first and second order polar derivative
approximations. The ALOI [6] dataset is used to evaluate
the HexBinary descriptors which we demonstrate to achieve
a superior performance to that of HexHoG [1] for pose
refinement. The validation of our object contour localisation
system shows promising results with correctly labelling ~86% of edgel positions and mis-labelling ~3%
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
Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel
basis, that does not take prior knowledge about the visual world into account.
We investigate the generalized notion of frames designed with image properties
in mind, as alternatives to this parametrization. We show that frame-based
ResNets and Densenets can improve performance on Cifar-10+ consistently, while
having additional pleasant properties like steerability. By exploiting these
transformation properties explicitly, we arrive at dynamic steerable blocks.
They are an extension of residual blocks, that are able to seamlessly transform
filters under pre-defined transformations, conditioned on the input at training
and inference time. Dynamic steerable blocks learn the degree of invariance
from data and locally adapt filters, allowing them to apply a different
geometrical variant of the same filter to each location of the feature map.
When evaluated on the Berkeley Segmentation contour detection dataset, our
approach outperforms all competing approaches that do not utilize pre-training.
Our results highlight the benefits of image-based regularization to deep
networks
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
Micro scalar patterning for printing ultra fine solid lines in flexographic printing process
This research focuses on the study of ultra-fine solid lines printing by using Micro-flexographic machine which is combination of flexography and micro-contact printing technique. Flexography is one of the famous and high speed roll to roll printing techniques that are possible to create graphic and electronic device on variable substrates. Micro-contact printing is a low cost technique that usually uses for micro to nano scale image especially in fine solid lines image structure. Graphene is nano material that can be used as printing ink which usually uses in producing micro to nano scale electronic devices. Lanthanum is a rare earth metal that has potential in printing industry. The combination of both printing techniques is known as Micro-flexographic printing has been successfully produced the lowest fine solid lines width and gap. The new printing technique could print fine solid lines image below 10 ÎĽm on biaxially oriented polypropylene (BOPP) substrate by using graphene as printing ink. The Micro-flexographic printing technique has been successfully printed fine solid lines with 2.6 ÎĽm width. This study also elaborates the imprint lithography process in achieving micro down to nano fine solid lines structure below 10 ÎĽm. In an additional, the lanthanum target has been successful printed on variable substrates with good surface adhesion property. This research illustrates the ultra-fine solid lines printing capability for the application of printing electronic, graphic and bio-medical
Deformable kernels for early vision
Early vision algorithms often have a first stage of linear filtering
that 'extracts' from the image information at multiple
scales of resolution and multiple orientations. A common
difficulty in the design and implementation of such
schemes is that one feels compelled to discretize coarsely
the space of scales and orientations in order to reduce computation and storage costs. This discretization produces
anisotropies due to a loss of traslation-, rotation- scaling- invariance that makes early vision algorithms less precise and
more difficult to design. This need not be so: one can compute
and store efficiently the response of families of linear
filters defined on a continuum of orientations and scales. A
technique is presented that allows (1) to compute the best approximation of a given family using linear combinations of
a small number of 'basis' functions; (2) to describe all finite-dimensional families, i.e. the families of filters for which a
finite dimensional representation is possible with no error.
The technique is general and can be applied to generating
filters in arbitrary dimensions. Experimental results are presented that demonstrate the applicability of the technique to
generating multi-orientation multi-scale 20 edge-detection
kernels. The implementation issues are also discussed
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