123,326 research outputs found
LDOP: Local Directional Order Pattern for Robust Face Retrieval
The local descriptors have gained wide range of attention due to their
enhanced discriminative abilities. It has been proved that the consideration of
multi-scale local neighborhood improves the performance of the descriptor,
though at the cost of increased dimension. This paper proposes a novel method
to construct a local descriptor using multi-scale neighborhood by finding the
local directional order among the intensity values at different scales in a
particular direction. Local directional order is the multi-radius relationship
factor in a particular direction. The proposed local directional order pattern
(LDOP) for a particular pixel is computed by finding the relationship between
the center pixel and local directional order indexes. It is required to
transform the center value into the range of neighboring orders. Finally, the
histogram of LDOP is computed over whole image to construct the descriptor. In
contrast to the state-of-the-art descriptors, the dimension of the proposed
descriptor does not depend upon the number of neighbors involved to compute the
order; it only depends upon the number of directions. The introduced descriptor
is evaluated over the image retrieval framework and compared with the
state-of-the-art descriptors over challenging face databases such as PaSC, LFW,
PubFig, FERET, AR, AT&T, and ExtendedYale. The experimental results confirm the
superiority and robustness of the LDOP descriptor.Comment: Published in Multimedia Tools and Applications, Springe
Direct Visual Odometry using Bit-Planes
Feature descriptors, such as SIFT and ORB, are well-known for their
robustness to illumination changes, which has made them popular for
feature-based VSLAM\@. However, in degraded imaging conditions such as low
light, low texture, blur and specular reflections, feature extraction is often
unreliable. In contrast, direct VSLAM methods which estimate the camera pose by
minimizing the photometric error using raw pixel intensities are often more
robust to low textured environments and blur. Nonetheless, at the core of
direct VSLAM is the reliance on a consistent photometric appearance across
images, otherwise known as the brightness constancy assumption. Unfortunately,
brightness constancy seldom holds in real world applications.
In this work, we overcome brightness constancy by incorporating feature
descriptors into a direct visual odometry framework. This combination results
in an efficient algorithm that combines the strength of both feature-based
algorithms and direct methods. Namely, we achieve robustness to arbitrary
photometric variations while operating in low-textured and poorly lit
environments. Our approach utilizes an efficient binary descriptor, which we
call Bit-Planes, and show how it can be used in the gradient-based optimization
required by direct methods. Moreover, we show that the squared Euclidean
distance between Bit-Planes is equivalent to the Hamming distance. Hence, the
descriptor may be used in least squares optimization without sacrificing its
photometric invariance. Finally, we present empirical results that demonstrate
the robustness of the approach in poorly lit underground environments
Local Multi-Grouped Binary Descriptor with Ring-based Pooling Configuration and Optimization
Local binary descriptors are attracting increasingly attention due to their
great advantages in computational speed, which are able to achieve real-time
performance in numerous image/vision applications. Various methods have been
proposed to learn data-dependent binary descriptors. However, most existing
binary descriptors aim overly at computational simplicity at the expense of
significant information loss which causes ambiguity in similarity measure using
Hamming distance. In this paper, by considering multiple features might share
complementary information, we present a novel local binary descriptor, referred
as Ring-based Multi-Grouped Descriptor (RMGD), to successfully bridge the
performance gap between current binary and floated-point descriptors. Our
contributions are two-fold. Firstly, we introduce a new pooling configuration
based on spatial ring-region sampling, allowing for involving binary tests on
the full set of pairwise regions with different shapes, scales and distances.
This leads to a more meaningful description than existing methods which
normally apply a limited set of pooling configurations. Then, an extended
Adaboost is proposed for efficient bit selection by emphasizing high variance
and low correlation, achieving a highly compact representation. Secondly, the
RMGD is computed from multiple image properties where binary strings are
extracted. We cast multi-grouped features integration as rankSVM or sparse SVM
learning problem, so that different features can compensate strongly for each
other, which is the key to discriminativeness and robustness. The performance
of RMGD was evaluated on a number of publicly available benchmarks, where the
RMGD outperforms the state-of-the-art binary descriptors significantly.Comment: To appear in IEEE Trans. on Image Processing, 201
From handcrafted to deep local features
This paper presents an overview of the evolution of local features from
handcrafted to deep-learning-based methods, followed by a discussion of several
benchmarks and papers evaluating such local features. Our investigations are
motivated by 3D reconstruction problems, where the precise location of the
features is important. As we describe these methods, we highlight and explain
the challenges of feature extraction and potential ways to overcome them. We
first present handcrafted methods, followed by methods based on classical
machine learning and finally we discuss methods based on deep-learning. This
largely chronologically-ordered presentation will help the reader to fully
understand the topic of image and region description in order to make best use
of it in modern computer vision applications. In particular, understanding
handcrafted methods and their motivation can help to understand modern
approaches and how machine learning is used to improve the results. We also
provide references to most of the relevant literature and code.Comment: Preprin
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm
In this paper, a new texture descriptor named "Fractional Local Neighborhood
Intensity Pattern" (FLNIP) has been proposed for content based image retrieval
(CBIR). It is an extension of the Local Neighborhood Intensity Pattern
(LNIP)[1]. FLNIP calculates the relative intensity difference between a
particular pixel and the center pixel of a 3x3 window by considering the
relationship with adjacent neighbors. In this work, the fractional change in
the local neighborhood involving the adjacent neighbors has been calculated
first with respect to one of the eight neighbors of the center pixel of a 3x3
window. Next, the fractional change has been calculated with respect to the
center itself. The two values of fractional change are next compared to
generate a binary bit pattern. Both sign and magnitude information are encoded
in a single descriptor as it deals with the relative change in magnitude in the
adjacent neighborhood i.e., the comparison of the fractional change. The
descriptor is applied on four multi-resolution images -- one being the raw
image and the other three being filtered gaussian images obtained by applying
gaussian filters of different standard deviations on the raw image to signify
the importance of exploring texture information at different resolutions in an
image. The four sets of distances obtained between the query and the target
image are then combined with a genetic algorithm based approach to improve the
retrieval performance by minimizing the distance between similar class images.
The performance of the method has been tested for image retrieval on four
popular databases. The precision and recall values observed on these databases
have been compared with recent state-of-art local patterns. The proposed method
has shown a significant improvement over many other existing methods.Comment: MTAP, Springer(Minor Revision
Intensity and Rescale Invariant Copy Move Forgery Detection Techniques
In this contemporary world digital media such as videos and images behave as
an active medium to carry valuable information across the globe on all fronts.
However there are several techniques evolved to tamper the image which has made
their authenticity untrustworthy. CopyMove Forgery CMF is one of the most
common forgeries present in an image where a cluster of pixels are duplicated
in the same image with potential postprocessing techniques. Various
state-of-art techniques are developed in the recent years which are effective
in detecting passive image forgery. However most methods do fail when the
copied image is rescaled or added with certain intensity before being pasted
due to de-synchronization of pixels in the searching process. To tackle this
problem the paper proposes distinct novel algorithms which recognize a unique
approach of using Hus invariant moments and Discreet Cosine Transformations DCT
to attain the desired rescale invariant and intensity invariant CMF detection
techniques respectively. The experiments conducted quantitatively and
qualitatively demonstrate the effectiveness of the algorithm.Comment: Further research is active on this paper in VIT University. Hence,
the paper is yet not publishe
U-CATCH: Using Color ATtribute of image patCHes in binary descriptors
In this study, we propose a simple yet very effective method for extracting
color information through binary feature description framework. Our method
expands the dimension of binary comparisons into RGB and YCbCr spaces, showing
more than 100% matching improve ment compared to non-color binary descriptors
for a wide range of hard-to-match cases. The proposed method is general and can
be applied to any binary descriptor to make it color sensitive. It is faster
than classical binary descriptors for RGB sampling due to the abandonment of
grayscale conversion and has almost identical complexity (insignificant
compared to smoothing operation) for YCbCr sampling
Local Jet Pattern: A Robust Descriptor for Texture Classification
Methods based on local image features have recently shown promise for texture
classification tasks, especially in the presence of large intra-class variation
due to illumination, scale, and viewpoint changes. Inspired by the theories of
image structure analysis, this paper presents a simple, efficient, yet robust
descriptor namely local jet pattern (LJP) for texture classification. In this
approach, a jet space representation of a texture image is derived from a set
of derivatives of Gaussian (DtGs) filter responses up to second order, so
called local jet vectors (LJV), which also satisfy the Scale Space properties.
The LJP is obtained by utilizing the relationship of center pixel with the
local neighborhood information in jet space. Finally, the feature vector of a
texture region is formed by concatenating the histogram of LJP for all elements
of LJV. All DtGs responses up to second order together preserves the intrinsic
local image structure, and achieves invariance to scale, rotation, and
reflection. This allows us to develop a texture classification framework which
is discriminative and robust. Extensive experiments on five standard texture
image databases, employing nearest subspace classifier (NSC), the proposed
descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for
Outex_TC-00010 (Outex_TC10), and Outex_TC-00012 (Outex_TC12), KTH-TIPS,
Brodatz, CUReT, respectively, which are outperforms the state-of-the-art
methods.Comment: Accepted in Multimedia Tools and Applications, Springe
Bit-Planes: Dense Subpixel Alignment of Binary Descriptors
Binary descriptors have been instrumental in the recent evolution of
computationally efficient sparse image alignment algorithms. Increasingly,
however, the vision community is interested in dense image alignment methods,
which are more suitable for estimating correspondences from high frame rate
cameras as they do not rely on exhaustive search. However, classic dense
alignment approaches are sensitive to illumination change. In this paper, we
propose an easy to implement and low complexity dense binary descriptor, which
we refer to as bit-planes, that can be seamlessly integrated within a
multi-channel Lucas & Kanade framework. This novel approach combines the
robustness of binary descriptors with the speed and accuracy of dense alignment
methods. The approach is demonstrated on a template tracking problem achieving
state-of-the-art robustness and faster than real-time performance on consumer
laptops (400+ fps on a single core Intel i7) and hand-held mobile devices (100+
fps on an iPad Air 2).Comment: 10 pages. In submissio
Robust Face Recognition with Structural Binary Gradient Patterns
This paper presents a computationally efficient yet powerful binary framework
for robust facial representation based on image gradients. It is termed as
structural binary gradient patterns (SBGP). To discover underlying local
structures in the gradient domain, we compute image gradients from multiple
directions and simplify them into a set of binary strings. The SBGP is derived
from certain types of these binary strings that have meaningful local
structures and are capable of resembling fundamental textural information. They
detect micro orientational edges and possess strong orientation and locality
capabilities, thus enabling great discrimination. The SBGP also benefits from
the advantages of the gradient domain and exhibits profound robustness against
illumination variations. The binary strategy realized by pixel correlations in
a small neighborhood substantially simplifies the computational complexity and
achieves extremely efficient processing with only 0.0032s in Matlab for a
typical face image. Furthermore, the discrimination power of the SBGP can be
enhanced on a set of defined orientational image gradient magnitudes, further
enforcing locality and orientation. Results of extensive experiments on various
benchmark databases illustrate significant improvements of the SBGP based
representations over the existing state-of-the-art local descriptors in the
terms of discrimination, robustness and complexity. Codes for the SBGP methods
will be available at
http://www.eee.manchester.ac.uk/research/groups/sisp/software/
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