46,509 research outputs found
Distributed associative memories for high-speed symbolic reasoning
This paper briefly introduces a novel symbolic reasoning system based upon distributed associative memories which are constructed from correlation matrix memories (CMM). The system is aimed at high-speed rule-based symbolic operations. It has the advantage of very fast rule matching without the long training times normally associated with neural-network-based symbolic manipulation systems. In particular, the network is able to perform partial matching on symbolic information at high speed. As such, the system is aimed at the practical use of neural networks in high-speed reasoning systems. The paper describes the advantages and disadvantages of using CMM and shows how the approach overcomes those disadvantages. It then briefly describes a system incorporating CMM
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%
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms
On Defining SPARQL with Boolean Tensor Algebra
The Resource Description Framework (RDF) represents information as
subject-predicate-object triples. These triples are commonly interpreted as a
directed labelled graph. We propose an alternative approach, interpreting the
data as a 3-way Boolean tensor. We show how SPARQL queries - the standard
queries for RDF - can be expressed as elementary operations in Boolean algebra,
giving us a complete re-interpretation of RDF and SPARQL. We show how the
Boolean tensor interpretation allows for new optimizations and analyses of the
complexity of SPARQL queries. For example, estimating the size of the results
for different join queries becomes much simpler
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
With advanced image journaling tools, one can easily alter the semantic
meaning of an image by exploiting certain manipulation techniques such as
copy-clone, object splicing, and removal, which mislead the viewers. In
contrast, the identification of these manipulations becomes a very challenging
task as manipulated regions are not visually apparent. This paper proposes a
high-confidence manipulation localization architecture which utilizes
resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder
network to segment out manipulated regions from non-manipulated ones.
Resampling features are used to capture artifacts like JPEG quality loss,
upsampling, downsampling, rotation, and shearing. The proposed network exploits
larger receptive fields (spatial maps) and frequency domain correlation to
analyze the discriminative characteristics between manipulated and
non-manipulated regions by incorporating encoder and LSTM network. Finally,
decoder network learns the mapping from low-resolution feature maps to
pixel-wise predictions for image tamper localization. With predicted mask
provided by final layer (softmax) of the proposed architecture, end-to-end
training is performed to learn the network parameters through back-propagation
using ground-truth masks. Furthermore, a large image splicing dataset is
introduced to guide the training process. The proposed method is capable of
localizing image manipulations at pixel level with high precision, which is
demonstrated through rigorous experimentation on three diverse datasets
Non-linear Pattern Matching with Backtracking for Non-free Data Types
Non-free data types are data types whose data have no canonical forms. For
example, multisets are non-free data types because the multiset has
two other equivalent but literally different forms and .
Pattern matching is known to provide a handy tool set to treat such data types.
Although many studies on pattern matching and implementations for practical
programming languages have been proposed so far, we observe that none of these
studies satisfy all the criteria of practical pattern matching, which are as
follows: i) efficiency of the backtracking algorithm for non-linear patterns,
ii) extensibility of matching process, and iii) polymorphism in patterns.
This paper aims to design a new pattern-matching-oriented programming
language that satisfies all the above three criteria. The proposed language
features clean Scheme-like syntax and efficient and extensible pattern matching
semantics. This programming language is especially useful for the processing of
complex non-free data types that not only include multisets and sets but also
graphs and symbolic mathematical expressions. We discuss the importance of our
criteria of practical pattern matching and how our language design naturally
arises from the criteria. The proposed language has been already implemented
and open-sourced as the Egison programming language
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