Information Resources on Computer Science
http://dr.lib.sjp.ac.lk/handle/123456789/164
2024-01-28T20:17:24ZDeep learned compact binary descriptor with a lightweight network-in-network architecture for visual description
http://dr.lib.sjp.ac.lk/handle/123456789/9025
Deep learned compact binary descriptor with a lightweight network-in-network architecture for visual description
Bandara, R; Ranathunga, L; Abdullah, N.A
Binary descriptors have been widely used for real-time image retrieval and correspondence matching. However, most of the
learned descriptors are obtained using a large deep neural network (DNN) with several million parameters, and the learned
binary codes are generally not invariant to many geometrical variances which is crucial for accurate correspondence matching.
To address this problem, we proposed a new learning approach using a lightweight DNN architecture via a slack of multiple
multilayer perceptions based on the network in network (N1N) architecture, and a restricted Boltzmann machine (RBM). The
latter is used for mapping the features to binary codes, and carry out the geometrically invariant correspondence matching
task. Our experimental results on several benchmark datasets (e.g., Brown, Oxford, Paris, INRIA Holidays, RomcPatchcs,
IIPatches, and CIFAR-10) show that the proposed approach produces the learned binary descriptor that outperforms other
baseline self-su per vised binary descriptors in terms of correspondence matching despite the smaller size of its DNN. Most
importantly, the proposed approach does not freeze the features that are obtained while pre-training the N1N model. Instead, it
line tunes the features while learning the features needed for binary mapping through the RBM. Additionally, its lightweight
architecture makes it suitable for resource-constrained devices.
2020-01-01T00:00:00ZSolving systems of nonlinear equations using a modified firefly algorithm (MODFA)
http://dr.lib.sjp.ac.lk/handle/123456789/8406
Solving systems of nonlinear equations using a modified firefly algorithm (MODFA)
Ariyaratne, M.K.A.; Fernando, T.G.I.; Weerakoon, S
2019-01-01T00:00:00ZAnimation of Fingerspelled Words and Number Signs of the Sinhala Sign Language
http://dr.lib.sjp.ac.lk/handle/123456789/6975
Animation of Fingerspelled Words and Number Signs of the Sinhala Sign Language
Meegama, R.G.N.; Punchimudiyanse, M.
Attached; Sign language is the primary communication medium of the aurally handicapped community. Often, a sign gesture is mapped to a word or a phrase in
a spoken language and named as a conversational sign. A fingerspelling sign is a special sign derived to show a single character that matches a
character in the alphabet of a given language. This enables the deaf community to express words that do not have a conversational sign, such as a
name, using a letter-bv-letter technique. Sinhala Sign Language (SSL) uses a phonetic pronunciation mechanism to decode such words due to the
presence of one or more modifiers after a consonant. Expressing numbers also have a similar notation, and it is broken down into parts before
interpretation in sign gestures.
This article presents the variations implemented to make the 3D avatar-based interpreter system look similar to an actual fingerspelled SSL by a
human interpreter. To accomplish the task, a phonetic English-based 3D avatar animation system is developed with Blender animation software. The
conversion of Sinhala Unicode text to phonetic English and numbers written in digits to sign gestures is done with a Visual Basic.NET (VB.NET)
application. The presented application has 61 SSL fingerspelling signs and 40 SSL number signs. It is capable of interpreting any word written using
the modern Sinhala alphabet without conversational signs and interprets the numbers that go up to the billions. This is a helpful tool in teaching SSL
fingerspelling and number signs of SSL to deaf children.
2017-09-01T00:00:00ZWeerakoon-Fernando Method with accelerated third-order convergence for systems of nonlinear equations
http://dr.lib.sjp.ac.lk/handle/123456789/6957
Weerakoon-Fernando Method with accelerated third-order convergence for systems of nonlinear equations
Nishani, H.P.S.; Weerakoon, S.; Fernando, T.G.I.; Liyanage, M.
Attached; Weerakoon-Fernando Method (WFM) is a widely accepted third order iterative method introduced
in the late 1990s to solve nonlinear equations. Even though it has become so popular among numerical
analysts resulting in hundreds of similar work for single variable case, after nearly two decades, nobody took
the challenge of extending the method to multivariable systems. In this paper, we extend the WFM to
functions of several variables and provide a rigorous proof for the third order convergence. This theory was
supported by computational results using several systems of nonlinear equations. Computational algorithms
were implemented using MATLAB. We further analyse the method mathematically and demonstrate the
reason for the strong performance of WFM computationally, despite it requiring more function evaluations.
2018-01-01T00:00:00Z