Topic-wise Grouping of
GVCL Publications
Consolidated List
Lab Theme: Human-Centric Spatio-Temporal Data Science
Topics involved:
[Back
to the publications page]
Geospatial Data:
J.
Sreevalsan-Nair,
A. Mundayatt,
B. Gnanaraj,
A. Thomas, N.
C. Kumar, G.
G. Sabhahit,
S. Joshi, and
T. K.
Srikanth,
“Mental
Healthcare in
the Times of
Climate Change
Action and
Data Science,”
in Data-Driven
Insights and
Analytics for
Measurable
Sustainable
Development
Goals,
Elsevier
(accepted),
2024.
V. Jaisankar
and J.
Sreevalsan-Nair,
“SuP-SLiP:
Subsampled
Processing of
Large-scale
Static LIDAR
Point Clouds,”
in Proceedings
of the 3rd ACM
SIGSPATIAL
International
Workshop on
Searching and
Mining Large
Collections of
Geospatial
Data
(GeoSearch
2024)
(accepted),
2024, pp. 1–8.
P. Nileshbhai
Butani, J.
Sreevalsan-Nair,
and N. Kamat,
“CMA: An
End-to-End
System for
Reverse
Engineering
Choropleth Map
Images,” IEEE
Geoscience and
Remote Sensing
Letters, vol.
21, pp. 1–5,
2024, to be
also presented
in the GRSL
Special Stream
at the 37th
Conference on
Graphics,
Patterns and
Images
(SIBGRAPI
2024). (doi)(URL)
A. Moreira, F.
Bovolo, A.
Plaza, and J.
Sreevalsan-Nair,
“44th IEEE
International
Geoscience and
Remote Sensing
Symposium -
IGARSS 2024,
Athens,
Greece, 7-12
July, 2024
Impressions of
the First
Days,” IEEE
Geoscience and
Remote Sensing
Magazine, vol.
12, no. 3, pp.
149–161, 2024.
(doi)
F. Bovolo, J.
Sreevalsan-Nair,
A. Plaza, H.
Yu, and A.
Moreira, “GRSS
Awards
Presented at
the IGARSS
2024 Banquet,”
IEEE
Geoscience and
Remote Sensing
Magazine, vol.
12, no. 3, pp.
161–170, 2024.
(doi)
J.
Sreevalsan-Nair,
A. Kiran, A.
Bhattacharya,
B. D. Sagar,
G. KN, U.
Verma, K.
Lanka, and S.
K. Meher,
“InGARSS 2023
in Bangalore:
Striking a
Balance,” IEEE
Geoscience and
Remote Sensing
Magazine, vol.
12, no. 3, pp.
180–187, 2024.
(doi)
J.
Sreevalsan-Nair and A.
Mundayatt, “Evolution of
Data-driven Single- and
Multi-Hazard Susceptibility
Mapping and Emergence of
Deep Learning Methods,” IOP
Conference Series: Earth and
Environmental Science
(accepted), April 2024.
A.
Mundayatt and J. Sreevalsan-Nair, “Scaling up Study Area
Size in Flood Susceptibility Mapping,” in Proceedings of
2024 IEEE International Geoscience and Remote Sensing
Symposium (IGARSS) (accepted), IEEE, 2024.
L. S. Liang, J. Sreevalsan-Nair, and B. S. D. Sagar,
“Multispectral Data Mining: A Focus on Remote Sensing
Satellite Images,” Wiley Interdisciplinary Reviews: Data
Mining and Knowledge Discovery, e1522, October 2023. (doi)(eprint)
L.-T. Tay and J. Sreevalsan-Nair,
“Disaster Susceptibility Analysis in Remote Sensing,” in Cognitive
Sensing Technologies and Applications, G. R. Sinha, B. Subudhi,
C.-P. Fan, and H. Nisar, Eds., Stevenage, UK: Institute of
Engineering and Technology (IET), 2023 (doi)(url).
J. Sreevalsan-Nair, Co-Association
Matrices in Ensemble Clustering:
Diverse Applications and Extensions,
Preprint available at SSRN, May
2023. (url)
S. Singh and J.
Sreevalsan-Nair, “Visual Exploration of LiDAR Point Clouds,” in
Advances in Scalable and Intelligent Geospatial Analytics:
Challenges and Applications, Chapter 12, K. Kurte, S. Durbha, J.
Sanyal, L. Yang, S. Chaudhari, U. Bhangale, and U. Bharambe,
Eds., Florida, USA: CRC Press, 2023, p. 19. (doi)
J.
Sreevalsan-Nair,
“Interpolation,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Eigenvalues
and
Eigenvectors,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Independent
Component
Analysis,” in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Laplace
Transform," in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Expectation-Maximization
Algorithm,” in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Simulated
Annealing,” in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“K-Medoids,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Fuzzy
C-Means
Clustering,"
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Proximity
Regression,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Normal
Distribution,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Virtual
Globe,” in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“K-Means
Clustering,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair, “K-Nearest
Neighbors,” in
Encyclopedia of
Mathematical
Geosciences,
Encyclopedia of
Earth Sciences
Series, B. S. Daya
Sagar, Q. Cheng, J.
McKinley, and F.
Agterberg, Eds.,
Cham: Springer
International
Publishing, 2022. (doi)
J.
Sreevalsan-Nair,
P. Mohapatra,
and S. Singh,
“IMGD:
Image-based
Multiscale
Global
Descriptors of
Airborne LIDAR
Point Clouds
Used for
Comparative
Analysis,” in
Smart Tools
and Apps in
Graphics (STAG
2021) -
Eurographics
Italian
Chapter
Conference, P.
Frosini, D.
Giorgi, S.
Melzi, and E.
Rodolá, Eds.,
The
Eurographics
Association,
October 2021,
pp 61--72,
ISBN:
978-3-03868-165-6.(doi)
R.
Thangavel and J.
Sreevalsan-Nair,
“CV4FEE: Flood
Extent Estimation
Using Consensus
Voting in Ensemble
of Methods for
Change Detection
in Sentinel-1 GRD
SAR Images,” in
7th Asia-Pacific
Conference on
Synthetic Aperture
Radar (APSAR
2021), IEEE, 2021,
pp. 1–6.
(doi)
S.
Singh, and J.
Sreevalsan-Nair,
“Adaptive
Multiscale
Feature
Extraction in
a Distributed
System for
Semantic
Classification
of Airborne
LiDAR Point
Clouds,” IEEE
Geoscience and
Remote Sensing
Letters, July
2021.
(doi)(preprint)
J.
Sreevalsan-Nair,
“Maximum
Likelihood,”
in
Encyclopedia
of
Mathematical
Geosciences,
Encyclopedia
of Earth
Sciences
Series, B. S.
Daya Sagar, Q.
Cheng, J.
McKinley, and
F. Agterberg,
Eds., Cham:
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“Minimum
Entropy
Deconvolution,”
in Earth
Sciences
Series,
Encyclopedia
of
Mathematical
Geosciences
(accepted), B.
S. D. Sagar,
Q. Cheng, J.
McKinley, and
F. Agterberg,
Eds., Springer
International
Publishing,
2022.
(doi)
J.
Sreevalsan-Nair, “Data
Visualization,”
in Earth
Sciences
Series,
Encyclopedia
of
Mathematical
Geosciences
(accepted), B.
S. D. Sagar,
Q. Cheng, J.
McKinley, and
F. Agterberg,
Eds., Springer
International
Publishing,
2022.
(doi)
J.
Sreevalsan-Nair,
“Multiscaling,”
in Earth
Sciences Series,
Encyclopedia of
Mathematical
Geosciences
(accepted), B.
S. D. Sagar, Q.
Cheng, J.
McKinley, and F.
Agterberg, Eds.,
Springer
International
Publishing,
2022. (doi)
J.
Sreevalsan-Nair,
“LiDAR,” in
Earth Sciences
Series,
Encyclopedia of
Mathematical
Geosciences
(accepted), B.
S. D. Sagar, Q.
Cheng, J.
McKinley, and F.
Agterberg, Eds.,
Springer
International
Publishing,
2022. (doi)
S.
Singh, and J.
Sreevalsan-Nair,
"A Distributed
System for
Optimal Scale
Feature
Extraction and
Semantic
Classification
of Airborne
LiDAR Point
Clouds," in
Distributed
Computing and
Internet
Technology,
Proceedings of
the 17th
International
Conference on
Distributed
Computing and
Internet
Technology (ICDCIT), January 2021, Sequence Number
18, Lecture
Notes in
Computer
Science,
Springer
International
Publishing. (doi)(preprint)
S.
Singh, and J.
Sreevalsan-Nair,
"A Distributed
System for
Multiscale
Feature
Extraction and
Semantic
Classification
of Airborne
LiDAR Point
Clouds,"
accepted at
the IEEE
International
India
Geoscience and
Remote Sensing
Symposium
(InGARSS)
2020, pp
74-77,
December 2020.
(doi)(pdf)
(Best Paper
Award)
J.
Sreevalsan-Nair, and P. Mohapatra, “Augmented Semantic Signatures
of Airborne
LiDAR Point
Clouds for
Comparison,”
in arXiv
(2020), May
2020, https://arxiv.org/abs/2005.02152
J.
Sreevalsan-Nair
and P.
Mohapatra,
“Influence of
Aleatoric
Uncertainty on
Semantic
Classification
of Air-borne
LiDAR Point
Clouds: A Case
Study with
Random Forest
Classifier
Using
Multiscale
Features”,
accepted in
the
Proceedings of
the 2020 IEEE
International
Geoscience and
Remote Sensing
Symposium
(IGARSS 2020),
pp 1066-1070,
September
2020. (doi)(pdf)
J.
Sreevalsan-Nair,
"Visual
Analytics of
3D Airborne
LiDAR Point
Clouds in
Urban
Regions," in
Sarda, N.,
Acharya, P.,
Sen, S. (eds)
Geospatial
Infrastructure,
Applications
and
Technologies:
India Case
Studies, pp
313-325,
Springer
Singapore,
November 2018.
(doi)(pdf)
J.
Sreevalsan-Nair,
A. Jindal, and
B. Kumari,
``Contour
Extraction in
Buildings in
Airborne LiDAR
Point Clouds
Using
Multi-scale
Local
Geometric
Descriptors
and Visual
Analytics,''
in IEEE
Journal of
Selected
Topics in
Applied Earth
Observations
and Remote
Sensing
(JSTARS),
11(7), pp
2320-2335,
June 2018. (doi)(pdf)
J.
Sreevalsan-Nair,
and A. Jindal,
``Using
Gradients and
Tensor Voting
in 3D Local
Geometric
Descriptors
for Feature
Detection in
Airborne LiDAR
Point Clouds
in Urban
Regions,'' in
the
Proceedings of
the 2017 IEEE
International
Geoscience and
Remote Sensing
Symposium
(IGARSS 2017),
July 2017. (doi)(pdf)
J.
Sreevalsan-Nair,
and B. Kumari,
``Local
Geometric
Descriptors
for
Multi-Scale
Probabilistic
Point
Classification
of Airborne
LiDAR Point
Clouds,'' in
``Modeling,
Analysis and
Visualization
of
Anisotropy,''
Mathematics
and
Visualization
Series,
Springer,
Cham, pp
175-200,
October 2017.
(from
Proceedings of
Dagstuhl
Seminar 16142)
(doi)(pdf)
B. Kumari, and
J.
Sreevalsan-Nair,
``An
interactive
visual
analytic tool
for semantic
classification
of 3D urban
LiDAR point
cloud,'' In
Proceedings of
the 23rd
SIGSPATIAL
International
Conference on
Advances in
Geographic
Information
Systems 2015
(p.
73:1--73:4),
ACM. (doi)(pdf)
B.
Kumari,
A.Ashe, and J.
Sreevalsan-Nair,
``Remote
Interactive
Visualization
of Parallel
Implementation
of Structural
Feature
Extraction of
Three-dimensional
Lidar Point
Cloud,'' in
the
Proceedings of
the Third
International
Conference on
Big Data
Analytics,
Lecture Notes
in Computer
Science (LNCS)
Series, Vol.
8883, 2014, pp
129-132,
Springer. (doi)(pdf)
[Back to
Consolidated List of Topics]
Population Data
J.
Sreevalsan-Nair,
A. Mundayatt,
B. Gnanaraj,
A. Thomas, N.
C. Kumar, G.
G. Sabhahit,
S. Joshi, and
T. K.
Srikanth,
“Mental
Healthcare in
the Times of
Climate Change
Action and
Data Science,”
in Data-Driven
Insights and
Analytics for
Measurable
Sustainable
Development
Goals,
Elsevier
(accepted),
2024.
S.
Mathai, P.
Krishnan, and
J. Sreevalsan
Nair,
“Understanding
Graphical
Literacy Using
School
Students’
Comprehension
Strategies,”
Contemporary
Education
Dialogue, pp.
1–35, 2024. (doi)
J. Sreevalsan-Nair, A. Mubayi, J. Chhabra, R. R.
Vangimalla, and P. R. Ghogale, “Evaluating Early
Pandemic Response through Length-of-Stay Analysis of
Case Logs and Epidemiological Modeling: A Case Study of
Singapore in Early 2020,” Computational and Mathematical
Biophysics, vol. 11, no. 1, p. 20 230 104, October 2023.
(doi)(open
access)
J. Sreevalsan-Nair, Co-Association
Matrices in Ensemble Clustering: Diverse Applications and
Extensions, Preprint available at SSRN, May 2023. (url)
H. Ravindra and J.
Sreevalsan-Nair, “A Methodology for Integrating Population
Health Surveys Using Spatial Statistics and Visualizations
for Cross-sectional Analysis,” SN Computer Science, vol.
4, no. 224, pp. 1–19, 2023. (full-text view) (doi)
J.
Sreevalsan-Nair
and A. Jakher,
“CAP-DSDN:
Node
Co-association
Prediction in
Communities in
Dynamic Sparse
Directed
Networks and a
Case Study of
Migration
Flow,” in
Proceedings of
the 14th
International
Conference on
Knowledge
Discovery and
Information
Retrieval,
INSTICC,
SciTePress,
2022, pp
63--74. ISBN :
978-989-758-614-9.
(doi).
(Best
Paper Award
Nomination)
H.
Ravindra and
J.
Sreevalsan-Nair,
Spatial and
Visual
Analytics for
Grouped
Analysis of
Population
Survey Data,
presented at
the doctoral
research
workshop at
the 26th
International
Conference on
Information
Visualization
IV2022, July
2022.
H.
Ravindra and
J.
Sreevalsan-Nair,
"Composition
of Geospatial
Visualizations
for
Scale-aware
Views of
Multiple
Outcome
Variables in
Population
Surveys," in
Proceedings of
the 26th
International
Conference on
Information
Visualization
IV2022, IEEE,
pp 432-441,
July 2022. (doi)
S.
Agarwal, F.
Beck, U.
Ghosh, and J.
Sreevalsan-Nair,
CiteVis:
Visual
Analysis of
Overlapping
Citation
Intents as
Dynamic Sets,
accepted for
poster
presentation
at the 15th
IEEE Pacific
Visualization
Symposium
(PacificVis)
2022, April
2022.
V. Sridhar, J.
Sreevalsan-Nair,
P. R. Ghogale,
and R. R.
Vangimalla,
“Sharing and
Use of
Non-Personal
Health
Information:
Case of the
COVID-19
Pandemic,” in
Data Centric
Living:
Algorithms,
Digitization
and
Regulation, V.
Sridhar, Ed.,
1st ed.,
Routledge
India, 2022,
ch. 8, ISBN :
9780367536534.
(doi)
R.
R. Vangimalla
and J.
Sreevalsan-Nair,
“Communities
and Cliques in
Functional
Brain Network
Using
Multiscale
Consensus
Approach,”
IEEE
Transactions
on Neural
Systems and
Rehabilitation
Engineering
(TNSRE), vol.
30, pp.
1951–1960,
2022 (doi)
R.
R. Vangimalla
and J.
Sreevalsan-Nair,
“HCNM:
Heterogenous
Correlation
Network Model
for
Multi-level
Integrative
Study of
Multi-omics
Data for
Cancer Subtype
Prediction,”
in Proceedings
of the 2021
43rd Annual
International
Conference of
the IEEE
Engineering in
Medicine &
Biology
Society
(EMBC), IEEE,
2021, pp.
1880–1886. (doi)
H.
Ravindra, and
J.
Sreevalsan-Nair,
"Integrating
Population
Surveys Using
Spatial Visual
Analytics: A
Case Study on
Nutrition and
Health
Indicators of
Children under
Five in
India," in the
Proceedings of
the 7th
International
Conference on
Geographical
Information
Systems
Theory,
Applications,
and Management
(GISTAM
2021), pp
203--213,
April 2021,
SCITEPRESS. (doi)(preprint)
A. Jakher, and J. Sreevalsan-Nair, “Community Detection
in Migration Flow Networks,” accepted for oral
presentation at the Urban Complex Sysems 2020, a
satellite event at the annual Conference on Complex
Systems 2020 (CCS 2020), December 9-10, 2020.
J.
Sreevalsan-Nair,
R. R.
Vangimalla,
and P. R.
Ghogale, "Influence
of COVID-19
Transmission
Stages and
Demographics
on Length of
In-Hospital
Stay in
Singapore for
the First 1000
Patients,"
accepted as
poster in
COVID-19 COSI
at the 28th
Conference on
Intelligent
Systems for
Molecular
Biology
(ISMB), July
2020. (abstract)(doi)
J. Sreevalsan-Nair, R. R. Vangimalla, and P.
R. Ghogale, “Estimation of Length of
In-Hospital Stay Using Demographic Data of the First 1000
COVID-19 Patients in Singapore,” medRxiv (2020), April 2020. (doi)
R.
R. Vangimalla, and J. Sreevalsan-Nair, “Comparing Community
Detection Methods in Brain Functional Connectivity Networks,” in
Balusamy S., Dudin A.N., Graña M., Mohideen A.K., Sreelaja N.K.,
Malar B. (eds) Computational Intelligence, Cyber Security and
Computational Models. Models and Techniques for Intelligent
Systems and Automation. ICC3 2019. Communications in Computer
and Information Science, vol 1213. Springer, Singapore, October
2020. (doi);
preprint at bioRxiv (2020), February 2020.(doi)
U. M. Mehta, D. Shadakshari, P. Vani, S. S.
Naik, Kiran Raj V., R. R. Vangimalla, Y. C. J. Reddy, J.
Sreevalsan-Nair, and R. D. Bharath, "Case Report: Obsessive
compulsive disorder in posterior cerebellar infarction -
illustrating clinical and functional connectivity modulation
using MRI-informed transcranial magnetic stimulation,"
Wellcome Open Research 2020, 5:189. (doi)
R.
R. Vangimalla, and J. Sreevalsan-Nair, "Construction
and
Visualization
of Diseasome
of Lung
Diseases
Associated
with COVID-19
from
Co-association
Networks of
Multi-omics
Data,"
accepted as
poster in
NetBio COSI at
the 28th
Conference on
Intelligent
Systems for
Molecular
Biology
(ISMB), July
2020. (abstract)(doi)
R. R. Vangimalla, and J. Sreevalsan-Nair,
“A Multiscale Consensus Method Using Factor Analysis to Extract
Modular Regions in the Functional Brain Network,” in the
Proceedings of the 42nd Annual Conferences of the IEEE
Engineering in Medicine and Biology Society, pp 2824-2828, July
2020. (pdf
with correction in axis labels in Fig 2(i))(doi)
R. R. Vangimalla, and J.
Sreevalsan-Nair, “Consensus Methods for Network Analysis of
Biomedical Data: Case Studies on Brain Functional Connectivity
Network and Gene-Gene Association Networks,” presented at the
doctoral colloquium at the 4th International Conference on
Computational Intelligence and Networks (CINE 2020), February
2020. (pdf)(researchgate)
R. R. Vangimalla, and J.
Sreevalsan-Nair, "RadTrix: A Composite Hybrid Visualization
for Unbalanced Bipartite Graphs in Biological Datasets,"
accepted as poster in the 9th Eurographics Workshop on Visual
Computing for Biology and Medicine, September 2019. (conference-proceedings)(pdf)(poster-pdf)
J. Sreevalsan-Nair, N. Murthy, S. Agarwal,
R. R. Vangimalla, and S. Ramesh, ``Collaborative Design of
Visual Analytics Techniques for Survey Data for Community-based
Research in Public Health,'' (accepted as poster) in the 8th
Workshop on Visual Analytics in Healthcare, affiliated with IEEE
VIS 2017. (pdf)
J. Sreevalsan-Nair, and S. Agarwal,
``NodeTrix-CommunityHierarchy: Techniques for Finding
Hierarchical Communities for Visual Analytics of Small-world
Networks,'' in the Proceedings of 12th International Joint
Conference on Computer Vision, Imaging, and Computer Graphics
Theory and Applications (VISIGRAPP 2017), vol 3: IVAPP, pp
140-151, SCITEPRESS, 2017. (doi)(pdf)
(Best Paper Award Nomination)
S. Agarwal, A. Tomar, and J.
Sreevalsan-Nair, ``NodeTrix-Multiplex: Visual Analytics of
Multiplex Small World Networks,'' in Complex Networks &
Their Applications V, Studies in Computational Intelligence,
vol. 693, pp 579-591, Springer International Publishing, 2017. (doi)(pdf)
J. Sreevalsan-Nair, ``A Survey of
Requirements of Multivariate Data and its Visualizations for
Analysis of Child Malnutrition in India,'' Data Science
Communications, vol. 1, IIITB Press, 1--26, October 2016. (pdf)
S. Parveen, and J. Sreevalsan-Nair,
``Visualization of Small World Networks Using Similarity
Matrices,'' in the Proceedings of the Second International
Conference on Big Data Analytics, Lecture Notes in Computer
Science, Volume 8302, 2013, pp 151-170, Springer. (doi)(pdf)
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Topics]
Urban Scenario Data:
V.
Jaisankar and
J.
Sreevalsan-Nair,
“SuP-SLiP:
Subsampled
Processing of
Large-scale
Static LIDAR
Point Clouds,”
in Proceedings
of the 3rd ACM
SIGSPATIAL
International
Workshop on
Searching and
Mining Large
Collections of
Geospatial
Data
(GeoSearch
2024)
(accepted),
2024, pp. 1–8.
D. Katkoria,
J.
Sreevalsan-Nair,
M. Sati, and
S.
Karunakaran,
“WBF-ODAL:
Weighted Boxes
Fusion for 3D
Object
Detection from
Automotive
LiDAR Point
Clouds,” in
Proceedings of
IEEE
International
Conference on
Vehicular
Technology and
Transportation
System
(ICVTTS) 2024
(to appear in
IEEExplore),
IEEE, 2024, Best
Paper Award.
D.
Katkoria, J.
Sreevalsan-Nair,
M. Sati, and
S.
Karunakaran,
“ME-ODAL:
Mixture-of-Experts
Ensemble of
CNN Models for
3D Object
Detection from
Automotive
LiDAR Point
Clouds,”in
Deep Learning
Theory and
Applications,
5th
International
Conference
DeLTA 2024,
Dijon, France,
July 10-11,
2024,
Proceedings,
Part II, CCIS,
vol. 2172,
Springer Cham,
2024, pp.
279–300. (doi)(URL)
D.
Katkoria and J. Sreevalsan-Nair, “Evaluating and Improving
RoSELS for Road Surface Extraction from 3D Automotive LiDAR
Point Cloud Sequences,” in Deep Learning Theory and
Applications: Revised Selected Papers from Third International
Conference DeLTA 2022, Portugal, Chapter 6, CCIS volume 1858,
Springer Cham, 2023. (doi)
(book-link)
D. Katkoria and J. Sreevalsan-Nair, “RoSELS: Road Surface
Extraction for 3D Automotive LiDAR Point Cloud Sequence,” in
Proceedings of the 3rd International Conference on Deep Learning
Theory and Applications (DeLTA), INSTICC, SciTePress,
2022, pp 55–67. ISBN : 978-989-758-584-5. (doi). (Best Paper Award Nomination)
A. C. Victor and J. Sreevalsan-Nair,
“Building 3D Virtual Worlds from Monocular Images of
Urban Road Traffic Scenes,” in
International Symposium on Visual Computing (ISVC
2021), Part II, Lecture Notes in Computer Science LNCS
13018, Bebis, George et al., Springer Nature
Switzerland AG, pp 1-14, 2021. (doi)(Easychair
preprint)
A. C. Victor and J.
Sreevalsan-Nair, "Scene Editing Using Synthesis of
Three-Dimensional Virtual Worlds From Monocular
Images of Urban Road Traffic Scenes", accepted for
spotlight session and poster at the ACM SIGGRAPH
European Conference on Visual Media Production
(CVMP) on December 2019.(pdf)(conference-proceedings)
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Consolidated List of Topics]
HCI Data
P. Rastogi, K. Singh, and
J. Sreevalsan-Nair,
“SunburstChartAnalyzer: Hierarchical Data
Retrieval from Images of Sunburst Charts
for Tree Visualization,” in Computer
Graphics & Visual Computing (CGVC), P.
Vangorp and D. Hunter, Eds., The
Eurographics Association, 2023, pp.
97–101, ISBN: 978-3-03868-231-8. (doi)
B. Gnanaraj and J. Sreevalsan-Nair,
“EyeExplore: An Interactive Visualization Tool for
Eye-Tracking Data for Novel Stimulus-Based
Analysis,” in Proceedings of the 2023 Symposium on
Eye Tracking Research and Applications, ser. ETRA
’23, Tubingen, Germany: ACM, 2023. (doi)
J. Sreevalsan-Nair, “On
Metavisualization and Properties of Visualization,” in
Proceedings of the 18th International Joint Conference
on Computer Vision, Imaging and Computer Graphics
Theory and Applications - Vol 3, IVAPP, INSTICC,
SciTePress, 2023, pp. 230–239, ISBN:
978-989-758-634-7. (doi)
S.
C. Daggubati,
J.
Sreevalsan-Nair,
and
K. Dadhich,
“BarChartAnalyzer:
Data
Extraction and
Summarization
of Bar Charts
from Images,”
SN Computer
Science,
3(500), 1–19,
2022. (full-text
view) (doi)
S.
C. Daggubati
and J.
Sreevalsan-Nair,
“ACCirO: A
System for
Analyzing and
Digitizing
Images of
Charts with
Circular
Objects,” in
Proceedings of
the 22 nd
International
Conference,
Part III,
chapter 50,
Cham: Springer
International
Publishing,
2022, pp.
605–612. (doi)
J.
Sreevalsan-Nair,
K.
Dadhich, and
S. C.
Daggubati,
"Tensor Fields
for Data
Extraction
from Chart
Images: Bar
Charts and
Scatter
Plots," in
Topological
Methods in
Data Analysis
and
Visualization
VI, Ingrid
Hotz, Talha
Bin Masood,
Filip Sadlo,
and Julien
Tierny (Eds.).
Springer,
Cham, 2021, pp
219-241. (doi).
Preprint
at arXiv
(2020),
October 2020,
https://arxiv.org/abs/2010.02319
K. Dadhich, S.
C. Daggubati,
and J.
Sreevalsan-Nair,
“ScatterPlotAnalyzer:
Digitizing
Images of
Charts Using
Tensor-based
Computational
Model,” in
International
Conference on
Computational
Science,
Computational
Science --
ICCS 2021,
Part V,
Lecture Notes
in Computer
Science,
volume 12746,
M. Paszynski,
D.
Kranzlmüller,
V. V.
Krzhizhanovskaya,
and P. M.
Dongarra Jack
J. and Sloot,
Eds., Cham:
Springer
International
Publishing,
2021, pp.
70–83, ISBN :
978-3-030-77977-1.
(doi)(preprint)
K. Dadhich, S.
C. Daggubati,
and J.
Sreevalsan-Nair,
"BarChartAnalyzer:
Digitizing
Images of Bar
Charts," in
the
Proceedings of
the
International
Conference on
Image
Processing and
Vision
Engineering (IMPROVE 2021), pp 17--28,
April 2021,
SCITEPRESS. (doi)(preprint)
(Best
Paper
Award
Nomination)
K. Lukose, S. Agarwal, V. N. Rao, and J.
Sreevalsan-Nair, ``Design Study for Creating Pathfinder: A
Visualization Tool for Generating Software Test Plans Using
Model Based Testing,'' in the Proceedings of the 13th
International Joint Conference on Computer Vision, Imaging, and
Computer Graphics Theory and Applications (VISIGRAPP 2018), vol
3: IVAPP, SCITEPRESS, pp. 289-300, 2018. (doi)(pdf)
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to Consolidated List of Topics]