739 research outputs found
Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents
Document Image Analysis, like any Digital Image Analysis requires
identification and extraction of proper features, which are generally extracted
from uncompressed images, though in reality images are made available in
compressed form for the reasons such as transmission and storage efficiency.
However, this implies that the compressed image should be decompressed, which
indents additional computing resources. This limitation induces the motivation
to research in extracting features directly from the compressed image. In this
research, we propose to extract essential features such as projection profile,
run-histogram and entropy for text document analysis directly from run-length
compressed text-documents. The experimentation illustrates that features are
extracted directly from the compressed image without going through the stage of
decompression, because of which the computing time is reduced. The feature
values so extracted are exactly identical to those extracted from uncompressed
images.Comment: Published by IEEE in Proceedings of ACPR-2013. arXiv admin note: text
overlap with arXiv:1403.778
GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures
Chi-square histogram distance is one of the distance measures that can be
used to find dissimilarity between two histograms. Motivated by the fact that
texture discrimination by human vision system is based on second-order
statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM)
that is based on second-order statistics and propose a new machine vision
algorithm for automatic defect detection on patterned textures. Input defective
images are split into several periodic blocks and GLCMs are computed after
quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact
and to reduce computation time. Dissimilarity matrix derived from chi-square
distances of the GLCMs is subjected to hierarchical clustering to automatically
identify defective and defect-free blocks. Effectiveness of the proposed method
is demonstrated through experiments on defective real-fabric images of 2 major
wallpaper groups (pmm and p4m groups).Comment: IJCVR, Vol. 2, No. 4, 2011, pp. 302-31
Supplier Selection Model using Game Theoretical Approach
The purchasing function has gained importance in supplier selection of procurement. As the evaluation of the supplier depends on various non-price attributes, formulating the strategy is very important .Every supplier tries to play tactical game in order to win the contract under uncertain situations. In our paper we propose a model through case study to select best supplier using game theoretical approach by applying simplex algorithm
An experimental and analytical investigation of isolated rotor flap-lag stability in forward flight
For flap-lag stability of isolated rotors, experimental and analytical investigations are conducted in hover and forward flight on the adequacy of a linear quasisteady aerodynamics theory with dynamic inflow. Forward flight effects on lag regressing mode are emphasized. A soft inplane hingeless rotor with three blades is tested at advance ratios as high as 0.55 and at shaft angles as high as 20 degrees. In combination with lag natural frequencies, collective pitch settings and flap-lag coupling parameters, the data base comprises nearly 1200 test points (damping and frequency) in forward flight and 200 test points in hover. By computerized symbolic manipulations, an analytic model is developed in substall to predict stability margins with mode identification. It also predicts substall and stall regions to help explain the correlation between theory and data
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields
Neural radiance fields (NeRF) have achieved impressive performances in view
synthesis by encoding neural representations of a scene. However, NeRFs require
hundreds of images per scene to synthesize photo-realistic novel views.
Training them on sparse input views leads to overfitting and incorrect scene
depth estimation resulting in artifacts in the rendered novel views. Sparse
input NeRFs were recently regularized by providing dense depth estimated from
pre-trained networks as supervision, to achieve improved performance over
sparse depth constraints. However, we find that such depth priors may be
inaccurate due to generalization issues. Instead, we hypothesize that the
visibility of pixels in different input views can be more reliably estimated to
provide dense supervision. In this regard, we compute a visibility prior
through the use of plane sweep volumes, which does not require any
pre-training. By regularizing the NeRF training with the visibility prior, we
successfully train the NeRF with few input views. We reformulate the NeRF to
also directly output the visibility of a 3D point from a given viewpoint to
reduce the training time with the visibility constraint. On multiple datasets,
our model outperforms the competing sparse input NeRF models including those
that use learned priors. The source code for our model can be found on our
project page:
https://nagabhushansn95.github.io/publications/2023/ViP-NeRF.html.Comment: SIGGRAPH 202
- …