8,956 research outputs found
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Filling polygonal holes with bicubic patches
Consider a bicubic rectangular patch complex which surrounds an n-sided hole in R3. Then the problem of filling the hole with n bicubic rectangular patches is studied
Single Image Super-Resolution using Back-Propagation Neural Networks
There are several existing mathematical algorithms for colour image upscaling like Nearest Neighbour, Bicubic and Bilinear. This paper firstly investigates the performances of these three and it has been found that Bicubic performs the best in terms of structural similarity and execution time. A Bicubic with backpropagation based ANN method has been proposed to improve the results. Bicubic with ANN shows 6.5% higher SSIM, 6.9% higher PSNR, 8.7% higher SNR and 30.23% lower MSE values than pure Bicubic. The results of Bicubic with ANN are also compared with state of the art super-resolution techniques like SRCNN. Bicubic with ANN produces 1.48% higher SSIM and 3.44% higher PSNR than SRCNN
SLIC Based Digital Image Enlargement
Low resolution image enhancement is a classical computer vision problem.
Selecting the best method to reconstruct an image to a higher resolution with
the limited data available in the low-resolution image is quite a challenge. A
major drawback from the existing enlargement techniques is the introduction of
color bleeding while interpolating pixels over the edges that separate distinct
colors in an image. The color bleeding causes to accentuate the edges with new
colors as a result of blending multiple colors over adjacent regions. This
paper proposes a novel approach to mitigate the color bleeding by segmenting
the homogeneous color regions of the image using Simple Linear Iterative
Clustering (SLIC) and applying a higher order interpolation technique
separately on the isolated segments. The interpolation at the boundaries of
each of the isolated segments is handled by using a morphological operation.
The approach is evaluated by comparing against several frequently used image
enlargement methods such as bilinear and bicubic interpolation by means of Peak
Signal-to-Noise-Ratio (PSNR) value. The results obtained exhibit that the
proposed method outperforms the baseline methods by means of PSNR and also
mitigates the color bleeding at the edges which improves the overall
appearance.Comment: 6 page
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Smooth parametric surfaces and n-sided patches
The theory of 'geometric continuity' within the subject of CAGD is reviewed. In particular, we are concerned with how parametric surface patches for CAGD can be pieced together to form a smooth Ck surface. The theory is applied to the problem of filling an n-sided hole occurring within a smooth rectangular patch complex. A number of solutions to this problem are surveyed
Two-Stream Action Recognition-Oriented Video Super-Resolution
We study the video super-resolution (SR) problem for facilitating video
analytics tasks, e.g. action recognition, instead of for visual quality. The
popular action recognition methods based on convolutional networks, exemplified
by two-stream networks, are not directly applicable on video of low spatial
resolution. This can be remedied by performing video SR prior to recognition,
which motivates us to improve the SR procedure for recognition accuracy.
Tailored for two-stream action recognition networks, we propose two video SR
methods for the spatial and temporal streams respectively. On the one hand, we
observe that regions with action are more important to recognition, and we
propose an optical-flow guided weighted mean-squared-error loss for our
spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving
objects. On the other hand, we observe that existing video SR methods incur
temporal discontinuity between frames, which also worsens the recognition
accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR)
training that emphasizes the temporal continuity between consecutive frames. We
perform experiments using two state-of-the-art action recognition networks and
two well-known datasets--UCF101 and HMDB51. Results demonstrate the
effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.Comment: Accepted to ICCV 2019. Code:
https://github.com/AlanZhang1995/TwoStreamS
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild
Majority of the face recognition algorithms use query faces captured from
uncontrolled, in the wild, environment. Often caused by the cameras limited
capabilities, it is common for these captured facial images to be blurred or
low resolution. Super resolution algorithms are therefore crucial in improving
the resolution of such images especially when the image size is small requiring
enlargement. This paper aims to demonstrate the effect of one of the
state-of-the-art algorithms in the field of image super resolution. To
demonstrate the functionality of the algorithm, various before and after 3D
face alignment cases are provided using the images from the Labeled Faces in
the Wild (lfw). Resulting images are subject to testing on a closed set face
recognition protocol using unsupervised algorithms with high dimension
extracted features. The inclusion of super resolution algorithm resulted in
significant improved recognition rate over recently reported results obtained
from unsupervised algorithms
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