1,643 research outputs found
Image Enhancement with Statistical Estimation
Contrast enhancement is an important area of research for the image analysis.
Over the decade, the researcher worked on this domain to develop an efficient
and adequate algorithm. The proposed method will enhance the contrast of image
using Binarization method with the help of Maximum Likelihood Estimation (MLE).
The paper aims to enhance the image contrast of bimodal and multi-modal images.
The proposed methodology use to collect mathematical information retrieves from
the image. In this paper, we are using binarization method that generates the
desired histogram by separating image nodes. It generates the enhanced image
using histogram specification with binarization method. The proposed method has
showed an improvement in the image contrast enhancement compare with the other
image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print
Locally Adaptive Block Thresholding Method with Continuity Constraint
We present an algorithm that enables one to perform locally adaptive block
thresholding, while maintaining image continuity. Images are divided into
sub-images based some standard image attributes and thresholding technique is
employed over the sub-images. The present algorithm makes use of the thresholds
of neighboring sub-images to calculate a range of values. The image continuity
is taken care by choosing the threshold of the sub-image under consideration to
lie within the above range. After examining the average range values for
various sub-image sizes of a variety of images, it was found that the range of
acceptable threshold values is substantially high, justifying our assumption of
exploiting the freedom of range for bringing out local details.Comment: 12 Pages, 4 figures, 1 Tabl
Ternary Weight Networks
We introduce ternary weight networks (TWNs) - neural networks with weights
constrained to +1, 0 and -1. The Euclidian distance between full (float or
double) precision weights and the ternary weights along with a scaling factor
is minimized. Besides, a threshold-based ternary function is optimized to get
an approximated solution which can be fast and easily computed. TWNs have
stronger expressive abilities than the recently proposed binary precision
counterparts and are thus more effective than the latter. Meanwhile, TWNs
achieve up to 16 or 32 model compression rate and need fewer
multiplications compared with the full precision counterparts. Benchmarks on
MNIST, CIFAR-10, and large scale ImageNet datasets show that the performance of
TWNs is only slightly worse than the full precision counterparts but
outperforms the analogous binary precision counterparts a lot.Comment: 5 pages, 3 fitures, conferenc
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