scholarly journals AN ADAPTIVE IMAGE SCALING ALGORITHM BASED ON CONTINUOUS FRACTION INTERPOLATION AND MULTI-RESOLUTION HIERARCHY PROCESSING

Fractals ◽  
2020 ◽  
Vol 28 (08) ◽  
pp. 2040016
Author(s):  
JIANGANG JIN
Keyword(s):  
Partial Information ◽  
Weighted Average ◽  
Local Area ◽  
Smooth Transition ◽  
Original Image ◽  
Target Image ◽  
Hierarchical Processing ◽  
Continuous Fraction ◽  
Image Scaling ◽  
Low Pass

Traditional interpolation algorithms often blur the edges of the target image due to low-pass filtering effects, making it difficult to obtain satisfactory visual effects. Especially when the reduction ratio becomes small, the phenomenon of jagged edges and partial information loss will occur. In order to obtain better image scaling quality, an adaptive image scaling algorithm based on continuous fraction interpolation and multi-resolution hierarchical processing is proposed. In order to overcome the noise problem of the original image, this paper first performs wavelet decomposition on the original image to obtain multiple images with different resolutions. Secondly, in order to eliminate the influence of local area variance on the overall image, weighted average is performed on images of different resolutions. Then, in order to overcome the blurring effect of the weighted average image, by calculating the variance of the three groups of pixels around the target pixel, selecting a group of pixels with the smallest variance and using the Salzer continuous fraction interpolation equation to obtain the gray value of the target pixel. Finally, the multiple corrected images are stitched together into a scaled image. The algorithm in this paper achieves a high-order smooth transition between pixels in the same feature area, and can also adaptively modify the pixels of the image. The experimental results show that the edge of the target image obtained by the algorithm in this paper is clear, and the algorithm complexity is low, which is convenient for hardware implementation and can realize real-time image scaling.

Improved Watershed Algorithm Based on Morphology and Distance Transform

2013 ◽  
Vol 333-335 ◽  
pp. 1071-1075 ◽  
Author(s):  
Peng He ◽  
Kang Ling Fang ◽  
Xin Hai Liu
Keyword(s):  
Mathematical Morphology ◽  
Local Maximum ◽  
Face Image ◽  
Watershed Algorithm ◽  
Distance Transform ◽  
Original Image ◽  
Target Image ◽  
Gradient Operator ◽  
Gray Image ◽  
Circular Structure

In this paper we proposed an improved watershed algorithm for the quasi-circle overlapping images of the bars end face. According to the classical watershed algorithm, which often causes over-segmentation, the improved algorithm does a series of pretreatment with the original image, such as sobel filter. With the gradient operator and mathematical morphology method, we firstly obtain the smooth image of the forced local maximum marks. Then, on the basis of the quasi-circle characteristic of the target image, we proceed to maximize the erosion with circular structure in order to prevent under-segmentation. Finally, we use the watershed algorithm to segment the gray image based on distance transform. So we can separate the target from each other to achieve the accurate counting purpose. By using the proposed algorithm in the article, we obtain satisfactory segmentation results of the quasi-circle overlapping image of the bars end face image.

scholarly journals Significant Secret Image Sharing Scheme Based on Boolean Operation

2017 ◽  
Vol 17 (2) ◽  
pp. 134-150
Author(s):  
Monu Verma ◽  
Rajneesh Rani
Keyword(s):  
Fault Tolerance ◽  
Partial Information ◽  
Vital Role ◽  
Boolean Operation ◽  
Original Image ◽  
Secret Image Sharing ◽  
Secret Image ◽  
Image Sharing ◽  
Sharing Scheme ◽  
Secret Image Sharing Scheme

Abstract Traditionally, (k, n) secret image sharing is an approach of breaking down a secret image into n number of shadow images to assign them to n number of users, so that any k or more then k users can bring back the secret image. But in case of less than k, users cannot reveal any partial information about the original image. We have proposed a significant secret image sharing technique based on XOR with arithmetic operations that upgrade the performance of traditional secret image sharing approaches by serving importance to shadow images according to user’s significance. This scheme also conserves the fault tolerance property which plays a vital role in image sharing field.

Hybrid Low Pass and De-Trending Filter for Robust Position Estimation of Quadcopters

2016 ◽  
Author(s):  
Shatadal Mishra ◽  
Wenlong Zhang
Keyword(s):  
Optical Flow ◽  
Weighted Average ◽  
Position Estimation ◽  
High Frequency Oscillation ◽  
Flight Tests ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Least Squares Fit ◽  
Low Pass ◽  
Velocity Information

In this paper, a hybrid low-pass and de-trending (HLPD) filtering technique is proposed to achieve robust position estimates using an optical flow based sensor which calculates velocity information at a rate of 400 Hz. In order to filter out the high-frequency oscillation in the velocity information, a standard low-pass filter is implemented. The low-pass filter successfully eliminates sudden jumps and missing data-points, which prevents unprecedented maneuvers and mid-air crashes. The integrated position estimate has the accumulated drift which occurs due to electrical signal and temperature fluctuations together with other environmental factors which affect the data acquisition from the optical flow sensor. A recursive linear least squares fit is performed for the drift model and de-trending is applied to the integrated position signal. The performance of the proposed estimator is validated by comparing with model-identification based weighted average (MI-WA) position estimator, which is commonly used in quadcopters for position estimation. Simulation and experimental flight tests are conducted and the results show that the flight performance of HLPD filter is better than the extensively used MI-WA position filter in hover and square pattern flight tests.

scholarly journals Statistical Downscaling Using Localized Constructed Analogs (LOCA)*

2014 ◽  
Vol 15 (6) ◽  
pp. 2558-2585 ◽  
Author(s):  
David W. Pierce ◽  
Daniel R. Cayan ◽  
Bridget L. Thrasher
Keyword(s):  
Climate Model ◽  
Spatial Coherence ◽  
Weighted Average ◽  
Grid Cell ◽  
Local Area ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
A New Technique ◽  
The One

Abstract A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

scholarly journals A CONTOURLET DOMAIN WATERMARKING ALGORITHM

2013 ◽  
pp. 254-257
Author(s):  
N.R. BRINTA ◽  
P.R. BIPIN
Keyword(s):  
Digital Images ◽  
Jpeg Compression ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Blind Watermarking ◽  
Watermark Image ◽  
Original Image ◽  
Image Watermark ◽  
Low Pass ◽  
Watermark Algorithm

This paper presents a blind watermarking algorithm for digital images based on contourlet transform. After Contourlet transform, original image is decomposed into a series of multiscale, local and directional sub images. Each blocks of Arnold transformed watermark image, is embedded into suitable blocks of low pass coefficients of the contourlet transformed original image. Watermark is embedded using module arithmetic and odd-even quantization. The retrieving watermark algorithm is a blind detecting process, and it does not need original image. The experimental results show that the proposed watermarking algorithm is able to resist attacks, such as JPEG compression, noising, cropping and other attacks, and the watermarking is invisible and robust.

scholarly journals Estimating the Benefits of Regionalizing Emergency Medical Service Provision

1985 ◽  
Vol 14 (2) ◽  
pp. 144-153
Author(s):  
James M. Wilson ◽  
Daniel J. Dudek
Keyword(s):  
Response Time ◽  
Emergency Medical Service ◽  
Service Provision ◽  
Medical Service ◽  
Programming Model ◽  
Weighted Average ◽  
Local Area ◽  
Mixed Integer ◽  
Emergency Medical ◽  
Time Changes

Local area governments have experienced increasingly stringent budget constraints in recent years. Innovations in service delivery provide one avenue for increasing the effectiveness of resource allocations. This paper explores the potential savings available from regionalizing emergency medical service provision. A mixed integer programming model incorporating peak demand considerations is used to minimize service cost given a desired maximum response time. Changes in the weighted average response time measure the quality degradation required to attain the savings from cooperative provision. The results indicate that the benefits are substantial but that distribution of these gains is a possible barrier to implementation.

Segmentation and Feature Extraction of Panoramic Dental X-Ray Images

2012 ◽  
pp. 306-320
Author(s):  
Pedro H.M. Lira ◽  
Gilson A. Giraldi ◽  
Luiz A. P. Neves
Keyword(s):  
Feature Extraction ◽  
Data Extraction ◽  
Principal Component ◽  
Original Image ◽  
Radiographic Images ◽  
Shape Model ◽  
X Ray ◽  
Quadtree Decomposition ◽  
Segmentation Approach ◽  
Low Pass

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generate a mask. Then, the original image is processed by Otsu’s thresholding. The result is improved by morphological operators and the quadtree mask is applied to address overlapping, a common problem in X-ray images. The obtained regions are searched and the larger ones are selected to find tooth candidates. The boundary of the obtained regions are extracted and aligned with the shape model in order to recognize the target tooth (molar). The selected curve is used in a search method to initialize a snake technique. Finally, morphometric data extraction is performed to obtain tooth measurements for dentist diagnosis. Experiments show the advantages of the proposed method to extract teeth from X-Ray images and discuss its drawbacks.

Segmentation and Feature Extraction of Panoramic Dental X-Ray Images

2010 ◽  
Vol 1 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Pedro H.M. Lira ◽  
Gilson A. Giraldi ◽  
Luiz A. P. Neves
Keyword(s):  
Feature Extraction ◽  
Data Extraction ◽  
Principal Component ◽  
Original Image ◽  
Radiographic Images ◽  
Shape Model ◽  
X Ray ◽  
Quadtree Decomposition ◽  
Segmentation Approach ◽  
Low Pass

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generate a mask. Then, the original image is processed by Otsu’s thresholding. The result is improved by morphological operators and the quadtree mask is applied to address overlapping, a common problem in X-ray images. The obtained regions are searched and the larger ones are selected to find tooth candidates. The boundary of the obtained regions are extracted and aligned with the shape model in order to recognize the target tooth (molar). The selected curve is used in a search method to initialize a snake technique. Finally, morphometric data extraction is performed to obtain tooth measurements for dentist diagnosis. Experiments show the advantages of the proposed method to extract teeth from X-Ray images and discuss its drawbacks.

scholarly journals Variational Method Integrating Edge Detection and Smoothing in Digital Images

2019 ◽  
Author(s):  
Italo M. F. Santos ◽  
Abimael D. Loula ◽  
Gilson A. Giraldi ◽  
Gastão F. Miranda Junior ◽  
Paulo S. S. Rodrigues
Keyword(s):  
Edge Detection ◽  
Variational Approach ◽  
Numerical Scheme ◽  
Iterative Scheme ◽  
Edge Extraction ◽  
Original Image ◽  
Image Smoothing ◽  
Linear Scheme ◽  
Low Pass ◽  
High Pass

There is a consensus in computer vision about the importance of the scale concept for edge extraction and for image smoothing or representation. In this paper we explore a variational approach that allows to put together edge detection and image smoothing in a unified linear scheme. Basically, the functional proposed by Mumford and Shah is re-written as an energy defined with two arguments: the first one representing smooth versions of the original image and the second one encompassing its edge set. We follow known results in the variational analysis to obtain a numerical scheme to minimize the energy. We apply Fourier analysis to verify that the iterative scheme converges to a low-pass representation of the original image in the first argument and a high-pass signal in the other one. In the experimental results we show that the obtained scheme encourages intraregion image smoothing in preference to interregion blurring with edge localization at a desired scale.

scholarly journals Old Wall Paintings Restored Using Digital Technology

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Logeshwaran R
Keyword(s):  
Digital Technology ◽  
Weighted Average ◽  
Mean Value ◽  
Wall Paintings ◽  
Original Image ◽  
The Mean

The ancient mural pictures are virtually recreated in this study using a technique that takes a weighted average of the mean image and the original image. There are four phases to this approach. Correlation and convolution are used to detect and extract lines. Toggle filter is used to improve the extracted lines. Templates with various orientations are used for correlation and convolution. Using k-means clustering, the pixels are grouped and each pixel is replaced by the cluster's mean value. After that, the weighted average is used to restore the ancient wall paintings.

Sign in / Sign up

Export Citation Format

Share Document