Correlation of Adjacent Pixels for Multiple Image Registration

Ranganath,; Shiva,

doi:10.1109/tc.1985.1676607

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…procedures to adjust adjacent pixels may improve the results (Ranganath and Shiva, 1985), but this issue is beyond the scope of this research. In addition, all of our validation maps have errors.…”

Section: Ft1mentioning

confidence: 99%

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Huesca

Riaño

Ustin

2019

International Journal of Applied Earth Observation and Geoinfor

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A B S T R A C TOriginally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.

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Section: Ft1mentioning

confidence: 99%

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Huesca

Riaño

Ustin

2019

International Journal of Applied Earth Observation and Geoinfor

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“…Table 8 computes correlation, MSE, PSNR, and SNR. Speckle coefficient for a frame with Speckle noise before and after median filter [30,35]. • Rotational attacks: Appropriate selection weights and learning rates considerably limit the impact of rotation on video frames [36,37].…”

Section: Noise and Attacksmentioning

confidence: 99%

Even symmetric chaotic and skewed maps as a technique in video encryption

El-den

Raslan

Abdullah

2023

EURASIP J. Adv. Signal Process.

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The massive growth and use of digital multimedia through computer networks, including video and images, have increased the demand for protecting this digital data. To secure digital video, video encryption is frequently utilized. In this paper, a brand-new video scrambling technique based on two chaotic linearly symmetric maps and one chaotic tent map that has been twisted is suggested. The permutation procedure moves every frame pixel's position using a P-box created by permuting a linearly symmetric chaotic sequence. The diffusion technique employs both linearly symmetric chaos maps and distorted tent maps to create key streams. The keystream closely resembles simple frames because the pixels in the permuted frame indicate which of the two even symmetric chaos maps is replicated each time for the following byte. The information entropy, histogram, neighboring pixel correlation and sensitivity analysis, number of pixels changing regions (NPCR), and unified mean change intensity are used to thoroughly evaluate the recommended method's capacity to improve performance and security (UACI). Comparatively to other methods, the suggested algorithm is resistant to clipping, salt and pepper noise, speckle noise rotation assaults, and clipping. This positive outcome indicates that the plan can be successfully implemented for secure video communication applications.

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“…Another application of correlation of adjacent pixels is image registration [4]. Authors use a feature vector containing correlation information to determine the best matches between search area and windows.…”

Section: Introductionmentioning

confidence: 99%

Statistical dependence of pixel intensities for pattern recognition

Smielik

Kuhnert

2013

2013 IEEE International Conference on Industrial Technology (ICIT)

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Abstract-In this paper, we describe an algorithm for speeding up object recognition by reducing the amount of pixels taken into account when processing images. We show that some statistically stable regions can be found on image. Taking just one pixel from each region preserves the most of information of the image. We employ linear dependency between pixel intensity values to organize neighbouring pixels in groups. Bayesian classification was chosen to prove suitability. We present the results that show computation speed increase without significant performance losses.

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Correlation of Adjacent Pixels for Multiple Image Registration

Cited by 11 publications

References 9 publications

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Spectral mapping methods applied to LiDAR data: Application to fuel type mapping

Even symmetric chaotic and skewed maps as a technique in video encryption

Statistical dependence of pixel intensities for pattern recognition

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