Digital Signal and Image Processing Using MATLAB®

Blanchet, Gérard; Charbit, Maurice

doi:10.1002/9781119054009

Cited by 7 publications

(2 citation statements)

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“…Evidently, hard carbon can be regarded as a heterogeneous agglomerate, containing many identifiable substructures that carry information about the origins and carbonization processes. Herein, the watershed algorithm 31 was applied to segment the conjugate maps of modulation field (marked by fitting ellipses).…”

Section: Resultsmentioning

confidence: 99%

Nanostructure Quantification of Hard Carbon Electrodes through Advanced HRTEM Image Analysis

Li¹,

Ouyang²,

Wang³

et al. 2022

J. Electrochem. Soc.

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The currently remaining uncertainties on nanostructure has severely hindered the optimal design of hard carbon electrodes. High-resolution transmission electron microscopy (HRTEM) characterizations allow us to carry out structural measurements at the atomic level, however, extracting physically meaningful and statistically robust data from HRTEM images is not an easy process. In this work, intelligent fringe analysis and Gabor filtering algorithm were combined to reduce subjective bias and improve statistical reliability. Fringe orientation was described by nematic symmetry of second-order statistics. As a representative, detailed structural analysis of sucrose-derived hard carbon prepared at different heat-treatment temperatures was performed. Advanced HRTEM image analysis reveals that the nanostructural evolution of hard carbon is manifested in several aspects, involving the growth and flattening of fringes, enhancement of ordering alignment, increase of stacking, and development of symmetry. More importantly, these structural parameters were quantitatively analyzed in detail. The structural parameters obtained by HRTEM are consistent with similar parameters obtained by other techniques such as X-ray diffraction. This work provides an unprecedented quantitative insight into the nanostructural trends of hard carbon, with potential to aid researchers in elucidating the structure-property relationships of carbon electrodes

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Section: Resultsmentioning

confidence: 99%

Nanostructure Quantification of Hard Carbon Electrodes through Advanced HRTEM Image Analysis

Li¹,

Ouyang²,

Wang³

et al. 2022

J. Electrochem. Soc.

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“…Table 1 shows dataset image that we use for analyzing. Handling data to extract relevant information or transform it into useful information is what signal processing entails [24], [25]. Digital image processing is divided into several image processing processes, including: (1).…”

Section: Methodsmentioning

confidence: 99%

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Kristanto

Riadi

Prayudi

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD), Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.

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