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Image Processing

Jason Villemaire, Pc Pentium, Mb Ram et al. · 2003

Implementation •Grab single image from camera or file •Convert buffer to image array for pixel manipulation •One-dimensional array with index format

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Image Processing

Sewoong Oh, Tina Dong, Sufeng Li et al. · 2007

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Image Processing

Gustafsson, Johan · Lund University Publications (Lund University) · 2022

Image processing provides tools for image enhancement and extraction of information from images. Modification of the colour table allows for amplification of the contrast in certain pixel-value intervals while the contrast is suppressed in other intervals. This can in turn be used to selectively enhance the displayed image in intervals of special importance to the current problem. One of the most important conflicts in imaging is the trade-off between a high signal-to-noise-ratio (SNR) and a good spatial resolution. Low-pass filtering can be used to improve the SNR at the expense of spatial resolution. Likewise, high-pass filtering can be used to enhance edges but tends to be noise sensitive. Another important kind of operations is spatial transformations, for example, scaling, translation, and rotation. The discrete nature of digital images makes the application of such operations dependent on the use of interpolation. There are different interpolation methods that vary in accuracy and computational burden. Lastly, many applications are dependent on image segmentation. The most common segmentation method is manual delineation, but there are a number of automatic and semi-automatic methods that may be used to save time and improve reproducibility. While the presentation of the subject is by no means complete, the hope is that it will serve as an introduction to the most important image-processing techniques used in nuclear medicine.

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Image Processing

NASA Technical Reports Server (NASA)

A new spinoff product was derived from Geospectra Corporation's expertise in processing LANDSAT data in a software package. Called ATOM (for Automatic Topographic Mapping), it's capable of digitally extracting elevation information from stereo photos taken by spaceborne cameras. ATOM offers a new dimension of realism in applications involving terrain simulations, producing extremely precise maps of an area's elevations at a lower cost than traditional methods. ATOM has a number of applications involving defense training simulations and offers utility in architecture, urban planning, forestry, petroleum and mineral exploration.

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Image processing

Elodie Germani · Zenodo (CERN European Organization for Nuclear Research) · 2023

Tool for image processing workflows.

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Image processing.

Jane Courtney (815942), Elena Woods (118840), Dimitri Scholz (519227) et al. · Figshare · 2015

<p>A zoomed confocal image of a HeLa cell transiently expressing photo-converted Dendra2 (A) before and (B) after application of a median filter; (C) before and (D) after subtraction of the pre-conversion red image (background); (E) before and (F) after application of the Contrast Stretching Transform.</p>

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Image processing.

Huazhe Wang (11361705), Li Ma (46633) · Figshare · 2024

<div><p>In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.</p></div>

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Image Processing.

Chen,C H · Defense Technical Information Center (DTIC) · 1981

This report is organized according to the topics we have worked under this project which include statistical image segmentation, two-dimensional ARMA models, multichannel (multivariate) maximum entropy spectral analysis, and non-Gaussian signal processing, etc. Under each topic, the reports and papers published or presented are also listed. A new AED-512 Imaging/Graphics terminal has been installed in our PDP11/45 minicomputer. The use of the terminal for the image processing project research is also presented. Statistical image segmentation refers to the computer-oriented procedures that partition the image into meaningful parts by using the statistical pattern recognition techniques. A number of techniques have been studied, some of which are supervised while others are unsupervised. A critical evaluation of these techniques has been made. Furthermore we have performed an extensive computer study of the Fisher's linear discriminant method, maximum likelihood estimation and decision-directed method for image segmentation.

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Image Processing

Yung-Hsiang Lu, George K. Thiruvathukal · 2023

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Image Processing

Hans Op de Beeck, Chie Nakatani · Cambridge University Press eBooks · 2025

Developed specifically for students in the behavioral and brain sciences, this textbook provides a practical overview of human neuroimaging. The fully updated second edition covers all major methods including functional and structural magnetic resonance imaging, positron emission tomography, electroencephalography, magnetoencephalography, multimodal imaging, and brain stimulation methods. Two new chapters have been added covering computational imaging as well as a discussion of the potential and limitations of neuroimaging in research. Experimental design, image processing, and statistical inference are addressed, with chapters for both basic and more advanced data analyses. Key concepts are illustrated through research studies on the relationship between brain and behavior, and review questions are included throughout to test knowledge and aid self-study. Combining wide coverage with detail, this is an essential text for advanced undergraduate and graduate students in psychology, neuroscience, and cognitive science programs taking introductory courses on human neuroimaging.

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Image Processing

Jason H. Best, Clay Barrett, Diego J Barroso · OSF Preprints (OSF Preprints) · 2023

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Image Processing

Christoph U. Keller, Ramón Navarro, Bernhard R. Brandl · SPIE eBooks · 2015

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Image processing

2003

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Image Processing

Nico Karssemeijer, Peter R. Snoeren · Medical radiology · 2010

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Image processing.

Yuan Wan (498398), Bowen Zhang (595078), Qinghui Lai (19139234) et al. · Figshare · 2024

<div><p>To improve the accuracy of the Hami melon discrete element model, the parameters of the Hami melon seed discrete element model were calibrated by combining practical experiments and simulation tests. The basic physical parameters of Hami melon seeds were obtained through physical experiments, including triaxial size, 100-grain mass, moisture content, density, Poisson’s ratio, Young’s modulus, shear modulus, angle of repose, suspension speed and various contact parameters. Taking the repose angle of seed simulation as an index, the parameters of each simulation model were significantly screened by the Plackett-Burman test. The results showed that the recovery coefficient, static friction coefficient and rolling friction coefficient of Hami melon seeds had significant effects on repose angle. Based on the steepest climbing test and quadratic regression orthogonal rotation combination test, it was determined that the significant order of the influence of various contact parameters on the angle of repose was static friction coefficient, collision recovery coefficient, and rolling friction coefficient. The optimal parameter combination was obtained through the mathematical regression model between the angle of repose and various contact parameters, namely, the collision recovery coefficient of Hami melon seeds was 0.518, the static friction coefficient of Hami melon seeds was 0.585 and the rolling friction coefficient of Hami melon seeds was 0.337. Under this condition, three static seed-dropping experiments and dynamic rolling accumulation experiments were carried out. The average simulated angle of repose was 31.93°, and the relative error with the actual value was only 1.71%. The average simulated rolling accumulation angle was 51.98°, and the relative error with the actual value was only 1.92%.</p></div>

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Image Processing

Abdussalam Elhanashi, Sergio Saponara · 2026

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Image Processing

2006

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Image Processing

Μαρία Πέτρου, Pedro Garcı́a-Sevilla · 2006

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Image processing

Cressie, Noel A, Davidson, Jon L · Research Online (University of Wollongong) · 1998

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Image Processing

Syarif, Syafruddin · Hasanuddin University Repository · 2013

Pada tahap pengolahan gambar, menggunakan segmentasi konvesional, di mana hanya lapisan merah diekstrak dari sebuah gambar RGB. Sebuah gambar sampel dipotong berdasarkan kondisi, yang merupakan daerah pohon dan daerah non-pohon. Hasil foto yang dipotong diektraksi dengan segmentsi konvensional. Segmentasi konvensional menggunakan pola 25 lapisan merah, pola yang dipilih melalui trial dan error tahap. Pada sistem ini ada 2100 merah pola lapisan untuk daerah pohon dan 2000 merah pola lapisan untuk non-pohon daerah atau sisa-sisa pembalakan liar. Sampel gambar yang digunakan untuk tes, diambil dari hutan lindung di Kampar, Riau dengan ukuran 750 mx 560 m. Wilayah ini dipilih karena ketersediaan citra di daerah ini pada Google Eart. Hasil pengujian menunjukkan akurasi untuk mendeteksi pembalakan liar bersama model backpropagation Neural Network mencapai 100%

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