scholarly journals Basic Introduction to Image Processing Using NIH-lmage as a Model

1994 ◽  
Vol 2 (9) ◽  
pp. 15-15
Author(s):  
John Mansfield
Keyword(s):  
Image Processing ◽  
Digital Instrument ◽  
The Past ◽  
Digital World ◽  
Instrument Control

Advances in camera technology and digital instrument control have meant that in modern microscopy, the image that was, in the past, typically recorded on a piece of film is now recorded directly into a computer. The transfer of the analog image seen in the microscope to the digitized picture in the computer does not mean, however, that the problems associated with recording images, analyzing them, and preparing them for publication, have all miraculously been solved. The steps involved in the recording of an image to film remain largely intact in the digital world. The image is recorded, prepared for measurement in some way, analyzed, and then prepared for presentation.

A basic introduction to image processing using NIH-Image as a model

1994 ◽  
Vol 52 ◽  
pp. 392-393
Author(s):  
John Mansfield
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Acquisition ◽  
Digital Instrument ◽  
Novice User ◽  
Nih Image ◽  
The Past ◽  
Digital World ◽  
The World ◽  
Instrument Control

Advances in camera technology and digital instrument control have meant that in modern microscopy, the image that was, in the past, typically recorded on a piece of film is now recorded directly into a computer. The transfer of the analog image seen in the microscope to the digitized picture in the computer does not mean, however, that the problems associated with recording images, analyzing them, and preparing them for publication, have all miraculously been solved. The steps involved in the recording an image to film remain largely intact in the digital world. The image is recorded, prepared for measurement in some way, analyzed, and then prepared for presentation.Digital image acquisition schemes are largely the realm of the microscope manufacturers, however, there are also a multitude of “homemade” acquisition systems in microscope laboratories around the world. It is not the mission of this tutorial to deal with the various acquisition systems, but rather to introduce the novice user to rudimentary image processing and measurement.

A Macintosh Interface for the Cameca Camebax-Micro Electron Microprobe

1990 ◽  
Vol 48 (1) ◽  
pp. 438-439
Author(s):  
Carl E. Henderson
Keyword(s):  
Electron Microprobe ◽  
Processing Speed ◽  
Word Processing ◽  
State Of The Art ◽  
Computer Control ◽  
Third Party ◽  
Disk Storage ◽  
The Past ◽  
User Facility ◽  
Instrument Control

Over the past few years it has become apparent in our multi-user facility that the computer system and software supplied in 1985 with our CAMECA CAMEBAX-MICRO electron microprobe analyzer has the greatest potential for improvement and updating of any component of the instrument. While the standard CAMECA software running on a DEC PDP-11/23+ computer under the RSX-11M operating system can perform almost any task required of the instrument, the commands are not always intuitive and can be difficult to remember for the casual user (of which our laboratory has many). Given the widespread and growing use of other microcomputers (such as PC’s and Macintoshes) by users of the microprobe, the PDP has become the “oddball” and has also fallen behind the state-of-the-art in terms of processing speed and disk storage capabilities. Upgrade paths within products available from DEC are considered to be too expensive for the benefits received. After using a Macintosh for other tasks in the laboratory, such as instrument use and billing records, word processing, and graphics display, its unique and “friendly” user interface suggested an easier-to-use system for computer control of the electron microprobe automation. Specifically a Macintosh IIx was chosen for its capacity for third-party add-on cards used in instrument control.

scholarly journals The Study of Three-Dimensional Fingerprint Recognition in Cultural Heritage

2021 ◽  
Vol 14 (4) ◽  
pp. 1-20
Author(s):  
Dzemila Sero ◽  
Isabelle Garachon ◽  
Erma Hermens ◽  
Robert Van Liere ◽  
Kees Joost Batenburg
Keyword(s):  
Image Processing ◽  
Cultural Heritage ◽  
Three Dimensional ◽  
Fingerprint Recognition ◽  
Person Identification ◽  
Imaging Technologies ◽  
The Past ◽  
Reconstruction Methods ◽  
Cultural Systems ◽  
Digital Fingerprint

Fingerprints play a central role in any field where person identification is required. In forensics and biometrics, three-dimensional fingerprint-based imaging technologies, and corresponding recognition methods, have been vastly investigated. In cultural heritage, preliminary studies provide evidence that the three-dimensional impressions left on objects from the past (ancient fingerprints) are of paramount relevance to understand the socio-cultural systems of former societies, to possibly identify a single producer of multiple potteries, and to authenticate the artist of a sculpture. These findings suggest that the study of ancient fingerprints can be further investigated and open new avenues of research. However, the potential for capturing and analyzing ancient fingerprints is still largely unexplored in the context of cultural heritage research. In fact, most of the existing studies have focused on plane fingerprint representations and commercial software for image processing. Our aim is to outline the opportunities and challenges of digital fingerprint recognition in answering a range of questions in cultural heritage research. Therefore, we summarize the fingerprint-based imaging technologies, reconstruction methods, and analyses used in biometrics that could be beneficial to the study of ancient fingerprints in cultural heritage. In addition, we analyze the works conducted on ancient fingerprints from potteries and ceramic/fired clay sculptures. We conclude with a discussion on the open challenges and future works that could initiate novel strategies for ancient fingerprint acquisition, digitization, and processing within the cultural heritage community.

scholarly journals A Wide-Ranging Review on Diabetic Retinotherapy

2020 ◽  
Vol 18 (02) ◽  
pp. 169-187
Author(s):  
Toufique Ahmed Soomro ◽  
Ahsin Murtaza Bughio ◽  
Shahid Hussain Siyal ◽  
Ali Anwar Panwar ◽  
Nasreen Nizamani
Keyword(s):  
Image Processing ◽  
Blood Vessels ◽  
Detection System ◽  
Eye Diseases ◽  
Fundus Images ◽  
The Past ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Computerized System ◽  
Automatic Algorithm

Diabetic Retinopathy (DR) is one of the major eye diseases that causes damage to retina of the human eye ball due to the rupture of tiny blood vessels. DR is identified by the ophthalmologists on the basis of various specifications i.e., textures, blood vessels and pathologies. The ophthalmologists are recently considering software for eye diseases detection based on image processing designed by the computing techniques and bio-medical images. In the analysis of medical imaging, traditional techniques of image processing and computer vision have played an important role in the field of ophthalmology. From the past two decades, there is a tremendous advancement in the development of computerized system for DR detection. This paper comprises the five parts of analysis on image based retinal detection DR, named as review of low varying contrast techniques of the retinal fundus Images (RFI), review of noise effect in the fundus images, review of pathology detection method from the retinal fundus images, review of blood vessels extraction from the RFI, and review of automatic algorithm for the DR detection. This paper presents a comprehensive detail to each problem in the retinal images. The procedures that are currently utilized to analyze the contrast issue and noise issues are discussed in detail. The paper also explains the techniques used for segmentation. In the end, the recent automated detection system of related eye diseases or DR is described.

Sixth Sense Technology

2016 ◽  
pp. 188-215 ◽  
Author(s):  
Zeenat S. AlKassim ◽  
Nader Mohamed
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Real World ◽  
Digital World ◽  
The Future ◽  
Computer Interaction

In this chapter, the authors discuss a unique technology known as the Sixth Sense Technology, highlighting the future opportunities of such technology in integrating the digital world with the real world. Challenges in implementing such technologies are also discussed along with a review of the different possible implementation approaches. This review is performed by exploring the different inventions in areas similar to the Sixth Sense Technology, namely augmented reality (AR), computer vision, image processing, gesture recognition, and artificial intelligence and then categorizing and comparing between them. Lastly, recommendations are discussed for improving such a unique technology that has the potential to create a new trend in human-computer interaction (HCI) in the coming years.

scholarly journals ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

2017 ◽  
Vol 10 (13) ◽  
pp. 284
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Online Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
The Past ◽  
Slow Development ◽  
Online Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency.  In this study we present a novel online learning algorithm for network based computational operations in image processing setting

Randomized Hough Transform

2011 ◽  
pp. 1343-1350 ◽  
Author(s):  
Lei Xu ◽  
Erkki Oja
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Hough Transform ◽  
Random Sampling ◽  
Computational Cost ◽  
Detection Accuracy ◽  
The Past ◽  
Randomized Hough Transform ◽  
Dynamic Storage ◽  
High Computational Cost

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for decades, while the key idea remains more or less the same. Proposed in 1989 and further developed thereafter, the Randomized Hough Transform (RHT) manages to considerably overcome these shortcomings via innovations on the fundamental mechanisms, with random sampling in place of pixel scanning, converging mapping in place of diverging mapping, and dynamic storage in place of accumulation array. This article will provides an overview on advances and applications of RHT in the past one and half decades.

scholarly journals A Review on Automatic Soil Classification in Digital Image Processing

2021 ◽  
Vol 9 (VIII) ◽  
pp. 449-455
Author(s):  
Shraddha Shivhare
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
False Alarm ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Time ◽  
Soil Classification ◽  
Research Papers ◽  
Digital World ◽  
Precision Rate

Soil classification is an essential piece of geology. However, many examinations have assessed the precision and consistency of the soil classification using various techniques. This examination starts by evaluating the verifiable advancement of soil classification science. The verifiable audit contextualizes the wordings and the speculations of soil development factors, which supported soil classification frameworks. This paper is intended to review some research papers on soil classification and analyze the limitations of implemented techniques by their parameters. In the age of digital world, it is beneficial to obtain the information from image without any hassle. Machine learning is an approach through which we can obtain the better level of accuracy and minimize the false alarm rate. But machine learning requires so many samples through which we can observe the correct precision that also requires much storage that may takes much processing time that reduces the feasibility of the system. We have to train a system with limited number of samples with high iterations that produces higher precision rate with minimal errors.

scholarly journals Interpretation and Translation of American Sign Language for Hearing Impaired Individuals using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 415-420
Keyword(s):  
Image Processing ◽  
American Sign Language ◽  
Sign Language ◽  
English Language ◽  
Hearing Impaired ◽  
Hand Gesture ◽  
American Sign ◽  
The Past ◽  
Impaired People ◽  
Live Image

The growth of technology has influenced development in various fields. Technology has helped people achieve their dreams over the past years. One such field that technology involves is aiding the hearing and speech impaired people. The obstruction between common individuals and individuals with hearing and language incapacities can be resolved by using the current technology to develop an environment such that the aforementioned easily communicate among one and other. ASL Interpreter aims to facilitate communication among the hearing and speech impaired individuals. This project mainly focuses on the development of software that can convert American Sign Language to Communicative English Language and vice-versa. This is accomplished via Image-Processing. The latter is a system that does a few activities on a picture, to acquire an improved picture or to extricate some valuable data from it. Image processing in this project is done by using MATLAB, software by MathWorks. The latter is programmed in a way that it captures the live image of the hand gesture. The captured gestures are put under the spotlight by being distinctively colored in contrast with the black background. The contrasted hand gesture will be delivered in the database as a binary equivalent of the location of each pixel and the interpreter would now link the binary value to its equivalent translation delivered in the database. This database shall be integrated into the mainframe image processing interface. The Image Processing toolbox, which is an inbuilt toolkit provided by MATLAB is used in the development of the software and Histogramic equivalents of the images are brought to the database and the extracted image will be converted to a histogram using the ‘imhist()’ function and would be compared with the same. The concluding phase of the project i.e. translation of speech to sign language is designed by matching the letter equivalent to the hand gesture in the database and displaying the result as images. The software will use a webcam to capture the hand gesture made by the user. This venture plans to facilitate the way toward learning gesture-based communication and supports hearing-impaired people to converse without trouble.

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