Zhengjun Qiu, Hui Fang, Yun Zhang et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Leaf Area Index (LAI), as a fundamental parameter to evaluate the physiological condition of plants, was calculated by image processing based on machine vision technology. The measurement system hardware consisted primarily of the MS3100 3CCD camera, the image grabber card, a desktop computer and the acquired images were processed by Matlab and ENVI. After acquiring the 3 images by the 3CCD camera of Green, Red and NIR channels, the NIR image was considered more effective in separating the soil background for its higher contrast value. Thus, it was selected for image processing to calculate the leaf area index (LAI). The transect method was applied to obtain the threshold 50 in the binary image conversion and the soil background was thus eliminated as a result that most of its reflectance in the image was under 50. Then the 'imerode'-'imdilate' operation in the image processing box of Matlab was used to remove the left crop stem noises, including those small weeds in the binary image background. Consequently, the LAI of the acquired NIR image was calculated as 0.523 by dividing the total image pixel amount by that of the black pixels in the binary image.
Cited by 1
Minoru Inamura, Hiromichi Toyota, Kazunori Ueda et al. · Transactions of the Society of Instrument and Control Engineers · 1979
Remotely sensed multispectral images are analyzed by the methods of maximum likelihood, Euclidean minimum distance and correlation etc. But a new fast analyzing method is searched because the processing speed of the conventional is slow.This paper describes the multispectral image processing by means of a binary decision tree. To appreciate the ability of the image analysis, the efficiency of the binary decision tree is compared with the efficiency of the maximum likelihood method under the same conditions.As a result, the processing speed of the binary decision tree was shown to be twenty times as fast as that of the maximum likelihood with a nine-classes six-features classification. Moreover, the processing time of the binary decision tree method does not increase even if the number of the features increses. This property is another strong point of the binary decision tree. On the contrary, the processing time of the maximum likelihood method is in proportion to the square of the number of selected features. On the other hand, the classification accuracy of the binary decision tree was about equal to that of the maximum likelihood method.
Cited by 3Open access
Vikas Sindhu · 2015
Abstract- Halftone processing is a technique to convert grayscale images into two-tone binary images. The two- tone binary images are used in printing and display. An image having several bits for brightness levels is converted into a binary image consisting of black and white dots which looks similar to an input image. The similarity of images is measured by the differences in the weighted sums of brightness levels of pixels in a neighborhood surrounding each pixel. The problem of producing halftoned image is to obtain an optimal binary image that retains the intensity information contained in the image. In this paper, we will study data hiding and steganography via image halftoning
Cited by 0
Chongzheng Fang, Chenhui Zhou, Fan Ye · 2023
With the continuous development of medical technology, the demand for the quantity of high performance ultrasonic imaging system is increasing rapidly. In ultrasonic imaging system, chips related to ultrasound signal processing play a core role, directly determining the final quality of the image. This article investigates the common architectures of digital processing for high performance ultrasonic imaging chips after AFE, as well as the general processing methods in the digital domain. The digital process system includes five modules: FIFO, standard binary data construction module, ADC offset elimination module, digital high pass filter (DHPF), and digital demodulator. These modules convert the non-binary data generated by the AFE into signed binary signals within the baseband of frequency domain that only contain useful information for the further image generating. The precise digital signals produced by the proposed digital processing architecture promise high quality of the final visual image of ultrasonic imaging system.
Cited by 0
Saima Iqbal, Wilayat Khan, Abdulrahman Alothaim et al. · Security and Communication Networks · 2022
Binary images have found its place in many applications, such as digital forensics involving legal documents, authentication of images, digital books, contracts, and text recognition. Modern digital forensics applications involve binary image processing as part of data hiding techniques for ownership protection, copyright control, and authentication of digital media. Whether in image forensics, health, or other fields, such transformations are often implemented in high-level languages without formal foundations. The lack of formal foundation questions the reliability of the image processing techniques and hence the forensic results loose their legal significance. Furthermore, counter-forensics can impede or mislead the forensic analysis of the digital images. To ensure that any image transformation meet high standards of safety and reliability, more rigorous methods should be applied to image processing applications. To verify the reliability of these transformations, we propose to use formal methods based on theorem proving that can fulfil high standards of safety. To formally investigate binary image processing, in this paper, a reversible formal model of the binary images is defined in the Proof Assistant Coq. Multiple image transformation methods are formalized and their reliability properties are proved. To analyse real-life RGB images, a prototype translator is developed that reads RGB images and translate them to Coq definitions. As the formal definitions and proof scripts can be validated automatically by the computer, this raises the reliability and legal significance of the image forensic applications.
Cited by 5Open access
sindhu, vikas · International Journal of Advances in Engineering Sciences (RG Journals of Scientific Research) · 2013
Halftone processing is a technique to convert grayscale images into two-tone binary images.The two-tone binary images are used in printing and display.An image having several bits for brightness levels is converted into a binary image consisting of black and white dots which looks similar to an input image.The similarity of images is measured by the differences in the weighted sums of brightness levels of pixels in a neighborhood surrounding each pixel.The problem of producing halftoned image is to obtain an optimal binary image that retains the intensity information contained in the image.In this paper, we will study data hiding and steganography via image halftoning
Cited by 1Open access
Dražen Turinski, Ilija Svalina, Roberto Lujić et al. · ACTA IMEKO · 2023
Some of the problems that arise during image processing are analysed in this paper. The most commonly used software tools in image processing require binary photography, and its conversion into the binary form is required first. Conversion problems, and their proper solution, are key steps for further machine determination of product dimensions. Removing smudges on binary photos and rotating displays for photo overlap methods are the problems that are solved in the present work, in order to successfully prepare the photo with software for further determination of product dimensions. The knowledge gained in this paper will help scientists and facilitate their software preparation and processing of the photo used to determine the required dimensions of the product.
Cited by 1Open access
Elahe Masaeli (383146), Mohammad Morshed (383147), Mohammad Hossein Nasr-Esfahani (383148) et al. · Figshare · 2015
<p>(A) Original SEM image of SCs on scaffold after 1 day of cell culture; (B) binary image showing the processing output of nonlinear diffusion filtering; (C) Radon transform results of binary image for a range of [0,180°] and with a resolution of θ = 1°; (D) dominant peak of the maximum of Radon transform matrix.</p>
Cited by 0Open access
Frederick M. Waltz · 1995
Binary correlation is often used for finding specified patterns in complex binary images, especially in industrial inspection tasks such as locating the corners and/or edges of parts. As such, it is an important tool for higher-level “intelligent ” vision systems. Binary correlation is a form of binary template matching which provides a numerical value corresponding to “degree of fit ” rather than an “all or nothing ” answer. Commercially-available high-speed image processing systems can readily perform this operation using linear convolvers, but such convolvers are very expensive except for very small kernels. Furthermore, linear convolvers constitute a gross “overkill ” for the relatively simple operation of binary correlation. Specialized binary convolvers have been built, but are not part of standard commercial systems. This paper describes a new pipelined implementation of binary correlation which fits into the standard SKIPSM (Separated-Kernel Image Processing using Finite State Machines) architecture and which can be built using standard ICs costing less than $500 total. The same approach can also be implemented in software, providing an order-of-magnitude increase in speed at no extra cost. Furthermore, this same SKIPSM architecture is highly versatile and programmable, allowing it to be software-reconfigured to perform hundreds of other pipelined image processing operations.
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Takuya Okamoto, Sharifu Ura · Preprints.org · 2024
Verifying the accuracy of a 3D-printed object involves using an image processing system. This system compares images of the CAD model of the object to be printed with its 3D-printed counterparts to identify any discrepancies. It is important to note that the integrity of the accuracy measurement is heavily dependent on the image processing settings chosen. This study focuses on this issue by developing a customized image processing system. The system generates binary images of a given CAD model and its 3D-printed counterparts and then compares them pixel-by-pixel to determine the accuracy. Users can experiment with various image processing settings, such as grayscale to binary image conversion threshold, noise reduction parameters, masking parameters, and pixel-fineness adjustment parameters, to see how they affect accuracy. The study concludes that the grayscale to binary image conversion threshold has the most significant impact on accuracy and that the optimal threshold varies depending on the color of the 3D-printed object. The system can also effectively eliminate noise (filament marks) during image processing, ensuring accurate measurements. Additionally, the system can measure the accuracy of highly complex porous structures where the pores size, depth, and distribution are random. The insights gained from this study can be used to develop intelligent systems for the metrology of additive manufacturing.
Cited by 2Open access
Maria Petrou, Pedro Garcı́a-Sevilla · 2006
This chapter contains sections titled: The uncertainty principle and its implications in signal and image processing Gabor functions Prolate spheroidal sequence functions Wavelets Where Image Processing and Pattern Recognition meet Laws' masks and the “what looks like where” space Local binary patterns The Wigner distribution
Cited by 5
Tzu-Yuan Chang, Yusuke TAKIGUCHI, Minoru Okada · Proceedings of the International Conference on Document Analysis and Recognition · 2007
Image segmentation is an essential topic in a document processing system, and also is an important initial task for higher level image processing such as recognition or object tracking since recent years. In this paper we propose a method for segmentation and extraction of the regions of mathematical formulae and graphics in an academic paper. First, an academic paper image is transformed into a binary image by Ohtsu's method. Second, the boundary blank spaces of the binary image are removed and then the image is recursively segmented and extracted by vertical and horizontal projection profiles. We apply the proposed method to 150 academic paper images, and show that the method is adaptable, robust and effective for segmentation.
Cited by 12
Yoshinori Kuno, Hideo Numagami, Minoru Ishikawa et al. · Robotica · 1983
SUMMARY This paper presents an intelligent robot vision system using TOSPIX which has been newly developed to realize frequently-used and time-consuming image processing functions at low-cost and high-speed. The vision system has been studied for use in observing surface information about electric parts (dry batteries), inspecting them and then placing good ones into a given box. Three major robot vision functions are implemented here: object recognition, inspection and position determination by binary and gray-scale image processing techniques. While binary image techniques are used in battery terminal inspection and box position determination gray-scale image processing functions are performed in a label pattern check on a battery surface, front or rear surface determination, and surface defect inspection.
Cited by 4
MohammadReza Saadat, Benedek Nagy · Acta Polytechnica Hungarica · 2018
Cellular automata are parallel computing devices working on a discrete timescale.In the paper, each cell of the triangular grid has a state from the binary set (i.e., we have a binary pattern, an image, on the grid), and the state in the next time instant depends only on the actual state of the cell itself and the states of its side-neighbor cells.We illustrate their use in image synthesis, e.g., generating snowflakes, and in image analysis: some of our automata are connected to image processing operations, e.g., dilation and erosion.Computation of Hausdorff distance of two binary images on the triangular grid is also presented.In image processing, and especially in mathematical morphology, operations are local operations, and thus, cellular automata are apt to use.On the other side, the triangular grid is not a point lattice, thus the definition of translation based image operations is not always straightforward.
Cited by 6Open access
M. Kalpana · 2014
Digital image Processing is one of the basic and important tool in the image processing and computer vision. In this paper we discuss about the extraction of a digital image edge using different digital image processing techniques. Edge detection is the most common technique for detecting discontinuities in intensity values. The input image or actual image have some noise that may cause the of quality of the digital image. Firstly, wavelet transform is used to remove noises from the image collected. Secondly, some edge detection operators such as Differential edge detection, Log edge detection, canny edge detection and Binary morphology are analyzed. And then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the Binary morphology operator can obtain better edge feature. Finally, in order to gain clear and integral image profile, the method of ordering closed is given. After experimentation, edge detection method proposed in this paper is feasible.
Cited by 0
Carmen Escribano, Antonio Giraldo, María Asunción Sastre et al. · UPM Digital Archive (Technical University of Madrid) · 2008
We present here a java applet, accessible through the World Wide Web, which allows to apply to a binary digital image a series of topological algorithms for image processing.
Cited by 1Open access
Chia-Lun J. Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
For a binary image containing only curves (and lines) in a background infested by binary noises, (e.g., salt-and-pepper noise,) a very efficient way to extract the image data, and to save them in a very compact file for an accurate and complete image recall later, is very attractive to many image processing and pattern recognition systems. This paper reports the data-extraction method we developed recently for inputting a binary image to a special neural-network pattern recognition system, the noniterative, real-time learning system. We use an adaptive/tracking window to track the direction of a continuous curve in the binary image, and record the xy-coordinates of all points on this curve until the window hits an end point, or a branch point, or the original starting point. By scanning this tracking window across the whole image frame, we can then segment the original binary image into many single curves. The xy’s of points on each curve can then be analyzed by a curve fitting process, and the analytic data can be stored very compactly in an analog data file. This data file can be recalled very efficiently to reconstruct the original binary image, or can be used directly for inputting to a special neural network and for carrying out an extremely fast pattern learning process. This paper reports the image-processing steps, the programming algorithm, and the experimental results on this novel <b>image extraction</b> technique. It will be verified in each experiment by reconstructing the original image from the compactly extracted analog data file.
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Fabian Englbrecht (10661097), Iris E. Ruider (10661100), Andreas R. Bausch (10661103) · Figshare · 2021
<p>Automated data annotation considers image pre-processing, binary thresholding, watershed-segmentation, filtering and post-processing to provide a training dataset for nuclei segmentation with a neural network. Manual data annotation is applied on raw image data (after pre-processing) in order to provide a training dataset of images and annotation masks.</p>
Cited by 0Open access
Khairul Anuar Mat Said, Asral Bahari Jambek · Journal of Physics Conference Series · 2021
Abstract Digital image processing is important for image information extraction. One of the image processing methods is morphological image processing. This technique uses erosion and dilation operations to enhance and improve the image quality by shrinking and enlarging the image foreground. However, morphological image processing performance depends on the characteristics of structuring elements and their foreground image that need to be extracted. This paper studies how the structuring elements affect the performance of morphological erosion and dilation on binary images. The experimental result shows that choosing the right structuring element for morphological erosion and dilation can significantly influence the foreground and background structure of the output image.
Cited by 60Open access
James Austin · 1988
This paper describes a generalisation of the binary N tuple technique originally described by Bledsoe and Browning (1). The binary N tuple technique has commonly been used for the classification (2) and pre-processing (3) of binary images. The extension to the method described here allows grey level images of objects to be classified using the N tuple method without first having to convert the image to an intermediate binary representation. The paper illustrates the methods use in image preprocessing. ################## From : Lecture Notes in Computer Science (301). Springer Verlag. Ed. J Kittler. Pattern Recognition, 4th international Conference, Cambridge, UK, March 1988. - 2 - Introduction The binary N tuple pattern recognition process was originally described by Bledsoe and Browning (1), and has been applied to a number of image processing tasks such as character recognition (4), face recognition (2), and scene analysis (5). The N tuple process may be seen as a simple perceptr...
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