Andrés Frías-Velázquez, Wilfried Philips · Ghent University Academic Bibliography (Ghent University) · 2010
This work presents a novel parallel technique to implement stack morphological filters for image processing. The method relies on applying the image bitwise decomposition to manipulate the grayscale image at a bit-plane level, while simple logical operations and Positive Boolean Functions (PBF's) are executed in parallel to derive the transformed bit-planes. The relationship between the bitwise and threshold decomposition is closely investigated and analysed, which lead us to derive an algorithm whose control flow is full binary encoded. Furthermore, the algorithm exhibits an interesting performance, which depends on the image histogram thanks to its hierarchical processing and the study of the relationship among binary decompositions.
Cited by 5Open access
Růžička, Kamil · Brno University of Technology Digital Library (Brno University of Technology) · 2014
In the first part of the bachelor’s thesis are described the basic terms in obtaining and description of a digital image. The next part describes the theory about possibilities of image processing, which are essential for the correct separation of the text from the background, and thus for the correct conversion grayscale image to binary image. Then was performed the analysis of the task with the preparation of data for image processing. Then were applied different methods for converting grayscale to binary images and it was compiled user application. The final step is to evaluate the practical realization of the conversion, the subjective testing of respondents and objektive testing with the OCR software.
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James Vincent · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1986
Machine vision systems are rapidly becoming the standard way of automating routine visual tasks such as inspection and reading. The systems operate by capturing an image, converting the image into bits of data, and processing that data. In converting an image into data, early machine vision systems simplified the image into one with only two intensity levels--black and white. Such "binary" systems thus discarded much information. More advanced machine vision systems process all of the shades of grey present in an image and consequently have more information on which to base their decisions. This paper explains how grey-scale and binary processing differ. It shows that for accurate, consistent performance, true grey-scale systems are superior.
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Li Wan, Gut Guiyang · 2002
This paper proposes a binary image digital watermarking algorithm based on wavelet multi resolution transform.The detection threshold is given.Experimental results show that this algorithm produces a better image quality and delivers a stable performance in image processing.
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Subedi Paudel, Sunita, deWeever, Althea, Sayner, Sarah et al. · Figshare · 2022
<strong>Supplemental Figure S3: Analyzed image for the quantification of PMFs</strong>. Output binary image of PAECs cultured on a collagen-coated hydrogel with 1.25 kP a (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative image after addition of DMSO as a vehicle control after 2 minutes of baseline in which cells had 2mM extracellular Ca<sup>2+</sup>. <br> <strong>Supplemental Figure S4: Analyzed images for the quantification of PMFs</strong>. Output binary image of PAECs cultured on a collagen-coated hydrogel with 1.25 kPa (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative images after addition of 1 µM thapsigargin as a treatment after 2 minutes of baseline in which cells had 2mM extracellular Ca<sup>2+</sup>. <br> <strong>Supplemental Figure S5: Analyzed images for the quantification of PMFs.</strong> Output binary image of PAECs cultured on a collagen-coated hydrogel with 1.25 kPa (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative images after addition of DMSO as a vehicle control after 2 minutes of baseline in which cells had 0.1 µM extracellular Ca<sup>2+</sup>. <br> <strong>Supplemental Figure S6: Analyzed images for the quantification of PMFs.</strong> Output binary images of PAECs cultured on a collagen-coated hydrogel with 1.25 kPa (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative images after addition of 1 µM thapsigargin at 2 minutes in which cells had 0.1 µM extracellular Ca<sup>2+</sup>.
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Mirna Rofail, Rasha Montaser, Ahmed Younes · Scientific Reports · 2025
Ternary Quantum Image Processing (TQIP) leverages the power of qutrits to enhance Quantum Image Processing (QIP) by enabling higher information density, reducing error rates, and simplifying circuit complexity compared to traditional binary quantum systems. Inspired by the ENEQR binary model, this paper introduces the Ternary Novel Colored Quantum Representation (TNCQR), a qutrit-based model for encoding RGB digital images in a ternary quantum system. The TNCQR model requires fewer qutrits than the number of qubits needed in equivalent binary models. By incorporating a single ancilla qutrit, the model reduces the depth of high-complexity gates, thereby lowering the overall quantum cost and improving time complexity. To simplify the constructed circuits, an optimization algorithm is presented, consisting of 2 general phases applicable to any image size and one conditional phase specific to each image. This makes TNCQR an efficient, scalable, and resource-optimized quantum image representation model.
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Min Wu, Xuemin Chen, C. Richard Liu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
A highway crack monitoring system (HCMS) has been successfully developed to measure the pavement crack and its coverage of the road. A road-image processing algorithm is presented in this paper. Recurring thresholding (RT) segmentation is developed to extract crack objects from a complicated background on an estimation-verification process. A connect component object identification (CCOI) identifies the binary image object according to its connectivity with other objects. A boundary contour convolution (BCC) is applied in the binary-image object analysis to achieve fast processing and reduce computational complexity. The experiment results are provided to illustrate the performance of the system scheme.
Cited by 7
Subedi Paudel, Sunita, deWeever, Althea, Sayner, Sarah et al. · Figshare · 2022
<strong>Supplemental Figure S3: Analyzed image for the quantification of PMFs</strong>. Output binary image of PAECs cultured on a collagen-coated hydrogel with 1.25 kP a (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative image after addition of DMSO as a vehicle control after 2 minutes of baseline in which cells had 2mM extracellular Ca<sup>2+</sup>. <br> <strong>Supplemental Figure S4: Analyzed images for the quantification of PMFs</strong>. Output binary image of PAECs cultured on a collagen-coated hydrogel with 1.25 kPa (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative images after addition of 1 µM thapsigargin as a treatment after 2 minutes of baseline in which cells had 2mM extracellular Ca<sup>2+</sup>. <br> <strong>Supplemental Figure S5: Analyzed images for the quantification of PMFs.</strong> Output binary image of PAECs cultured on a collagen-coated hydrogel with 1.25 kPa (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative images after addition of DMSO as a vehicle control after 2 minutes of baseline in which cells had 0.1 µM extracellular Ca<sup>2+</sup>. <br> <strong>Supplemental Figure S6: Analyzed images for the quantification of PMFs.</strong> Output binary images of PAECs cultured on a collagen-coated hydrogel with 1.25 kPa (left) and 30 kPa (right) substrate stiffness after processing through PFA. This image was one of the representative images after addition of 1 µM thapsigargin at 2 minutes in which cells had 0.1 µM extracellular Ca<sup>2+</sup>.
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Siu-Yeung Cho, Zheru Chi, Wan-Chi Siu · 2003
Image content representation is still a challenging problem in the field of image processing. In this paper, a binary space partition (BSP) tree structure image representation incorporating with adaptive processing of data structures is presented. This tree structure representation is robust to preserve the scene semantics of image contents. Promising results were achieved when our approach was tested on the classification of natural scenery images.
Cited by 2
Julien Dubois, G. Jacquet, G. Motyl et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
In this paper we present a hardware design, built from scalable components named binary correlators, to exploit in real time the high-speed image grabbing in PIV (Particle Image Velocimetry). PIV can produce instantaneous measurements on a full flow field if real time processing can be reached. Thus, a specific hardware system is required. This hardware system is designed to allow the component scalability by using hardware templates which is grouped to form a binary correlator. Moreover, binary correlators are suitable as processing units and can be integrated in a specific architecture named Round-About. Real time measurement can be attained with only one FPGA.
Cited by 1
B. Janardhana Rao · CVR Journal of Science & Technology · 2018
Image processing play a major role in day to day life. The implementation of image processing applications using MATLAB takes place in practice. The block based implementation of image processing applications can be done with LabVIEW (Laboratory Virtual Instrument Engineering Workbench). This paper discusses the basic operations on images like extraction of the RGB (Red, Green, and Blue) components in a color image, converting the gray scale image to binary image and the edge detection process in both MATLAB and LabVIEW. Based on the experimental results, the merits and demerits of programming languages are discussed.
Cited by 3Open access
Frederick M. Waltz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 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.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Cited by 14
Hao Zhou · 2007
Mathematical Morphology is a powerful nonlinear image processing tool and is widely used in image segmentation, image filtering, shape representation, image measurement and so on. Most of the morphological operation based image processing approaches are developed for binary and grayscale images. Recently, since the color image has become the dominant image format nowadays and contains much more information than grayscale image, a problem of significant challenging in mathematical morphology is to extend basic morphological operations and their applications into color image processing area. This thesis mainly focuses on extending the existing basic morphological operators and their applications into color image processing area, by incorporating supervised and unsupervised learning techniques. The major contributions are summarized below:
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L. Li, Qianqian Zhang, Gongmei Qi et al. · 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2019
This paper mainly introduces the algorithm of avoiding obstacles based on the detection of LIDAR and gives the results of simulation under environment of MATLAB. The image acquisition system based on laser radar can provide real-time location information to meet the requirements of image acquisition and processing of system. The VFH+ algorithm is used to update angle and speed of the ASV. A method of rapid expansion, corrosion processing of binary image and device were used for the process of laser image processing. Binary image has features of simple, less-space et al., so efficiency of image processing is improved by dealing with multiple pixels at one time. The innovative point of the laser image processing technology is combined with the classical algorithm VFH+ of avoiding obstacle to realize the autonomous navigation system of ASV. The proposed ASV algorithm of avoiding obstacle based on LIDAR detection is applied to the ASV, which has great value of research and application. For example, it was used for ASV with wind and water complementary, water quality monitoring and sampling, and cleaning up the surface of oil pollution.
Cited by 3
Li, Jiaqi, Shao, Siyao, Hong, Jiarong · arXiv (Cornell University) · 2020
Conventional image processing for particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with the images consisting of complex-shaped and clustered particles with varying backgrounds. In this paper, we introduce a robust learning-based method using a single convolution neural network (CNN) for analyzing particle shadow images. Our approach employs a two-channel-output U-net model to generate a binary particle image and a particle centroid image. The binary particle image is subsequently segmented through marker-controlled watershed approach with particle centroid image as the marker image. The assessment of this method on both synthetic and experimental bubble images has shown better performance compared to the state-of-art non-machine-learning method. The proposed machine learning shadow image processing approach provides a promising tool for real-time particle image analysis.
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Kung-Shiuh Huang, B. Keith Jenkins, A. A. Sawchuk · Annual Meeting Optical Society of America · 1987
Binary image algebra (BIA) provides a systematic mathematical formalism for both digital optical cellular logic and symbolic substitution processors, in addition to being a unified theory of parallel binary image processing. 1 Cellular logic operations and symbolic substitution rules are proved to be special cases of image transformations in BIA; and BIA serves as an algebraic theory for developing and analyzing parallel algorithms of both cellular logic and symbolic substitution.
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Bharti Chauhan · International Journal of Science and Research (IJSR) · 2021
Image segmentation is considered one of the most significant image processing techniques. The technique of splitting or partitioning an image into bits, called segments, is known as image segmentation. It's most effective for applications like image compression and object detection, since processing the whole image is wasteful in these cases.As a result, image segmentation is used to separate the image's components for further processing. Digital image processing techniques allow for computer-assisted editing of digital images. Pre-processing, enhancement and display, and content retrieval are the general processes that all forms of data must go through by using modern technology. In image processing, image segmentation is a critical stage. Image segmentation strategies such as Thresh-holding, Edge Detection, Colour dependent binary Image segmentation, and Biogeography Based Optimization are studied in this paper and their precision, sensitivity, and specificity are evaluated.
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Qinbin He, Fangyue Chen · International Journal of Bifurcation and Chaos · 2006
Edge smoothing and noise removing for images are a common method of image processing. By designing CNN genes, edges can be smoothed and particles can be removed from a binary image. However, a satisfying result cannot be obtained by choosing only one CNN gene. In this paper, a group of edge smoothing and noise removing CNN genes is proposed as a synthetic disposal for a binary image. Disposed by the group of CNN genes, the characteristics of the original image can be preserved as much as possible. Two examples of edge smoothing and noise removing for a binary image are well illustrated by this method in this paper.
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Matko Bariŝić, Antonio Vasilijević, Đula Nađ · 2011
The paper deals with an image processing method for extracting the direction of the propagation of the upper Neptune grass (Posidonia oceanica ) bed boundary along the sea-bottom by the use of a monocular camera. The ultimate goal of research is to integrate this classifier into a feedback loop allowing an ROV to navigate the upper sea-grass bed border autonomously. This facilitates geo- referenced mapping of the border. The algorithm for extraction features four distinct phases: multi-resolution analysis using wavelets, vector quantization, post-processing of the obtained binary image and the extraction of the line parameters. The classification and line-fitting procedure are computationally optimized and made more robust by using weights in the Least Squares fitting procedure, and using nonlinear binary-image domain processing.
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Jun Fu, Zhi Meng Shi, Da Rong Dai et al. · Advanced materials research · 2013
A digital processing method based on the binary image in the Bayesian subspace and geometry vectorization algorithms is proposed to extract on the image of a cross-section of material interface. The actual microstructure image of emulsified asphalt cement concrete interface is processed and obtained as an example by application software based on the digital processing method, then the two-dimensional microstructure image is transferred into vectorization graphics CAD file.
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