Samaneh Kouchaki, Avraam Tapinos, David L Robertson · bioRxiv (Cold Spring Harbor Laboratory) · 2016
ABSTRACT Bioinformatics methods typically use textual representations of genetic information, represented computationally as strings or sub-strings of the characters A, T, G and C. Image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. We introduce a method, multi-resolution local binary patterns (MLBP) from image processing to extract local ‘texture’ changes from nucleotide sequence data. We apply this feature space to the alignment-free binning of metagenomic data. The effectiveness of MLBP is demonstrated using both simulated and real human gut microbial communities. The intuition behind our method is the MLBP feature vectors permit sequence comparisons without the need for explicit pairwise matching. Sequence reads or contigs can then be represented as vectors and their ‘texture’ compared efficiently using state-of-the-art machine learning algorithms to perform dimensionality reduction to capture eigengenome information and perform clustering (here using RSVD and BH-tSNE). We demonstrate this approach outperforms existing methods based on k -mer frequency. The image processing method, MLBP, thus offers a viable alternative feature space to textual representations of sequence data. The source code for our Multi-resolution Genomic Binary Patterns method can be found at https://github.com/skouchaki/MrGBP .
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Yao Rao, K. Rajitha, D. Vijay Harsha Vardhan et al. · 2025
An efficient processing element was used to design the data/image processing. The mesh-connected and ring-connected systolic arrays are created by the detailed communication networks, circuit blocks and instruction sets are created for the retrieving and learning phases of the neural network operations. Therefore, a parallel Morphological Image Processor (MIP) has been developed onto a full-custom optoelectronic Very Large Scale Integration (VLSI) designed by combining an on-chip focal-plane photo detectors and transmitters with 2-dimensional fine-grain parallel array architectures. The opto-detected binary image is processed by the optical I/O array processor to perform morphological functions using the programmable structuring element of size. MIPL is a specific language and defined for parallel morphological image processing, this is fully supported by MIP hardware. Grayscale images and binary images play a main role in the image processing field for the applications of machine intelligence, object recognition, authentication, tracking, and document analysis. This paper proposed, the image processing functions and algorithms can be implemented in a reconfigurable processing module for an image processor using application-specific integrated circuits (ASICs) for each algorithm. The reconfigurable module performs basic morphological mathematical operations on grayscale and binary images, as well as certain image processing algorithms. Field programmable gate arrays used to implement the reconfiguration module and compared to existing systems. Delay, speed, power consumption, and memory space were the performance parameters used to examine the model's performance.
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Claudio Lezcano, José Luis Vázquez Noguera, Diego P. Pinto-Roa et al. · Heliyon · 2020
In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image, in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.
Cited by 5Open access
Nur Azizah Eka Budiarti, Kurnia Wahyu Prima, Carli Apriansyah Hutagalung · Journal of Embedded Systems Security and Intelligent Systems · 2025
The automatic identification of fruit dimensions and morphology is a major requirement in agricultural and food technology. This research formulates a technique for fruit separation by employing mathematical morphology on binary images to identify the attributes of fruit shape and size. The dataset has 100 fruit photos featuring diverse geometric designs. The image processing procedure commences with grayscale conversion, followed by threshold adjustment, and culminates in the use of morphological operations to refine the image. The area and perimeter characteristics of the objects are extracted and utilized to categorize the fruit forms into circles or ellipses, triangles, and squares or rectangles. Segmentation accuracy was evaluated using the Intersection over Union (IoU) metric, yielding an average result of 85.67%, which signifies a high degree of segmentation precision. This study's findings affirm that mathematical morphology techniques are exceptionally effective in the automated detection of fruit size and form, with the potential to enhance efficiency in agricultural crop sorting processes.
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Emerson Carlos Pedrino · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2003
A morfologia matemática é o estudo da forma utilizando as ferramentas da teoria de conjuntos e representa uma área extremamente importante em análise de imagens. Suas operações básicas são a dilatação e a erosão, e através destas é possível realizar outras operações mais complexas. A morfologia matemática fornece ferramentas poderosas para a realização de análise de imagens em baixo nível e tem encontrado aplicações em diversas áreas, tais como: visão robótica, inspeção visual, medicina, análise de textura, entre outras. Muitas destas aplicações requerem processamento em tempo real, e para sua execução de forma eficiente freqüentemente é utilizado hardware dedicado. A análise de imagens em baixo nível geralmente envolve computações repetidas sobre estruturas grandes de dados. Assim, o paralelismo parece ser um atributo necessário de um sistema de hardware capaz de executar eficientemente estas tarefas. As ferramentas da morfologia matemática são bem adequadas à implementação em arquiteturas pipeline. A necessidade de sistemas capazes de realizar o processamento de imagens digitais em tempo real, com o menor custo e tempo de desenvolvimento, tem sido suprida pela tecnologia de dispositivos de lógica programável complexa. Assim, neste trabalho foi projetada e implementada uma arquitetura pipeline dedicada para dilatação e erosão de imagens binárias em tempo real utilizando dispositivos lógicos programáveis de alta capacidade. Esta arquitetura é capaz de processar imagens binárias de 512 x 512 pixels. Os estágios desta arquitetura são flexíveis, permitindo a reprogramação da forma e do tamanho dos elementos estruturantes utilizados nas operações morfológicas. A arquitetura desenvolvida apresentou um desempenho satisfatório, demonstrando ser uma alternativa viável e eficiente.
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Hangkai Zhong · Applied and Computational Engineering · 2024
Aiming at the problem of no suitable measurement method for the angle between a product's upper and lower cylinder axes in a specific horizontal rotation position, a calculation method based on the Cognex vision system for automatic angle measurement was proposed. This algorithm uses binary image processing technology to reduce interference in product implementation caused by variations in surface roughness and resulting inconsistencies in the reflection effect. The ability to perform robust image feature searching is thereby built upon. It utilizes edge tools to locate the points on either side of a cylindrical product and compute the axial coordinate for averaging the measured axes at these positions to determine each product elements upper and lower axial angle measurement. Simulation results show that this algorithm utilizes binary image processing to effectively filter product differences, which plays a role in capturing product image features continuously and reliably. The edge tool based on feature location can accurately locate product edges and complete target angle calculations. It has certain production field application capabilities regarding image processing effectiveness and computational logic accuracy.
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Sabbu, Sravya, Salepela, Prasuna Reddy, Talluri, Yamini Hanisha et al. · DigitalCommons - Kennesaw State University (Kennesaw State University) · 2021
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Fathimath Suhara, Mary Joseph · AIP conference proceedings · 2024
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Tri Bui-Dinh, Tae‐Sun Choi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
The bubble rise velocity in air/water two-phase flow in a vertical pipe is studied experimentally by processing consecutive series of digitized video images. The shape of the bubble, as well as its instantaneous velocity is measured by using binary image processing techniques. Digital image processing algorithms have been developed to obtain the coordinates of specified points in image. This coordinate data was used to calculate the instantaneous bubble velocity, which can be expressed as a separation distance between the two consecutive image frames. This method has many advantages it is a non-invasive measurement, and does not require sophisticated laboratory equipment. Images are directly digitized by using CCD digital camera. Image analysis involves purely computer-based computation. Hence reasonably accurate velocity data has been obtained from only one experiment in a small period of time.
Cited by 3
Philippe Salembier, Luís Garrido · IEEE Transactions on Image Processing · 2000
This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.
Cited by 577Open access
Salembier Clairon, Philippe Jean, Garrido, L · RECERCAT (Consorci de Serveis Universitaris de Catalunya)
This paper discusses the interest of binary partition\ntrees as a region-oriented image representation. Binary partition\ntrees concentrate in a compact and structured representation a set\nof meaningful regions that can be extracted from an image. They\noffer a multiscale representation of the image and define a translation\ninvariant 2-connectivity rule among regions. As shown in this\npaper, this representation can be used for a large number of processing\ngoals such as filtering, segmentation, information retrieval\nand visual browsing. Furthermore, the processing of the tree representation\nleads to very efficient algorithms. Finally, for some applications,\nit may be interesting to compute the binary partition tree\nonce and to store it for subsequent use for various applications. In\nthis context, the last section of the paper will show that the amount\nof bits necessary to encode a binary partition tree remains moderate.
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Andrea G. Fabbri · 1982
Cited by 1
Farina Riaz, Shahab Abdulla, Hajime Suzuki et al. · Journal of Data Science and Intelligent Systems · 2024
Over the past few years, there has been significant interest in quantum machine learning (QML) among researchers, as it has the potential to transform the field of machine learning. Several models that exploit the properties of quantum mechanics have been developed for practical applications. In this study, we investigated the application of our previously proposed quantum pre-processing filter (QPF) to binary image classification. We evaluated the QPF on four datasets: MNIST (handwritten digits), EMNIST (handwritten digits and alphabets), CIFAR-10 (photographic images), and GTSRB (real-life traffic sign images). Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98.9% to 99.2%, 97.8% to 98.3%, and 71.2% to 76.1%, respectively, but degraded it against GTSRB from 93.5% to 92.0%. We then applied QPF in cases using a smaller number of training and testing samples, i.e., 80 and 20 samples per class, respectively. In order to derive statistically stable results, we conducted the experiment with 100 trials choosing randomly different training and testing samples and averaging the results. The result showed that the application of QPF did not improve the image classification accuracy against MNIST and EMNIST but improved it against CIFAR-10 and GTSRB from 65.8% to 67.2% and 90.5% to 91.8%, respectively. Further research will be conducted as part of future work to investigate the potential of QPF to assess the scalability of the proposed approach to larger and complex datasets. Received: 2 September 2024 | Revised: 15 October 2024 | Accepted: 4 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/hajimesuzuki999/qpf-bic. Author Contribution Statement Farina Riaz: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Shahab Abdulla: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision. Hajime Suzuki: Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Srinjoy Ganguly: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Ravinesh C. Deo: Investigation, Resources, Data curation, Writing – review & editing, Supervision. Susan Hopkins: Writing – review & editing, Supervision.
Cited by 2Open access
Inoue, Katsunori · OUKA (Osaka University Knowledge Archive) (Osaka University)
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Sébastien Marcel, Yann Rodriguez, Guillaume Heusch · 2008
This paper presents a survey on the recent use of Local Binary Patterns (LBPs) for face recognition. LBP is becoming a popular technique for face representation. It is a non-parametric kernel which summarizes the local spacial structure of an image and it is invariant to monotonic gray-scale transformations. This is a very interesting property in face recognition. In this paper, we describe the LBP technique and different approaches proposed in the literature to represent and to recognize faces. The most representatives are considered for experimental comparison on a common face authentication task. For that purpose, the XM2VTS and BANCA databases are used according to their respective experimental protocols.
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Meihua Bao, Siyu Guo, Qiu Tang et al. · 2009
Skeleton is an important shape property and has a variety of applications. The skeletonization implementation provided in the Matlab image processing toolbox is often used, but its execution speed is not satisfactory. The implementation is introduced and analyzed in the paper, and performance bottlenecks are pointed out. Optimizations are proposed for all these bottlenecks. As the result of the optimization, a single Matlab function is realized for skeleton computation. Results of experiments on a test image set show that the optimizations are effective, and the speed performance of the new function is considerably higher than that of the function provided in the Matlab image processing toolbox.
Cited by 20
Cheng Li, Xi Gao, Steven Rowan et al. · 2021
The binary fluidization of Geldart-D type non-spherical wood particles and spherical LDPE particles was investigated in a laboratory-scale bed. The experiment was performed for varying static bed height, wood particles count, as well as superficial gas velocity. The LDPE velocity field were quantified using Particle Image Velocimetry (PIV). The wood particles orientation and velocity are measured using Particle Tracking Velocimetry (PTV). A machine learning pixel-wise classification model was trained and applied to acquire wood and LDPE particle masks for PIV and PTV processing, respectively. The results show significant differences in the fluidization behavior between LDPE only case and binary fluidization case. The effects of wood particles on the slugging frequency, mean, and variation of bed height, and characteristics of the particle velocities/orientations were quantified and compared. This comprehensive experimental dataset serves as a benchmark for validating numerical models.
Cited by 1Open access
Penghao Chen (4111465), Haoran Sun (486019), Ziyu Ming (19208154) et al. · Figshare · 2025
Recent advancements in in-sensor computing technology have demonstrated significant advantages in time latency and energy efficiency in visual information processing through device-level integration of photosensing and neuromorphic computing. However, current implementations face challenges due to their single-layer architecture, creating an urgent demand for the development of devices that integrate front-end in-sensor processing with back-end computing layers. Here, we report a programmable graphene/Si Schottky diode (PGSSD) featuring gate-voltage-programmed photoresponsivity and rectification direction. The programmability of the photoresponsivity enables the application of reconfigurable convolution kernels to implement in-sensor convolution of optical images. Simultaneously, the programmable rectification direction permits analog-domain execution of quasi-binary multiply-accumulate (MAC) operations. Based on these capabilities, we constructed a complete binary neural network (BNN) using the PGSSDs and demonstrated its application for image recognition. The BNN combines front-end convolution processing and back-end computing layers, achieving an inference accuracy of 98.35% on the MNIST database.
Cited by 0Open access
Leo Ramos, Israel Pineda · SN Computer Science · 2024
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S. I. Vyatkin · Optoelectronics Instrumentation and Data Processing · 2014
This paper deals with the real-time synthesis of high-quality images, a method of defining free forms without approximating them with polygons or patches, issues of using perturbation functions for real-time animation of the surfaces of three-dimensional objects. A method for visualizing functionally defined objects adapted for graphics processing units is proposed, and methods of transforming the describing function are implemented for geometric operations: deformation, displacement and metamorphosis, or morphing, including those for nonhomeomorphic objects. Advantages of the method of defining surfaces to the existing methods (algebraic and Bezier methods and their visualization techniques) are shown.
Cited by 15