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image processing algorithms in the assessment of grain damage degree

BIO-algorithms AND MED-SYSTEMS 2022年第1期18卷 17-28页

作者： Maliga, Wojciech Dudzinski, Wlodzimierz Labowska, Magdalena Detyna, Jerzy Lopusiewicz, Marcin Bujak, Henryk Wroclaw Univ Sci & Technol Dept Mech Mat & Biomed Engn Smoluchowskiego 25 PL-50372 Wroclaw Poland Wroclaw Univ Sci & Technol Dept Mat Sci Welding & Strength Mat Wroclaw Poland Wroclaw Univ Environm & Life Sci Dept Genet Plant Breeding & Seed Prod Wroclaw Poland Res Ctr Cultivar Testing Slupia Wielka Poland

Objectives: The paper presents preliminary results on the assessment of algorithms used in image processing of the grain damage degree. The purpose of the work is developing a tool allowing to analyse sample cross-sections of rye germs. Methods: The analysis of the grain cross-sections was carried out on the basis of a series their photos taken at equal time intervals at a set depth. The cross-sections will be used to create additional virtual cross-sections allowing to analyse the whole sample volume. The ultimate plan is to generate two cross-sections perpendicular to each other. Based on volumetric data read from the sample section, a three-dimensional model of an object will be generated. Results: The analysis of model surface will allowed us to detect possible grain damage. The developed method of preparing the research material and the proprietary application allowed for the identification of internal defects in the biological material (cereal grains). Conclusions: The presented methodology may be used in the agri-food industry in the future. However, much research remains to be done. These works should primarily aim at significantly reducing the time-consuming nature of individual stages, as well as improving the quality of the reconstructed image.

关键词： 3D surface models grain damage image recognition algorithms seeds infection surface mapping

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Descriptive Models of Information Transformation Processes in image Analysis

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PATTERN recognition AND image ANALYSIS 2021年第3期31卷 402-420页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is devoted to the basic models of descriptive image analysis, which is the leading section of the modern mathematical theory of image analysis and recognition. The fundamental problem that the subject of the article addresses is automated extraction of information from images necessary for making intelligent decisions. Descriptive analysis envisages the implementation of image analysis processes in the image formalization space, the elements of which are various forms (states, phases) of the image representation, which is converted from the original form to one convenient for recognition (i.e., to a model), and data representation conversion models. The image analysis processes are considered to be sequences of transformations implemented in the phase space and providing the construction of phase states of the image, which together form the phase trajectory of image transfer from the original form to the model. The study of the image formalization space leads to formalization of the concepts of representation/image model, as well as to construction of models of the image recognition and analysis processes and formulation of mathematical statements of image recognition and analysis problems. Two types of image analysis models are considered: (1) models reflecting the methodology and mathematical foundations of image recognition and analysis the problem statement, used mathematical and heuristic methods, algorithmic content of the process: (a) a model based on reverse algebraic closure, (b) a model based on the property of equivalence of images of the same class, (c) a model based on multiple image models and multiple classifiers;(2) models characterizing the architecture and structure of the recognition process: (a) a multilevel model for combining algorithms and initial data for image recognition, (b) information structure for generating descriptive algorithmic image recognition schemes. A brief description of the above models is given. A comparative an

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated extraction of information from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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Descriptive image Analysis: Part IV. Information Structure for Generating Descriptive Algorithmic Schemes for image recognition

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PATTERN recognition AND image ANALYSIS 2020年第4期30卷 638-654页

作者： Gurevich, I. B. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia

This article is the fourth in a series of publications devoted to the state of the art and prospects for developing Descriptive image Analysis, one of the leading and intensively developing fields of modern mathematical image analysis theory. The fundamental problem discussed in the article is to automate information extraction from images necessary for making intelligent decisions. This study is devoted to regularizing the generation of descriptive algorithmic image analysis and recognition schemes. The main result is the definition of a new mathematical structure with the following functional capabilities: 1) solution of an image recognition problem in a given formulation, with given initial data and a scenario that determines the sequence of application of information processing procedures and their iterative loops;2) construction of descriptive algorithmic schemes for solving a problem with given initial data in the absence of a given scenario;in the case of a successful solution, the fixation of the sequence of procedures and information processing loops that yielded its solution governs the corresponding descriptive algorithmic schemes and scenarios that can be further used to solve the corresponding class of image recognition problems;3) comparative analysis and optimization of methods for solving image recognition problems via their realization as descriptive algorithmic schemes and scenarios allowed by the structure. The introduced structure is a tool for representing and implementing information processing while solving an image recognition problem for arbitrary formulations, scenarios, models, and solution methods;it can also emulate any descriptive algorithmic scheme and combinations thereof, which are used and generated when solving an image recognition problem. The introduced structure is interpreted as a fundamental model for generating and emulating image recognition procedures. A type characteristic of the introduced information structure for genera

关键词： image analysis descriptive image analysis mathematical theory of image analysis automated information extraction from images image recognition descriptive algorithmic schemes models of image analysis procedures image representations image models image recognition algorithms mathematical structures information structures formal systems

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Automated alignment system for optical wireless communication systems using image recognition

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OPTICS LETTERS 2014年第13期39卷 4045-4048页

作者： Brandl, Paul Weiss, Alexander Zimmermann, Horst Vienna Univ Technol Inst Electrodynam Microwave & Circuit Engn A-1040 Vienna Austria

In this Letter, we describe the realization of a tracked line-of-sight optical wireless communication system for indoor data distribution. We built a laser-based transmitter with adaptive focus and ray steering by a microelectromechanical systems mirror. To execute the alignment procedure, we used a CMOS image sensor at the transmitter side and developed an algorithm for image recognition to localize the receiver's position. The receiver is based on a self-developed optoelectronic integrated chip with low requirements on the receiver optics to make the system economically attractive. With this system, we were able to set up the communication link automatically without any back channel and to perform error-free (bit error rate < 10(-9)) data transmission over a distance of 3.5 m with a data rate of 3 Gbit/s. (C) 2014 Optical Society of America

关键词： image recognition image recognition algorithms Laser beams Microelectromechanical systems Optical testing Optical wireless communication

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COMPARISON OF ARTIFICIAL NEURAL NETWORKS WITH PATTERN-recognition AND image-PROCESSING

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APPLIED OPTICS 1989年第2期28卷 302-305页

作者： JAU, JY FAINMAN, Y LEE, SH []The authors are with University of California San Diego Department of Electrical Computer Engineering La Jolla California 92093.

This paper presents a comparison between the field of artificial neural network and the field of image processing and pattern recognition. It shows that some of the adaptive processing algorithms for pattern recognition and image processing, in terms of neural networks, can be seen as adaptive heteroassociative and autoassociative memories, respectively. The similarities and differences between these two fields are addressed.

关键词： image processing image processing algorithms image recognition algorithms image restoration Neural networks Pattern recognition

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Rapid identification of the variety of maize seeds based on near-infrared spectroscopy coupled with locally linear embedding

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APPLIED OPTICS 2022年第7期61卷 1704-1710页

作者： Liu, Shu Chen, Zhengguang Jiao, Feng Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Peoples R China Heilongjiang Bayi Agr Univ Coll Agr Daqing 163319 Peoples R China

Maize is the main cereal crop in China. In the process of maize planting, the selection of suitable maize varieties is an important link to achieving a high yield. Because the appearance of maize seeds is very similar, it is difficult to accurately identify their species with the naked eye. In order to realize the rapid identification of different varieties of maize seeds, this paper proposes a rapid identification method of maize varieties based on near-infrared (NIR) spectroscopy coupled with locally linear embedding (LLE) and a support vector machine (SVM). The MR data, preprocessed by multiple scattering correction (MSC), were dimensionally reduced by LLE, a principal component analysis (PCA), and isometric mapping (Isomap), and combined with SVM to establish a maize variety identification model. The results show that the LLE-SVM model has the best performance, whose classification accuracy and kappa coefficient of the test set can reach 100% and 1.00. The classification accuracy and kappa coefficient of the LLE-SVM model are better than the PCA-SVM model and Isomap-SVM model. Therefore, LLE can reduce the complexity of the model and improve the accuracy of the model. It can be used for the rapid identification of maize varieties and provide a new idea for the classification and identification of other agricultural products. (C) 2022 Optica Publishing Group

关键词： Face recognition Fourier transforms Genetic algorithms image recognition algorithms Multiple scattering Near infrared spectroscopy

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Optoelectronic region of interest detection: an application in automated cytology

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APPLIED OPTICS 1998年第25期37卷 6011-6025页

作者： Narayanswamy, R Johnson, KM Univ Colorado Ctr Optoelect Comp Syst Boulder CO 80309 USA

Diagnostic cytology, which is used to screen for cervical cancer, involves characterizing cellular features such as shape, size, and texture. Automated screening of cervical smear slides is desirable but computationally challenging since each slide requires processing 2 x 10(9) pixels at a resolution of 0.8 mu m per pixel. We demonstrate that the throughput of optical processors can be exploited in automated cervical smear-screening systems. In particular, we identify a morphological shape detector to perform the initial region of interest (ROT) detection and to demonstrate experimentally its optoelectronic implementation. The ROI detector is tested on 200 images, and its performance is characterized as a receiver operating characteristic (ROC). The area under the ROC curve is as high as 96.4% of the total area. The simulation and the experimental results are found comparable, and the discrepancy between the two results is determined to be a function of the number of bits represented in the filter plane device. (C) 1998 Optical Society of America.

关键词： Binary phase only filters Diode lasers image processing image recognition algorithms image sharpening Optical processing devices

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OPTICAL IMPLEMENTATION OF NEURAL NETWORKS FOR FACE recognition BY THE USE OF NONLINEAR JOINT TRANSFORM CORRELATORS

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APPLIED OPTICS 1995年第20期34卷 3950-3962页

作者： JAVIDI, B LI, J TANG, Q []The authors are with the Department of Electrical Systems Engineering University of Connecticut Storrs Connecticut 06269.

We describe a nonlinear joint transform correlator-based two-layer neural network that uses a supervised learning algorithm for real-time face recognition. The system is trained with a sequence of facial images and is able to classify an input face image in real time. Computer simulations and optical experimental results are presented. The processor can be manufactured into a compact low-cost optoelectronic system. The use of the nonlinear joint transform correlator provides good noise robustness and good image discrimination.

关键词： Face recognition image recognition algorithms Joint transform correlators Neural network pattern recognition Neural networks Volume holography

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Demonstration of shift, scale, and rotation invariant target recognition using the hybrid opto-electronic correlator

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OPTICS EXPRESS 2019年第12期27卷 16507-16520页

作者： Gamboa, Julian Fouda, Mohamed Shahriar, Selim M. Northwestern Univ Dept EECS Evanston IL 60208 USA Northwestern Univ Dept Phys & Astron Evanston IL 60208 USA

Previously, we had proposed a hybrid opto-electronic correlator (HOC), which can achieve the same functionality as that of a holographic optical correlator but without using any holographic medium. Here, we demonstrate experimentally that the HOC is capable of detecting objects in a scale, rotation, and shift invariant manner. First, the polar Mellin transformed (PMT) versions of two images are produced, using a combination of optical and electronic signal processing. The PMT images are then used as the reference and the query inputs for the HOC. The observed correlation signal is used to infer, with high accuracy, the relative scale and angular orientation of the original images. We also discuss practical constraints in reaching a high-speed implementation of such a system. In addition, we describe how these challenges may be overcome for producing an automated version of such a correlator. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Application specific integrated circuits Diode pumped lasers image processing image recognition algorithms Joint transform correlators Optical components

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Hybrid sensing face detection and registration for low-light and unconstrained conditions

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APPLIED OPTICS 2018年第1期57卷 69-78页

作者： Zhou, Mingyuan Lin, Haiting Young, S. Susan Yu, Jingyi Univ Delaware Newark DE 19716 USA US Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA

The capability to track, detect, and identify human targets in highly cluttered scenes under extreme conditions, such as in complete darkness or on the battlefield, has been one of the primary tactical advantages in military operations. In this paper, we propose a new collaborative, multi-spectrum sensing method to achieve face detection and registration under low-light and unconstrained conditions. We design and prototype a novel type of hybrid sensor by combining a pair of near-infrared (NIR) cameras and a thermal camera (a long-wave infrared camera). We strategically surround each NIR sensor with a ring of LED IR flashes to capture the "red-eye," or more precisely, the "bright-eye" effect of the target. The "bright-eyes" are used to localize the 3D position of eyes and face. The recovered 3D information is further used to warp the thermal face imagery to a frontal-parallel pose so that additional tasks, such as face recognition, can be reliably conducted, especially with the assistance of accurate eye locations. Experiments on real face images are provided to demonstrate the merit of our method. (C) 2017 Optical Society of America

关键词： Computational imaging Face recognition image detection systems image recognition algorithms Infrared radiation Visible light

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