Journal articles: 'Image processing. Computer vision'

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Mason, Scott. "Computer vision and image processing." ISPRS Journal of Photogrammetry and Remote Sensing 48, no. 2 (1993): 24–25. http://dx.doi.org/10.1016/0924-2716(93)90037-n.

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Pedrycz, Witold. "Computer vision and image processing." Fuzzy Sets and Systems 42, no. 3 (1991): 400. http://dx.doi.org/10.1016/0165-0114(91)90121-6.

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Defée, Irek. "Computer vision and image processing." Signal Processing 24, no. 2 (1991): 241. http://dx.doi.org/10.1016/0165-1684(91)90135-6.

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Nakajima, Masayuki, Shiniti Murakami, Katsuyuki Shinohara, Kunio Kondo, Kazumasa Enami, and Takehiro Kurono. "Image Processing and Computer Vision." Journal of the Institute of Television Engineers of Japan 48, no. 7 (1994): 828–33. http://dx.doi.org/10.3169/itej1978.48.828.

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Rakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov, and Dilnoza Boltabaevna Abdirimova. "Review And Analysis Of Computer Vision Algorithms." American Journal of Applied sciences 03, no. 05 (2021): 245–50. http://dx.doi.org/10.37547/tajas/volume03issue05-39.

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Computer vision as a scientific discipline refers to the theories and technologies for creating artificial systems that receive information from an image. Despite the fact that this discipline is quite young, its results have penetrated almost all areas of life. Computer vision is closely related to other practical fields like image processing, the input of which is two-dimensional images obtained from a camera or artificially created. This form of image transformation is aimed at noise suppression, filtering, color correction and image analysis, which allows you to directly obtain specific information from the processed image. This information may include searching for objects, keypoints, segments, and annexes;

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Horace H-S, Ip. "Digital image processing and computer vision." Image and Vision Computing 8, no. 3 (1990): 254. http://dx.doi.org/10.1016/0262-8856(90)90079-k.

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Wiley, Victor, and Thomas Lucas. "Computer Vision and Image Processing: A Paper Review." International Journal of Artificial Intelligence Research 2, no. 1 (2018): 22. http://dx.doi.org/10.29099/ijair.v2i1.42.

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Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.

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Gawande, Mohini. "Image Detection System Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5356–62. http://dx.doi.org/10.22214/ijraset.2021.36190.

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The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efﬁciently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

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Abdulhamid, Mohanad, and Lwanga Wanjira. "Image Processing Techniques Based Crowd Size Estimation." Radioelectronics. Nanosystems. Information Technologies 12, no. 3 (2020): 407–14. http://dx.doi.org/10.17725/rensit.2020.12.407.

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Image processing algorithms are the basis for image computer analysis and machine Vision. Employing a theoretical foundation, image algebra, and powerful development tools, Visual C++, Visual Fortran, Visual Basic, and Visual Java, high-level and efficient computer vision techniques have been developed. This paper analyzes different image processing algorithms by classifying them in logical groups. In addition, specific methods are presented illustrating the application of such techniques to the real world images. In most cases more than one method is used. This allows a basis for comparison of different methods as advantageous features as well as negative characteristics of each technique is delineated. The main objective of this paper is to use image processing techniques to estimate the size of a crowd from a still photograph. The simulation results show that the different images have different efficiencies.

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Hartley, M. G. "Book Review: Computer Vision and Image Processing." International Journal of Electrical Engineering Education 28, no. 2 (1991): 143. http://dx.doi.org/10.1177/002072099102800208.

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Fisher, Robert. "Dictionary of Computer Vision and Image Processing." Journal of Electronic Imaging 15, no. 1 (2006): 019902. http://dx.doi.org/10.1117/1.2179077.

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POWELL, MARK W., and DMITRY GOLDGOF. "SOFTWARE TOOLKIT FOR TEACHING IMAGE PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (2001): 833–44. http://dx.doi.org/10.1142/s0218001401001180.

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We introduce a software framework called the Java Vision Toolkit (JVT) for teaching image processing and computer vision. The toolkit provides over 50 image operations and presents them to the user in a GUI that can render grayscale, color and 3D range images. The software is written in Java, enabling it to be integrated into HTML documents and interactive course materials. The framework is designed for extensibility using a source code template that supports the implementation of any new operation with a minimal amount of supporting code. For students, this framework encapsulates the GUI, file I/O and other trivial programming details and allows them the maximum amount of time to spend on understanding computer vision. We compare the JVT with other computer vision software frameworks that are used for teaching and research. We also discuss the use of the JVT in an undergraduate image processing course at the University of South Florida.

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Oe, Shunichiro. "Special Issue on Vision." Journal of Robotics and Mechatronics 11, no. 2 (1999): 87. http://dx.doi.org/10.20965/jrm.1999.p0087.

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The widely used term <B>Computer Vision</B> applies to when computers are substituted for human visual information processing. As Real-world objects, except for characters, symbols, figures and photographs created by people, are 3-dimensional (3-D), their two-dimensional (2-D) images obtained by camera are produced by compressing 3-D information to 2-D. Many methods of 2-D image processing and pattern recognition have been developed and widely applied to industrial and medical processing, etc. Research work enabling computers to recognize 3-D objects by 3-D information extracted from 2-D images has been carried out in artificial intelligent robotics. Many techniques have been developed and some applied practically in scene analysis or 3-D measurement. These practical applications are based on image sensing, image processing, pattern recognition, image measurement, extraction of 3-D information, and image understanding. New techniques are constantly appearing. The title of this special issue is <B>Vision</B>, and it features 8 papers from basic computer vision theory to industrial applications. These papers include the following: Kohji Kamejima proposes a method to detect self-similarity in random image fields - the basis of human visual processing. Akio Nagasaka et al. developed a way to identify a real scene in real time using run-length encoding of video feature sequences. This technique will become a basis for active video recording and new robotic machine vision. Toshifumi Honda presents a method for visual inspection of solder joint by 3-D image analysis - a very important issue in the inspection of printed circuit boards. Saburo Okada et al. contribute a new technique on simultaneous measurement of shape and normal vector for specular objects. These methods are all useful for obtaining 3-D information. Masato Nakajima presents a human face identification method for security monitoring using 3-D gray-level information. Kenji Terada et al. propose a method of automatic counting passing people using image sensing. These two technologies are very useful in access control. Yoji. Ogawa presents a new image processing method for automatic welding in turbid water under a non-preparatory environment. Liu Wei et al. develop a method for detection and management of cutting-tool wear using visual sensors. We are certain that all of these papers will contribute greatly to the development of vision systems in robotics and mechatronics.

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Deshmukh, Omkar Madhukar. "Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1237–39. http://dx.doi.org/10.22214/ijraset.2021.35926.

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Computer vision may be a field of computer science that trains computers to interpret and perceive the visual world. exploitation digital pictures from cameras and videos and deep learning models, machines will accurately determine and classify objects — and so react to what they "see.”. Computer vision is Associate in Nursing knowledge domain scientific field that deals with however computers will gain high-level understanding from digital pictures or videos. From the angle of engineering, it seeks to grasp and alter tasks that the human sensory system will do. Computer vision tasks embrace strategies for exploit, processing, analyzing and understanding digital pictures, and extraction of high-dimensional knowledge from the important world so as to supply numerical or symbolic info, e.g. within the styles of selections. Understanding during this context suggests that the transformation of visual pictures (the input of the retina) into descriptions of the planet that be to thought processes and might elicit acceptable action. This image understanding will be seen because the disentangling of symbolic info from image knowledge mistreatment models created with the help of pure mathematics, physics, statistics, and learning theory.

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Zhou, Chao, Chuanheng Sun, Kai Lin, et al. "Handling Water Reflections for Computer Vision in Aquaculture." Transactions of the ASABE 61, no. 2 (2018): 469–79. http://dx.doi.org/10.13031/trans.12466.

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Abstract. In aquaculture, almost all images collected of an aquaculture scene contain reflections, which often affect the results and accuracy of machine vision. Classifying these images and obtaining images of interest are key to subsequent image processing. The purpose of this study was to identify useful images and remove images that had a substantial effect on the results of image processing for computer vision in aquaculture. In this study, a method for classification of reflective frames based on image texture and a support vector machine (SVM) was proposed for an actual aquaculture site. Objectives of this study were to: (1) develop an algorithm to improve the speed of the method and to ensure that the method has a high classification accuracy, (2) design an algorithm to improve the intelligence and adaptability of the classification, and (3) demonstrate the performance of the method. The results show that the average classification accuracy, false positive rate, and false negative rate for two types of reflective frames (type I and II) were 96.34%, 4.65%, and 2.23%, respectively. In addition, the running time was very low (1.25 s). This strategy also displayed considerable adaptability and could be used to obtain useful images or remove images that have substantial effects on the accuracy of image processing results, thereby improving the applicability of computer vision in aquaculture. Keywords: Aquaculture, Genetic algorithm, Gray level-gradient co-occurrence matrix, Principal component analysis, Reflection frame, Support vector machine.

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Purahong, B., V. Chaowalittawin, W. Krungseanmuang, P. Sathaporn, T. Anuwongpinit, and A. Lasakul. "Crack Detection of Eggshell using Image Processing and Computer Vision." Journal of Physics: Conference Series 2261, no. 1 (2022): 012021. http://dx.doi.org/10.1088/1742-6596/2261/1/012021.

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Abstract This article presents an eggshell crack inspection using image processing techniques. This approach uses the concept of industrial 4.0 to reduce manual coordination in the egg industry’s manufacturing process. The method started with receiving images from a webcam camera. Then, we rescaled the image to 1147 x 633 for faster computation. Next, divide the image into the red and green channels. The red channel image was converted to grayscale using a Gaussian blur filter with a kernel filter 11 x 11 to reduce noise, followed by turning the image to binary. After that, multiply the binary image with the grayscale of the green channel to remove the background. By that time, a morphological operation was used to enhance the quality of the image. Finally, use the contour matrix to find the area of the object and then build the condition to detect the crack in the eggshell. These techniques of image processing are used to inspect the eggshell crack with a high accuracy of more than 98% as well as the high performance of computing.

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DRAPER, BRUCE A., and J. ROSS BEVERIDGE. "TEACHING IMAGE COMPUTATION: FROM COMPUTER GRAPHICS TO COMPUTER VISION." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (2001): 823–31. http://dx.doi.org/10.1142/s0218001401001179.

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This paper describes a course in image computation that is designed to follow and build up an established course in computer graphics. The course is centered on images: how they are generated, manipulated, matched and symbolically described. It builds on the student's knowledge of coordinate systems and the perspective projection pipeline. It covers image generation techniques not covered by the computer graphics course, most notably ray tracing. It introduces students to basic image processing concepts such as Fourier analysis and then to basic computer vision topics such as principal components analysis, edge detection and symbolic feature matching. The goal is to prepare students for advanced work in either computer vision or computer graphics.

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Dee, Hannah, and Andrew French. "From image processing to computer vision: plant imaging grows up." Functional Plant Biology 42, no. 5 (2015): iii. http://dx.doi.org/10.1071/fpv42n5_fo.

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Image analysis is a field of research which, combined with novel methods of capturing images, can help to bridge the genotype–phenotype gap, where our understanding of the genotype has until now been leaps and bounds ahead of our ability to work with the phenotype. Methods of automating image capture in plant science research have increased in usage recently, as has the need to provide objective and highly accurate measures on large image datasets, thereby bringing the phenotype back to the centre of interest. In this special issue of Functional Plant Biology, we present some recent advances in the field of image analysis, and look at examples of different kinds of image processing and computer vision, which is occurring with increasing frequency in the plant sciences.

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Cao, Min. "Optimization of Plane Image Color Enhancement Based on Computer Vision." Wireless Communications and Mobile Computing 2022 (August 8, 2022): 1–8. http://dx.doi.org/10.1155/2022/3463222.

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In order to enhance the color effect of plane image, this paper presents a method of optimization of color enhancement processing of plane image based on computer vision technology. This method combines Retinex algorithm with adaptive two-dimensional empirical decomposition and decomposes the image to achieve the effect of image color enhancement. The experimental results show that the average value of the image processed by this method is increased by about 0.3. The variance increased by about 0.13. Information entropy increased by about 0.3. The definition is improved by about 0.02. Conclusion. The optimization method of color enhancement processing of plane graphics based on computer vision technology can effectively improve the color of plane images, which is of great significance for image processing.

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Holt, R. J., T. S. Huang, and A. N. Netravali. "Algebraic methods for image processing and computer vision." IEEE Transactions on Image Processing 5, no. 6 (1996): 976–86. http://dx.doi.org/10.1109/83.503913.

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Alvarez, Luis. "Computer Vision and Image Processing in Environmental Research." Systems Analysis Modelling Simulation 43, no. 9 (2003): 1229–42. http://dx.doi.org/10.1080/0232929032000115010.

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Sumathi, J. k. "Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework." Wasit Journal of Computer and Mathematics Science 1, no. 1 (2021): 1–8. http://dx.doi.org/10.31185/wjcm.vol1.iss1.3.

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The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platforms such as Facebook and Instagram led to an increase in the volume of image data that was being generated. Use of image and video processing software is a major concern for Facebook because the photos and videos that people post to the social network are doctored images. These kind of images are frequently cited as fake and used in malevolent ways such as motivating violence and death. You need to authenticate the questionable images before take action. It is very hard to ensure photo authenticity due to the power of photo manipulations. Image formation can be determined by image forensic techniques. The technique of image duplication is used to conceal missing areas.

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Saini, Preeti, and Mr Rohit Anand. "Identification of Defects in Plastic Gears Using Image Processing and Computer Vision : A Review." International Journal of Engineering Research 3, no. 2 (2014): 94–99. http://dx.doi.org/10.17950/ijer/v3s2/212.

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Chen, Xiang Wei, Zhi Kui Zhang, and Zhao Hui Liu. "Measurement of Surface Roughness by Computer Vision in Planning Operations." Advanced Materials Research 146-147 (October 2010): 361–65. http://dx.doi.org/10.4028/www.scientific.net/amr.146-147.361.

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Detection and Recognition of the surface roughness in the images is a topic which has received a lot of attention in the field of image processing. In this paper, a new non-contact measurement method of surface roughness, by texture analysis, is developed based on Charge Coupled Device (CCD) image in planning operations. The surface image of the workpiece is first acquired using the A102f CCD digital camera. The image captured will be converted to others kinds of images (Binary, and Gray scale) to be suitable for the detection algorithms used for the different types of surface. The main Image processing approaches is used such as Smoothing process, Noise reduction, Edge detection, Region Splitting, and Hough Transform etc. The predicted surface finish values using this measurement method are found to correlate well with the conventional stylus surface finish ( Ra ) values.

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Shi, Zizhou. "Research on image matching methods in computer vision." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 198–201. http://dx.doi.org/10.54097/hset.v23i.3267.

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Today, computer vision has shown a variety of roles in our lives, making people’s life more convenient. Also, a variety of artificial intelligence models and algorithms have emerged for computer vision. Image matching is an important technique in the field of computer vision to find the similarities between two images or multiple images with the help of matching algorithms to achieve scientific and accurate processing of images. Therefore, summarizing the effective approximate solution to this problem as well as the future research is the main part for current research. The paper firstly describes the basic elements of the image matching technique. Then, some representative traditional image matching algorithms proposed in the field of computer vision research in recent years are summarized and reviewed. Finally, the future research directions and research ideas of image matching are discussed, providing reliable guidance and reference for subsequent research.

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Duanmu, Zhengfang, Wentao Liu, Zhongling Wang, and Zhou Wang. "Quantifying Visual Image Quality: A Bayesian View." Annual Review of Vision Science 7, no. 1 (2021): 437–64. http://dx.doi.org/10.1146/annurev-vision-100419-120301.

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Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their quality as perceived by human observers. IQA modeling plays a special bridging role between vision science and engineering practice, both as a test-bed for vision theories and computational biovision models and as a powerful tool that could potentially have a profound impact on a broad range of image processing, computer vision, and computer graphics applications for design, optimization, and evaluation purposes. The growth of IQA research has accelerated over the past two decades. In this review, we present an overview of IQA methods from a Bayesian perspective, with the goals of unifying a wide spectrum of IQA approaches under a common framework and providing useful references to fundamental concepts accessible to vision scientists and image processing practitioners. We discuss the implications of the successes and limitations of modern IQA methods for biological vision and the prospect for vision science to inform the design of future artificial vision systems. (The detailed model taxonomy can be found at http://ivc.uwaterloo.ca/research/bayesianIQA/ .)

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Obaidat, Mohammed Taleb, Hashem R. Al-Masaeid, Fouad Gharaybeh, and Taisir S. Khedaywi. "An innovative digital image analysis approach to quantify the percentage of voids in mineral aggregates of bituminous mixtures." Canadian Journal of Civil Engineering 25, no. 6 (1998): 1041–49. http://dx.doi.org/10.1139/l98-034.

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The objective of this study was to examine the feasibility of using a semiautomated computer-vision system to quantify the percentage of voids in mineral aggregates (VMA%) of bituminous mixtures. The system used a hybrid procedure which utilized a digital image analysis scheme and a planimeter surveying instrument. Thirty-nine Marshall specimens were prepared using limestone and gravel aggregates. Values of VMA% were obtained using the ASTM conventional procedure and the computer-vision procedure. To compute VMA% using the computer-vision procedure, normal case photography with uniform scale images was used to map horizontal and vertical cross sections of Marshall specimens. Image domain measurements were corrected for distortion. Spatial filters and image processing operations were used to enhance the aggregate edges. Experimental results showed slight variations between VMA% computed using conventional and the computer-vision procedures. The average differences of VMA% between conventional and the computer-vision procedures were 0.81% and 0.006% for gravel and limestone specimens, respectively. Measurements of VMA% for limestone mixtures were more precise than those for gravel mixtures because of the angular edge shape of limestone particles. Variations in VMA% were due to the anisotropic properties of asphalt mixtures, aggregate distribution in the asphalt mixture, and different shapes of aggregates. Using the computer-vision-based technique, VMA% of horizontal and vertical cross sections were 50% consistent. The existence of fine aggregate in the asphalt mixture affected the accuracy potential of the developed system because a low-resolution camera was used. Increasing the camera resolution and automating the area computation of aggregate are expected to enhance the potential accuracy of the procedure. The proposed method for VMA quantification is anticipated to improve field quality control of hot-mix asphalt (HMA). The use of computer-vision technology with bituminous mixtures can open the doors to a wide variety of applications.Key words: bituminous mixtures, voids in mineral aggregate, computer vision, automation, image processing.

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Taha, Zahari, Jouh Yeong Chew, and Hwa Jen Yap. "Omnidirectional Vision for Mobile Robot Navigation." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (2010): 55–62. http://dx.doi.org/10.20965/jaciii.2010.p0055.

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Machine vision has been widely studied, leading to the discovery of many image-processing and identification techniques. Together with this, rapid advances in computer processing speed have triggered a growing need for vision sensor data and faster robot response. In considering omnidirectional camera use in machine vision, we have studied omnidirectional image features in depth to determine correlation between parameters and ways to flatten 3-dimensional images into 2 dimensions. We also discuss ways to process omnidirectional images based on their individual features.

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Qi, Xuanye. "Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/5951102.

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The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor–patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system.

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Tang, Li Fang, and Chuan Jin Wang. "Vision Control System of Pipe Welding Robot." Advanced Materials Research 756-759 (September 2013): 509–13. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.509.

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The author of this article designs a non-track automatic pipe welding robot, which mainly studies the image processing system of visual welding tracking. With the requirement of various interference noise and tracking accuracy in the welding process, this study adopts structure light CCD sensor checking system and image acquisition card processing images of computer software, in which sample filtering, edge checking, contour tracking, laser centerlines selection and checking of its characteristics. This processing method has the advantages of good effect and speedy processing that is able to meet the timely requirement of tracking system.

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Atkočiūnas, E., R. Blake, A. Juozapavičius, and M. Kazimianec. "Image Processing in Road Traffic Analysis." Nonlinear Analysis: Modelling and Control 10, no. 4 (2005): 315–32. http://dx.doi.org/10.15388/na.2005.10.4.15112.

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The article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application is utilizing image-processing and pattern recognition methods designed and modified to the needs and constrains of road traffic analysis. These methods combined together gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking, to measure the speed, and to recognize number plates of a car. Software developed was applied in and approved with video monitoring system, based on standard CCTV cameras connected to wide area network computers.

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CHENG, H. D., YANHUI GUO, and YINGTAO ZHANG. "A NOVEL APPROACH TO IMAGE THRESHOLDING BASED ON 2D HOMOGENEITY HISTOGRAM AND MAXIMUM FUZZY ENTROPY." New Mathematics and Natural Computation 07, no. 01 (2011): 105–33. http://dx.doi.org/10.1142/s1793005711001834.

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Image thresholding is an important topic for image processing, pattern recognition and computer vision. Fuzzy set theory has been successfully applied to many areas, and it is generally believed that image processing bears some fuzziness in nature. In this paper, we employ the newly proposed 2D homogeneity histogram (homogram) and the maximum fuzzy entropy principle to perform thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it not only can process "clean" images, but also can process images with different kinds of noises and images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding. It will be useful for applications in computer vision and image processing.

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Wang, Hai. "Research on the Real-Time Infrared Tracking Athletics Image Registration Based on Computer Vision." Advanced Materials Research 791-793 (September 2013): 2002–6. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2002.

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with the development of computer hardware, computer vision technology has been applied to the engineering field. Through the computer vision technology, the image track registration algorithm is conducted in-depth research, and based on the images rigid body transformation, affine, projection and linear superposition method, this paper is the reconstruction of wavelet algorithm and program. This paper uses the general image processing software MATLAB to carry on image processing for the track and field sports, and then the computer vision infrared tracking image registration is numerically simulated, we find that the standard deviation of wavelet reconstruction algorithm achieves 72.3258, which is maximum in four algorithms, entropy and joint entropy respectively reach 5.2332 and 6.2369, after pixel reaching 800, coincidence degree is still maintained at more than 90%. Through the fusion degree of image and the standard deviation of gray entropy, it can be seen that the image registration is achieved good effect.

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Kun Gao, Wang Qin, and Lifeng Xi. "Rendering Cloud for Processing Computer Vision, Graphics and Image." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 5 (2012): 274–82. http://dx.doi.org/10.4156/aiss.vol4.issue5.33.

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BHANDARKAR, SUCHENDRA M., HAMID R. ARABNIA, and JEFFREY W. SMITH. "A RECONFIGURABLE ARCHITECTURE FOR IMAGE PROCESSING AND COMPUTER VISION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 02 (1995): 201–29. http://dx.doi.org/10.1142/s0218001495000110.

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In this paper we describe a reconfigurable architecture for image processing and computer vision based on a multi-ring network which we call a Reconfigurable Multi-Ring System (RMRS). We describe the reconfiguration switch for the RMRS and also describe its VLSI implementation. The RMRS topology is shown to be regular and scalable and hence well-suited for VLSI implementation. We prove some important properties of the RMRS topology and show that a broad class of algorithms for the n-cube can be mapped to the RMRS in a simple and elegant manner. We design and analyze a class of procedural primitives for the SIMD RMRS and show how these primitives can be used as building blocks for more complex parallel operations. We demonstrate the usefulness of the RMRS for problems in image processing and computer vision by considering two important operations—the Fast Fourier Transform (FFT) and the Hough transform for detection of linear features in an image. Parallel algorithms for the FFT and the Hough transform on the SIMD RMRS are designed using the aforementioned procedural primitives. The analysis of the complexity of these algorithms shows that the SIMD RMRS is a viable architecture for problems in computer vision and image processing.

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Xavier Falcão, Alexandre. "20th SIBGRAPI: Advances in Image Processing and Computer Vision." Pattern Recognition Letters 31, no. 4 (2010): 267. http://dx.doi.org/10.1016/j.patrec.2009.12.011.

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Puyda, Volodymyr. "Computer vision system for research in the area of defectoscopy for materials and products." Computer systems and network 4, no. 1 (2022): 122–30. http://dx.doi.org/10.23939/csn2022.01.122.

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In many cases, visual and optical methods can be used in defectoscopy for different materials and products. With the development of microprocessor components and significant expansion of usage of computer technologies and image processing and analysis techniques in different areas, the use of visual and optical methods in defectoscopy for production and research purposes is rapidly developing. In this paper, the author proposes a computer vision system for experiments and research in the area of studying defects of materials and products. The system uses modern methods of image processing and object identification based on their images. The system allows to install the object so that it can be rotated horizontally, take high-quality images of the object using a digital video camera, pre- process images to enhance image quality using a local computing module, transfer images to the main computing module to identify defects and make decisions about rejection of the material or product. To install and rotate the material or product, the author uses the stepper motor 17HS4401 and a horizontal platform fixed on the vertical axis. The stepper motor is controlled using Microstep Driver TB6600 and a local computing module based on a microcontroller with an ARM Cortex-M7 core. The video stream is recorded using a USB microscope video camera which provides sufficiently high image resolution allowing to find defects on the object surface of size 50 micron and larger. Rotation speed can be controlled using a local computing module. The input data for the local computing module can be provided in the form of a video stream or a sequence of images. The local computing module has an LCD screen based on the ВС1602А indicator, programmable LEDs, a keyboard to select operating modes for the stepper motor, a USB port to connect the microscope video camera and an SWD port to program the Flash memory and debug the firmware in real time. Original images or the images after quality enhancement are passed to the main computing module using the SPI interface. The author has developed software for the local computing module to control the stepper motor, record a video stream or series of images of the object area with possible defects, quality enhancement and passing the video stream or images to the main computing module for further processing and analysis. The results can be used in scientific research and in development of automated systems for non-destructive defectoscopy for materials and end products.

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Jiao, Yarong. "Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision." Journal of Sensors 2022 (September 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/3654743.

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As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent.

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Choudhury, Amitava, Snehanshu Pal, Ruchira Naskar, and Amitava Basumallick. "Computer vision approach for phase identification from steel microstructure." Engineering Computations 36, no. 6 (2019): 1913–33. http://dx.doi.org/10.1108/ec-11-2018-0498.

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PurposeThe purpose of this paper is to develop an automated phase segmentation model from complex microstructure. The mechanical and physical properties of metals and alloys are influenced by their microstructure, and therefore the investigation of microstructure is essential. Coexistence of random or sometimes patterned distribution of different microstructural features such as phase, grains and defects makes microstructure highly complex, and accordingly identification or recognition of individual phase, grains and defects within a microstructure is difficult.Design/methodology/approachIn this perspective, computer vision and image processing techniques are effective to help in understanding and proper interpretation of microscopic image. Microstructure-based image processing mainly focuses on image segmentation, boundary detection and grain size approximation. In this paper, a new approach is presented for automated phase segmentation from 2D microstructure images. The benefit of the proposed work is to identify dominated phase from complex microstructure images. The proposed model is trained and tested with 373 different ultra-high carbon steel (UHCS) microscopic images.FindingsIn this paper, Sobel and Watershed transformation algorithms are used for identification of dominating phases, and deep learning model has been used for identification of phase class from microstructural images.Originality/valueFor the first time, the authors have implemented edge detection followed by watershed segmentation and deep learning (convolutional neural network) to identify phases of UHCS microstructure.

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Rosenfeld, A. "Image Analysis and Computer Vision: 1992." Computer Vision and Image Understanding 58, no. 1 (1993): 85–135. http://dx.doi.org/10.1006/cviu.1993.1034.

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Rosenfeld, A. "Image Analysis and Computer Vision: 1993." Computer Vision and Image Understanding 59, no. 3 (1994): 367–404. http://dx.doi.org/10.1006/cviu.1994.1030.

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Rosenfeld, Azriel. "Image Analysis and Computer Vision: 1994." Computer Vision and Image Understanding 62, no. 1 (1995): 90–143. http://dx.doi.org/10.1006/cviu.1995.1044.

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Rosenfeld, Azriel. "Image Analysis and Computer Vision: 1995." Computer Vision and Image Understanding 63, no. 3 (1996): 568–612. http://dx.doi.org/10.1006/cviu.1996.0041.

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Rosenfeld, Azriel. "Image Analysis and Computer Vision: 1996." Computer Vision and Image Understanding 66, no. 1 (1997): 33–93. http://dx.doi.org/10.1006/cviu.1997.0602.

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Rosenfeld, Azriel. "Image Analysis and Computer Vision: 1997." Computer Vision and Image Understanding 70, no. 2 (1998): 239–84. http://dx.doi.org/10.1006/cviu.1998.0697.

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Rosenfeld, Azriel. "Image Analysis and Computer Vision: 1998." Computer Vision and Image Understanding 74, no. 1 (1999): 36–95. http://dx.doi.org/10.1006/cviu.1999.0746.

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Rosenfeld, Azriel. "Image Analysis and Computer Vision: 1999." Computer Vision and Image Understanding 78, no. 2 (2000): 222–302. http://dx.doi.org/10.1006/cviu.2000.0835.

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LIONS, PIERRE-LOUIS. "AXIOMATIC DERIVATION OF IMAGE PROCESSING MODELS." Mathematical Models and Methods in Applied Sciences 04, no. 04 (1994): 467–75. http://dx.doi.org/10.1142/s0218202594000261.

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We briefly review the derivation due to Alvarez, Guichard, Morel and the author of mathematical models in Image Processing. We deduce from classical axions in Computer Vision some nonlinear partial differential equations of evolution type that correspond to general multi-scale analysis (scale-space). We also obtain specific nonlinear models that satisfy additional invariances which are relevant for the analysis of images.

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Al Smadi, Takialddin. "Modern Technology for Image processing and Computer vision -A Review." Journal of advanced Sciences and Engineering Technologies 1, no. 2 (2018): 17–23. http://dx.doi.org/10.32441/jaset.v1i2.178.

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This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality. © 2018 JASET, International Scholars and Researchers Association

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Silva, Ewerton, Ricardo da S. Torres, Allan Pinto, et al. "Application-Oriented Retinal Image Models for Computer Vision." Sensors 20, no. 13 (2020): 3746. http://dx.doi.org/10.3390/s20133746.

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Energy and storage restrictions are relevant variables that software applications should be concerned about when running in low-power environments. In particular, computer vision (CV) applications exemplify well that concern, since conventional uniform image sensors typically capture large amounts of data to be further handled by the appropriate CV algorithms. Moreover, much of the acquired data are often redundant and outside of the application’s interest, which leads to unnecessary processing and energy spending. In the literature, techniques for sensing and re-sampling images in non-uniform fashions have emerged to cope with these problems. In this study, we propose Application-Oriented Retinal Image Models that define a space-variant configuration of uniform images and contemplate requirements of energy consumption and storage footprints for CV applications. We hypothesize that our models might decrease energy consumption in CV tasks. Moreover, we show how to create the models and validate their use in a face detection/recognition application, evidencing the compromise between storage, energy, and accuracy.

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Journal articles on the topic 'Image processing. Computer vision'

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