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Journal articles on the topic 'Computational intelligence. Image analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Rymarczyk, Tomasz. "LEVEL SETS AND COMPUTATIONAL INTELLIGENCE ALGORITHMS TO MEDICAL IMAGE ANALYSIS IN E-MEDICUS SYSTEM." Informatics Control Measurement in Economy and Environment Protection 7, no. 1 (2017): 63–67. http://dx.doi.org/10.5604/01.3001.0010.4585.

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In this work, there were implemented methods to analyze and segmentation medical images by using topological, statistical algorithms and artificial intelligence techniques. The solution shows the architecture of the system collecting and analyzing data. There was tried to develop an algorithm for level set method (LSM) applied to piecewise constant image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. The image segmentation refers to the process of partitioning a digital image into multiple regions. There is typically used to locat

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CHUBB, CHARLIE, YOSHI INAGAKI, C. COTMAN, B. CUMMINGS, and P. C. Y. SHEU. "SEMANTIC BIOLOGICAL IMAGE MANAGEMENT AND ANALYSIS." International Journal on Artificial Intelligence Tools 13, no. 04 (2004): 881–96. http://dx.doi.org/10.1142/s0218213004001879.

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Most commercially available image retrieval systems are so generic that they are not specialized to handle biological images and the feature domains associated with them. In molecular biology, neurobiology and cellular biology, for example, the recognition, classification and retrieval of distinct cellular features is a critically needed tool representing a computational problem that embodies the central challenges facing biological image database research. It often requires the consideration of expert/conceptual knowledge of images and the objects contained within such images. This paper disc

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Et. al., Akshay Kapoor,. "Disease Identification System using Image Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 115–23. http://dx.doi.org/10.17762/turcomat.v12i1s.1582.

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Artificial Intelligence (AI) and Sensor Technologies making a point-of-care delivery possible which was considered impossible. The human body shows early indicators of disease manifestation before actual clinical symptoms appear. These indicators can be picked up by analyzing the optical face image, which can be used for rapid and cost-effective screening of life-threatening health conditions. This article focuses on how health status can be tracked using optical image analytics. Early identification of disease plays a vital role in therapeutics of a patient. In accordance with review of secon

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Hirschberg, Cosima, Magnus Edinger, Else Holmfred, Jukka Rantanen, and Johan Boetker. "Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality." Pharmaceutics 12, no. 9 (2020): 877. http://dx.doi.org/10.3390/pharmaceutics12090877.

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Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The ima

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Bhowmik, Anirban, Joydeep Dey, Arindam Sarkar, and Sunil Karforma. "Computational Intelligence based Lossless Regeneration (CILR) of Blocked Gingivitis Intraoral Image Transportation." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (2019): 197. http://dx.doi.org/10.11591/ijai.v8.i3.pp197-204.

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<p>This paper presented that an intraoral image has been wrapped during wireless transportation with an encryption tool with an added essence of lossless regeneration property. Threshold based cryptographic transportation has provided the construction of reliable and robust medical data communication system. The accumulation of threshold shares only would result to the formation of the intraoral gingivitis image at the receivers’ end. The proposed technique dealt with the generation of n number of partial shares by creating a unique frame structure by the dentist / physician. Additional

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Luther, Kurt. "Human Computation for Image and Video Analysis." AI Magazine 39, no. 4 (2018): 67–68. http://dx.doi.org/10.1609/aimag.v39i4.2825.

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The second Workshop on Human Computation for Image and Video Analysis (GroupSight) took place in Quebec City, Canada on October 24, 2017, as part of the fifth AAAI Conference on Human Computation and Crowdsourcing (HCOMP). The aim of this workshop was to promote greater interaction between researchers and practitioners who examine how to mix human and computer efforts to convert visual data into discoveries and innovations that benefit society.

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Liu, Jiming, and Y. Y. Tang. "Distributed Autonomous Agents for Chinese Document Image Segmentation." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 01 (1998): 97–118. http://dx.doi.org/10.1142/s0218001498000087.

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In Chinese document image processing, text and/or graphical block detection serves as an essential step in document layout analysis that in turn permits the effective reasoning about the logical relationships among various text paragraphs and graphical entities for the purpose of document understanding. This paper presents a novel computational paradigm for extracting text/graphic blocks from Chinese document images, which is based on a notion of distributed autonomous agents. The primary features of the agents lie in that they are (1) adaptive to the locality of given images and hence efficie

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Chavat, Juan, Sergio Nesmachnow, Andrei Tchernykh, and Vladimir Shepelev. "Active Safety System for Urban Environments with Detecting Harmful Pedestrian Movement Patterns Using Computational Intelligence." Applied Sciences 10, no. 24 (2020): 9021. http://dx.doi.org/10.3390/app10249021.

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This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge

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Chunduru, Pranathi, Joanna Phillips, and Annette Molinaro. "PATH-31. ARTIFICIAL INTELLIGENCE IN GLIOMA PATHOLOGY IMAGE ANALYSIS FOR RISK PREDICTION." Neuro-Oncology 22, Supplement_2 (2020): ii171. http://dx.doi.org/10.1093/neuonc/noaa215.712.

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Abstract Pathological evaluation of tumor tissue images stained with hematoxylin and eosin (H&E) is pivotal in diagnosis and predictive of outcome, yet only a small fraction of the rich phenotypic information on the slide is currently used for clinical care. In this study, we developed a computational approach based on deep learning to predict overall survival within distinct molecular subtypes of glioma patients and to extract prognostic biomarkers from microscopic images of tissue biopsies. Whole-slide images from 766 unique patients [IDH: 336 IDH-wildtype, 364 IDH-mutant, 1p/19q: 142 1p

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Nandhini Abirami, R., P. M. Durai Raj Vincent, Kathiravan Srinivasan, Usman Tariq, and Chuan-Yu Chang. "Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis." Complexity 2021 (April 15, 2021): 1–30. http://dx.doi.org/10.1155/2021/5541134.

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Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of de

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Yan, Xuguo, Long Wen, and Liang Gao. "A Fast and Effective Image Preprocessing Method for Hot Round Steel Surface." Mathematical Problems in Engineering 2019 (October 29, 2019): 1–14. http://dx.doi.org/10.1155/2019/9457826.

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With the development of computer vision technology, more and more enterprises begin to use computer vision instead of manual inspection for steel surface defect detection. However, classical image processing methods often face great difficulties when dealing with images containing noise and distortions, which leads to low computational efficiency and poor accuracy of detection. In view of the particularity of hot round steel production, a computational intelligence method is proposed in this paper. On the basis of preliminary image preprocessing, we combine the improved PCA with genetic algori

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PANIN, GIORGIO. "FAST, MULTI-MODAL AND DISCONTINUITY-PRESERVING IMAGE REGISTRATION USING MUTUAL INFORMATION." International Journal on Artificial Intelligence Tools 22, no. 06 (2013): 1360015. http://dx.doi.org/10.1142/s0218213013600154.

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In this paper, we describe a fast and efficient method for multi-modal and discontinuity-preserving image registration, implemented on graphics hardware. Multi-sensory data fusion and medical image analysis often pose the challenging task of aligning dense, non-rigid and multi-modal images. However, also optical sequences or stereo image pairs may present variable illumination conditions and noise. The above problems can be addressed by an invariant similarity measure, such as mutual information. Additionally, when using a regularized approach to deal with the ill-posedness of the problem, one

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Hashizume, Makoto. "Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence." Cyborg and Bionic Systems 2021 (January 8, 2021): 1–3. http://dx.doi.org/10.34133/2021/9160478.

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Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of “dynamic living human anatomy.” It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extendi

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Hashizume, Makoto. "Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence." Cyborg and Bionic Systems 2021 (January 8, 2021): 1–3. http://dx.doi.org/10.34133/2021/9160478.

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Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of “dynamic living human anatomy.” It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extendi

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Nahar, Jesmin, Tasadduq Imam, Kevin S. Tickle, A. B. M. Shawkat Ali, and Yi-Ping Phoebe Chen. "Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer." Expert Systems with Applications 39, no. 16 (2012): 12371–77. http://dx.doi.org/10.1016/j.eswa.2012.04.045.

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TANIMOTO, STEVEN L., and RUSS MILLER. "MESH ALGORITHMS FOR FINDING REPETITIONS AND PARTIAL SYMMETRIES IN ARRAYS." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 02 (1994): 465–83. http://dx.doi.org/10.1142/s0218001494000231.

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The two-dimensional mesh computer architecture has proven to be an appropriate means to apply parallel computation to problems in image processing. However, this is most often done using local-neighbourhood operations to accomplish image filtering and morphological transformations. The discovery of structures in an image such as repetitions and symmetries is another form of visual analysis, and yet relatively little has been done to apply mesh computers to this problem. In this paper, we apply the primitive operations of prefix scanning and sorting to efficiently implement a repetition finding

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Filisbino, Tiene A., Gilson A. Giraldi, and Carlos Thomaz. "Ranking Tensor Subspaces in Weighted Multilinear Principal Component Analysis." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 07 (2017): 1751003. http://dx.doi.org/10.1142/s021800141751003x.

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Multilinear principal component analysis (MPCA) has been applied for tensor decomposition and dimensionality reduction in image databases modeled through higher order tensors. Despite the well-known attractive properties of MPCA, the traditional approach does not incorporate prior information in order to steer its subspace computation. In this paper, we propose a method to explicitly incorporate such semantics in the MPCA framework to allow an automatic selective treatment of the variables that compose the patterns of interest. The method relies on spatial weights calculated, in this work, by

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Li, Hao. "Edge color difference detection of color image based on artificial intelligence technology." Journal of Computational Methods in Sciences and Engineering 21, no. 3 (2021): 787–802. http://dx.doi.org/10.3233/jcm-215189.

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In order to solve the problems of the traditional methods in detecting color image edge chromatic aberration, such as the poor accuracy of detection and the poor detection effect, a color image edge chromatic aberration detection method based on artificial intelligence technology is proposed. The approximate principal component analysis method is used to segment the color image and smooth the image denoising; The linear gray-scale transformation is applied to the color image to enlarge the smaller gray-scale space to the larger gray-scale space according to the linear relationship and obtain t

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Watt, Roger. "A Computational Examination of Image Segmentation and the Initial Stages of Human Vision." Perception 23, no. 4 (1994): 383–98. http://dx.doi.org/10.1068/p230383.

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A novel approach to the question of image segmentation is considered. Instead of relying on edge-detection mechanisms to encircle the image of an object, it is proposed that the general Gestalt-like properties of images of objects can be used. These manifest themselves as particularly simple properties at relatively coarse spatial scales when the image is filtered with orientation-selective filters. The first part of the paper is concerned with a computational analysis of this proposal. A key issue is the question of how the information in filtered images is extracted. A simple primitive param

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Alexandrov, Theodore. "Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence." Annual Review of Biomedical Data Science 3, no. 1 (2020): 61–87. http://dx.doi.org/10.1146/annurev-biodatasci-011420-031537.

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Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology—imaging mass spectrometry—generate big hyperspectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the sam

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Martí-Bonmatí, Luis. "Estimates of Causality with Medical Image in Oncology." ANALES RANM 138, no. 138(01) (2021): 16–23. http://dx.doi.org/10.32440/ar.2021.138.01.rev02.

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This work defines a research on data strategy focused on medical imaging and derived image biomarkers to critically assess the concept of causal inference and uncertainties. Computational observational studies will be valued to generate casual inference from real world data. Our main goal is to propose a scientific methodology that allows to estimate causalities from observational studies through quality control of large databases, definition of plausible hypotheses, using computational estimated models and artificial intelligence tools. The computational approach of radiology to precision med

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Agudelo, Gerardo Ernesto Rolong, Carlos Enrique Montenegro Marin, and Paulo Alonso Gaona-Garcia. "Computational Model to Support the Detection of Profiles of Missing Person in Colombia." Inteligencia Artificial 24, no. 67 (2021): 121–28. http://dx.doi.org/10.4114/intartif.vol24iss67pp121-128.

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In the world and some countries like Colombia, the number of missing person is a phenome very worrying and growing, every year, thousands of people are reported missing all over the world, the fact that this keeps happening might indicate that there are still analyses that have not been done and tools that have not been considered in order to find patterns in the information of missing person. The present article presents a study of the way informatics and computational tools can be used to help find missing person and what patterns can be found in missing person datasets using as a study case

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Bacanin, Nebojsa, Timea Bezdan, Eva Tuba, Ivana Strumberger, and Milan Tuba. "Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics." Algorithms 13, no. 3 (2020): 67. http://dx.doi.org/10.3390/a13030067.

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Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming pr

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Baz, Mohammed, Hatem Zaini, Hala S. El-sayed, Matokah AbuAlNaja, Heba M. El-Hoseny, and Osama S. Faragallah. "Utilization of Artificial Intelligence in Medical Image Analysis for COVID-19 Patients Detection." Intelligent Automation & Soft Computing 29, no. 3 (2021): 97–111. http://dx.doi.org/10.32604/iasc.2021.018265.

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Kumar, Subbiahpillai Neelakantapillai, Alfred Lenin Fred, and Paul Sebastin Varghese. "An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images." Journal of Intelligent Systems 29, no. 1 (2018): 612–25. http://dx.doi.org/10.1515/jisys-2017-0629.

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Abstract Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentatio

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Tellez, David, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi. "Neural Image Compression for Gigapixel Histopathology Image Analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 2 (2021): 567–78. http://dx.doi.org/10.1109/tpami.2019.2936841.

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WANG, DENGWEI, TIANXU ZHANG, MEIJUN WAN, WENJUN SHI, LONGSHENG WEI, and XIAOYU YANG. "BUILDING RECOGNITION BASED ON INDIRECT LOCATION OF PLANAR LANDMARK IN FLIR IMAGE SEQUENCES." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 03 (2011): 431–48. http://dx.doi.org/10.1142/s0218001411008695.

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A novel method is proposed to deal with the problem of building recognition in forward-looking infrared (FLIR) image sequences. Two computational models are deduced in this paper: one is the perspective transformation model, through which an image is transformed perspectively from downward-looking state to forward-looking state; the other is the indirect location model, through which the position of a building is computed in an FLIR image. In addition, in order to illustrate the application scope of our method, the error analysis is presented. The proposed approach is validated by extensive ex

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Vachkov, Gancho. "Human-Assisted Fuzzy Image Similarity Analysis Based on Information Compression." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (2009): 255–61. http://dx.doi.org/10.20965/jaciii.2009.p0255.

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The fuzzy similarity analysis we propose in this paper is used for unsupervised image classification. We introduce a special growing unsupervised learning algorithm for information compression (granulation) of the original “raw data” (the RGB pixels) of an image with a smaller number of neurons (information granules). Two important parameters are extracted from each image, namely the center of gravity (COG) and the model volume of the image, taken as the number of neurons obtained from information compression. These two features are used as inputs for special fuzzy inference for numerically ca

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Estrela, Vania V. "Content-Based Image Retrieval (CBIR) in Big Histological Image Databases." Medical Technologies Journal 4, no. 3 (2020): 581–82. http://dx.doi.org/10.26415/2572-004x-vol4iss3p581-582.

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Background: Automatic analysis of Histopathological Images (HIs) demands image processing and Computational Intelligence (CI) techniques. Both Computer-Aided Diagnosis (CAD) and Content-Based Image-Retrieval (CBIR) systems assist diagnosis, disease discovery, and biological decision-making. Classical tests comprise screening examinations and biopsy. Histopathology slides offer more ample diagnosis data. However, manual examination of microscopic images is labor-intensive and time-consuming and may depend on a subjective assessment by the pathologist, which can be a challenge. Methods: Th

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Manoj Prakash, P., Sreerag Premanathan, ShivamKumar Surwase, M. S. Antony Vigil, and Shivam Bohare. "License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3246–51. http://dx.doi.org/10.1166/jctn.2019.8171.

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Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CC

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Ao, Jun, and Chunbo Ma. "Adaptive Stretching Method for Underwater Image Color Correction." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (2017): 1854001. http://dx.doi.org/10.1142/s0218001418540010.

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The physical properties of water lead to attenuation of light that travels through the water channel. The attenuation is dependent on the color spectrum wavelength, that results in low contrast and color cast in image acquisition. Several methods have been proposed to handle these problems, such as Linear Stretching, Histogram Equalization (HE) and their variants. Considering the advantages of HE and Linear Stretching, this paper presents a new Adaptive Linear Stretch method (ALS) which can efficiently improve the subjective impression of the traditional Linear Stretching and keep the computat

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Haralick, Robert M., Stanley R. Sternberg, and Xinhua Zhuang. "Image Analysis Using Mathematical Morphology." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 4 (1987): 532–50. http://dx.doi.org/10.1109/tpami.1987.4767941.

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Khan, Talha Ali, and Sai Ho Ling. "Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications." Algorithms 12, no. 5 (2019): 88. http://dx.doi.org/10.3390/a12050088.

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Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper,

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Bhattacharjee, Subrata, Cho-Hee Kim, Deekshitha Prakash, Hyeon-Gyun Park, Nam-Hoon Cho, and Heung-Kook Choi. "An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis." Applied Sciences 10, no. 22 (2020): 8013. http://dx.doi.org/10.3390/app10228013.

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Prostate carcinoma is caused when cells and glands in the prostate change their shape and size from normal to abnormal. Typically, the pathologist’s goal is to classify the staining slides and differentiate normal from abnormal tissue. In the present study, we used a computational approach to classify images and features of benign and malignant tissues using artificial intelligence (AI) techniques. Here, we introduce two lightweight convolutional neural network (CNN) architectures and an ensemble machine learning (EML) method for image and feature classification, respectively. Moreover, the cl

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Jamil, Uzma, and Shehzad Khalid. "Analysis of Valuable Techniques and Algorithms Used in Automated Skin Lesion Recognition Systems." International Journal of Privacy and Health Information Management 3, no. 2 (2015): 92–107. http://dx.doi.org/10.4018/ijphim.2015070106.

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Application of computational intelligence techniques helps physicians as well as dermatologists in faster data process to give better and more reliable diagnoses. The whole system is categorized as: Pre-processing the lesion image to enhance its readability, Segmentation of the Lesion from skin, Feature extraction, selection, and finally the identification of dermoscopic images. Pros and cons of various methods are focused to provide a help for the researchers starting work in automated lesion detection system. Numerous computerized diagnostic systems have been reported in which different bord

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Ahmad, Ijaz, Inam Ullah, Wali Ullah Khan, et al. "Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem." Journal of Healthcare Engineering 2021 (May 27, 2021): 1–16. http://dx.doi.org/10.1155/2021/9500304.

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Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical serv

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Appati, Justice Kwame, Kennedy Yaw Lodonu, and Richmond Chris-Koka. "A Review of Image Analysis Techniques for Adult Content Detection." International Journal of Software Innovation 9, no. 2 (2021): 102–21. http://dx.doi.org/10.4018/ijsi.2021040106.

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The fast growth of internet access globally without boundary has led to some negative impacts among children who are exposed to pornographic contents daily. Many parental control strategies have been put in place to protect these children; however, these strategies are usually inspired by political and social interventions. With the availability of computational tools, many automated explicit content detection methods though having their flaws have been proposed to support these social interventions. In this study, a review of the current automated adult content detectors is presented with ope

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Kawamura, Satoru, Masashi Komori, and Yusuke Miyamoto. "Smiling Reduces Masculinity: Principal Component Analysis Applied to Facial Images." Perception 37, no. 11 (2008): 1637–48. http://dx.doi.org/10.1068/p5811.

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We examined the effect of facial expression on the assignment of gender to facial images. A computational analysis of the facial images was applied to examine whether physical aspects of the face itself induced this effect. Thirty-six observers rated the degree of masculinity of the faces of 48 men, and the degree of femininity of the faces of 48 women. Half of the faces had a neutral facial expression, and the other half was smiling. Smiling significantly reduced the perceived masculinity of men's faces, especially for male observers, whereas no effect of smiling on femininity ratings was obt

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Samiappan, Dhanalakshmi, S. Latha, T. Rama Rao, Deepak Verma, and CSA Sriharsha. "Enhancing Machine Learning Aptitude Using Significant Cluster Identification for Augmented Image Refining." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (2019): 2051009. http://dx.doi.org/10.1142/s021800142051009x.

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Enhancing the image to remove noise, preserving the useful features and edges are the most important tasks in image analysis. In this paper, Significant Cluster Identification for Maximum Edge Preservation (SCI-MEP), which works in parallel with clustering algorithms and improved efficiency of the machine learning aptitude, is proposed. Affinity propagation (AP) is a base method to obtain clusters from a learnt dictionary, with an adaptive window selection, which are then refined using SCI-MEP to preserve the semantic components of the image. Since only the significant clusters are worked upon

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LASENBY, JOAN, and EDUARDO BAYRO-CORROCHANO. "ANALYSIS AND COMPUTATION OF PROJECTIVE INVARIANTS FROM MULTIPLE VIEWS IN THE GEOMETRIC ALGEBRA FRAMEWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 08 (1999): 1105–21. http://dx.doi.org/10.1142/s0218001499000628.

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A central task of computer vision is to automatically recognize objects in real-world scenes. The parameters defining image and object spaces can vary due to lighting conditions, camera calibration and viewing positions. It is therefore desirable to look for geometric properties of the object which remain invariant under such changes. In this paper we present geometric algebra as a complete framework for the theory and computation of projective invariants formed from points and lines in computer vision. We will look at the formation of 3D projective invariants from multiple images, show how th

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VIJAYALAKSHMI, K., and V. VIJAY KUMAR. "Performance Analysis of Remote Sensing Application using Area Wise Prediction." Oriental journal of computer science and technology 12, no. 1 (2019): 21–27. http://dx.doi.org/10.13005/ojcst12.01.05.

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Remote sensors from the Satellite or Aircrafts are generated by huge volume of data which can utilize for impending signification if collected data aggregated effectively incorporates by insight information. Data is collection from simple to hybrid devices, which are continuously working for technology around us and communicate with each other. These devices are transferring huge amounts of real time data daily. The transaction added to the synchronized inaccessible sensing data that is retrieving the useful information in the proficient way of classification in the direction of the severe com

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Cardoner, Rafael, and Federico Thomas. "Efficient Morphological Set Transformations on Line Drawings." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 06 (1997): 947–59. http://dx.doi.org/10.1142/s0218001497000433.

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Image compression techniques have been recently used not only for reducing storage requirements, but also computational costs when processing images on low cost computers. This approach might be also of interest for processing large engineering drawings, where feature extraction techniques must be intensively applied for their segmentation into regions of interest for subsequent analysis. This paper explores this alternative using a simple run-length compression, leading to excellent results. Although this approach is not new and can be classified within the decomposition paradigm used since t

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Wang, Lei, Jinhai Sun, and Tuojian Li. "Intelligent sports feature recognition system based on texture feature extraction and SVM parameter selection." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 4847–58. http://dx.doi.org/10.3233/jifs-179970.

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Feature extraction is the basis of texture analysis. How to obtain texture features with small feature dimension, simple calculation and comprehensive representation of images is a hot spot and a difficult point in feature extraction. The traditional image texture feature extraction method is to process the image in the spatial domain. However, due to its high computational complexity, its practical application is restricted. Based on this, this study studies the extraction method of texture features, and deeply analyzes the principle of non-subsampled Contourlet transform. Moreover, this stud

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Jiang, Yu, Hua Zhu, Reza Malekian, and Cong Ding. "An improved quantitative recurrence analysis using artificial intelligence based image processing applied to sensor measurements." Concurrency and Computation: Practice and Experience 31, no. 10 (2018): e4858. http://dx.doi.org/10.1002/cpe.4858.

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Baggenstoss, P. M. "Image distortion analysis using polynomial series expansion." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 11 (2004): 1438–51. http://dx.doi.org/10.1109/tpami.2004.106.

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Braga-Neto, U., and J. Goutsias. "Object-based image analysis using multiscale connectivity." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 6 (2005): 892–907. http://dx.doi.org/10.1109/tpami.2005.124.

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Kim, Sang-Yup, and Seong-Whan Lee. "Gray-Scale Nonlinear Shape Normalization Method for Handwritten Oriental Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 01 (1998): 81–95. http://dx.doi.org/10.1142/s0218001498000075.

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In general, nonlinear shape normalization methods for binary images have been used in order to compensate for the shape distortions of handwritten characters. However, in most document image analysis and recognition systems, a gray-scale image is first captured and digitized using a scanner or a video camera, then a binary image is extracted from the original gray-scale image using a certain extraction technique. This binarization process may remove some useful information of character images such as topological features, and introduce noises to character background. These errors are accumulat

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Hoang, Nhat-Duc, and Quoc-Lam Nguyen. "A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine." Advances in Civil Engineering 2020 (July 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/4190682.

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To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. Image texture computations employ the Gabor filter and gray-level run lengths to characterize the condition of a concrete surface. Based on features of image texture, Support Vector Machines (SVM) establish a decision boundary that separates collected image

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Wang, Yingying, Yibin Li, Yong Song, and Xuewen Rong. "Facial Expression Recognition Based on Auxiliary Models." Algorithms 12, no. 11 (2019): 227. http://dx.doi.org/10.3390/a12110227.

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In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of t

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WATTANACHON, UREERAT, and CHIDCHANOK LURSINSAP. "SPSM: A NEW HYBRID DATA CLUSTERING ALGORITHM FOR NONLINEAR DATA ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 08 (2009): 1701–37. http://dx.doi.org/10.1142/s0218001409007685.

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Existing clustering algorithms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on predefined parameters specified by users. These algorithms may be unsuccessful if the choice of parameters is inappropriate with respect to the data set being clustered. Most of these algorithms work very well for compact and hyper-spherical clusters. In this paper, a new hybrid clustering algorithm called Self-Partition and Self-Merging (SPSM) is proposed. The SPSM algorithm partitions the input data set into several subclusters in the first phase and, then, removes th

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