Relevant bibliographies by topics / Pixel classifier

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Pixel classifier'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Pixel classifier"

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PIATER, JUSTUS H., EDWARD M. RISEMAN, and PAUL E. UTGOFF. "INTERACTIVELY TRAINING PIXEL CLASSIFIERS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (1999): 171–93. http://dx.doi.org/10.1142/s0218001499000112.

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For typical classification tasks, all training data are prepared in advance and are supplied to the classifier all at once. This is unnecessarily expensive and incurs overfitting problems, since the individual contributions of the training instances to the classifier are not known. We address this by proposing an interactive incremental framework for image classifier construction, where small numbers of training examples are supplied at each user interaction. After incorporating new training instances, the classifier immediately reclassifies the image to provide the user with instant feedback.
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Paillassa, M., E. Bertin, and H. Bouy. "MAXIMASK and MAXITRACK: Two new tools for identifying contaminants in astronomical images using convolutional neural networks." Astronomy & Astrophysics 634 (February 2020): A48. http://dx.doi.org/10.1051/0004-6361/201936345.

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In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are able to identify various contaminants, such as cosmic rays, hot and bad pixels, persistence effects, satellite or plane trails, residual fringe patterns, nebulous features, saturated pixels, diffraction spikes, and tracking errors in images. They encompass a broad range of ambient conditions, such as seeing, image sampling, detector type, optics, and stellar density. The first classifier, MAXIMASK, performs semantic segmentation and generate
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Nguyen-Trang, Thao. "A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm." Mathematical Problems in Engineering 2018 (August 6, 2018): 1–10. http://dx.doi.org/10.1155/2018/5754604.

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Skin detection is an interesting problem in image processing and is an important preprocessing step for further techniques like face detection, objectionable image detection, etc. However, its performance has not really been high because of the high overlapped degree between “skin” and “nonskin” pixels. This paper proposes a new approach to improve the skin detection performance using the Bayesian classifier and connected component algorithm. Specifically, the Bayesian classifier is utilized to identify “true skin” pixels using the first posterior probability threshold, which is approximate to
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Momeni, Ehsan, Mahmoud Reza Sahebi, and Ali Mohammadzadeh. "CLASSIFICATION OF HIGH-RESOLUTION SATELLITE IMAGES USING FUZZY LOGICS INTO DECISION TREE." Malaysian Journal of Geosciences 4, no. 1 (2020): 07–12. http://dx.doi.org/10.26480/mjg.01.2020.07.12.

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In this paper, DTFL an image classifier based on Decision Tree and Fuzzy Logics is proposed. At the beginning of classification using DTFL, each pixel is located at the highest level of a decision tree where it belongs to the combination of all classes. DTFL transfers a pixel to a lower level of the decision tree where the pixel belongs to a combination of fewer classes. Decision-making about transfers is based on fuzzy logic with seven different membership functions including triangular-shaped, trapezoidal-shaped, π-shaped, bell-shaped, Gaussian, differential S-shaped and multiplicative S-sha
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PALUBINSKAS, GINTAUTAS. "A COMPARATIVE STUDY OF DECISION MAKING ALGORITHMS IN IMAGES MODELED BY GAUSSIAN MARKOV RANDOM FIELDS." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 04 (1988): 621–39. http://dx.doi.org/10.1142/s021800148800039x.

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In this work the performance and computer time requirements of 15 classifiers are compared in images modeled by two-dimensional Gaussian Markov random fields which are represented by a causal autoregressive model of the second order. The per-pixel classifier and the object classifier directly or indirectly utilizing spectral-spatial characteristies of images are among them. The probability of misclassification (PMC) calculated analytically and experimentally on modeled data was used as a measure of a classifier performance. The influence of such factors as the object size and form, the inadequ
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Lukin, Vladimir, Galina Proskura, and Irina Vasilieva. "COMPARISON OF ALGORITHMS FOR CONTROLLED PIXEL-BY-PIXEL CLASSIFICATION OF NOISY MULTICHANNEL IMAGES." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (December 25, 2019): 39–46. http://dx.doi.org/10.32620/reks.2019.4.04.

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The subject of this study is the pixel-by-pixel controlled classification of multichannel satellite images distorted by additive white Gaussian noise. The paper aim is to study the effectiveness of various methods of image classification in a wide range of signal-to-noise ratios; an F-measure is used as a criterion for recognition efficiency. It is a harmonic mean of accuracy and completeness: accuracy shows how much of the objects identified by the classifier as positive are positive; completeness shows how much of the positive objects were allocated by the classifier. Tasks: generate random
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Ranefall, Petter, Lars Egevad, Bo Nordin, and Ewert Bengtsson. "A New Method for Segmentation of Colour Images Applied to Immunohistochemically Stained Cell Nuclei." Analytical Cellular Pathology 15, no. 3 (1997): 145–56. http://dx.doi.org/10.1155/1997/304073.

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A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a new supervised algorithm for creating a pixel classifier is applied to an image that is typical for the sample. The training phase of the classifier is very user friendly since only a few typical pixels for each class need to be selected. The classifier is robust in that it is non‐parametric and has a built‐in metric that adapts to the colour
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Kumar, Amit, and Shivani Malhotra. "Pixel-Based Skin Color Classifier: A Review." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 7 (2015): 283–90. http://dx.doi.org/10.14257/ijsip.2015.8.7.27.

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Sengar, S. S., S. K. Ghosh, A. Kumar, and H. Chaudhary. "LANDSLIDE IDENTIFICATION FROM IRS-P6 LISS-IV TEMPORAL DATA-A COMPARATIVE STUDY USING FUZZY BASED CLASSIFIERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 461–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-461-2018.

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<p><strong>Abstract.</strong> While extracting land cover from remote sensing images, each pixel in the image is allocated to one of the possible class. In reality different land covers within a pixel can be found due to continuum of variation in landscape and intrinsic mixed nature of most classes. Mixed pixels may not be appropriately processed by traditional image classifiers, which assume that pixels are pure. The existence of mixed pixels led to the development of several approaches for soft (often termed fuzzy in the remote sensing literature) classification in which ea
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Serwa, Ahmed. "POTENTIALITY OF USING DIGITAL WAVELET/QMF PYRAMIDS IN REMOTELY SENSED SATELLITES’ IMAGES CLASSIFICATION." Geodesy and cartography 46, no. 4 (2020): 163–69. http://dx.doi.org/10.3846/gac.2020.11415.

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Wavelet or quadrature mirror filter (QMF) satellites’ images are not commonly used in classification because of the modification in spectral responses that may confuse any classifier. Boundary pixels are hardly classified correctly in pixel-based classification especially in medium and coarse resolution. In such case, the sudden change in landcover is not measurable by the classifiers because the pixel may contain mor than one class. This research work is a trial to investigate the proper enhancement in accuracy that may occur by using wavelet/QMF bands’ pyramids are in classification instead
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Dissertations / Theses on the topic "Pixel classifier"

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Staufer-Steinnocher, Petra, and Manfred M. Fischer. "A Neural Network Classifier for Spectral Pattern Recognition. On-Line versus Off-Line Backpropagation Training." WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/4152/1/WSG_DP_6097.pdf.

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In this contributon we evaluate on-line and off-line techniques to train a single hidden layer neural network classifier with logistic hidden and softmax output transfer functions on a multispectral pixel-by-pixel classification problem. In contrast to current practice a multiple class cross-entropy error function has been chosen as the function to be minimized. The non-linear diffierential equations cannot be solved in closed form. To solve for a set of locally minimizing parameters we use the gradient descent technique for parameter updating based upon the backpropagation technique fo
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Xavier, Clahildek Matos. "Segmentação, classificação e quantificação de bacilos de tuberculose em imagens de baciloscopia de campo claro através do emprego de uma nova técnica de classificação de pixels utilizando máquinas de vetores de suporte." Universidade Federal do Amazonas, 2012. http://tede.ufam.edu.br/handle/tede/4387.

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Submitted by Geyciane Santos (geyciane_thamires@hotmail.com) on 2015-07-15T14:04:04Z No. of bitstreams: 1 Dissertação - Clahildek Matos Xavier.pdf: 23017599 bytes, checksum: f3e0230fd866c0a784966606404bb807 (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-07-15T18:37:45Z (GMT) No. of bitstreams: 1 Dissertação - Clahildek Matos Xavier.pdf: 23017599 bytes, checksum: f3e0230fd866c0a784966606404bb807 (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-07-15T18:47:
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Fischer, Manfred M., Sucharita Gopal, Petra Staufer-Steinnocher, and Klaus Steinocher. "Evaluation of Neural Pattern Classifiers for a Remote Sensing Application." WU Vienna University of Economics and Business, 1995. http://epub.wu.ac.at/4184/1/WSG_DP_4695.pdf.

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This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification
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Junior, Waldemar Bonventi. "Aprendizado nebuloso híbrido e incremental para classificar pixels por cores." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-03102005-095502/.

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Segmentação de uma imagem é um processo de extrema importância em processamento de imagens e consiste em subdividir a imagem em partes constituintes correspondentes a objetos de interesse no domínio de aplicação. Objetos de interesse podem apresentar cores que se caracterizam numa imagem por um conjunto de pixels, que por sua vez possuem um número muito grande de valores cromáticos. Estes conjuntos podem ser denominados por relativamente poucos rótulos lingüísticos atribuídos por seres humanos, caracterizando as cores, representadas por classes. Entretanto, a fronteira entre estas classes é v
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Sajid, Hasan. "A Universal Background Subtraction System." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/47.

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Background Subtraction is one of the fundamental pre-processing steps in video processing. It helps to distinguish between foreground and background for any given image and thus has numerous applications including security, privacy, surveillance and traffic monitoring to name a few. Unfortunately, no single algorithm exists that can handle various challenges associated with background subtraction such as illumination changes, dynamic background, camera jitter etc. In this work, we propose a Multiple Background Model based Background Subtraction (MB2S) system, which is universal in nature and i
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Negri, Pablo Augusto. "Détection et reconnaissance d'objets structurés : application aux transports intelligents." Paris 6, 2008. http://www.theses.fr/2008PA066346.

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Cette thèse est dédiée à l'étude de méthodes de vision artificielle pour la détection et la reconnaissance d'objets structurés, plus précisément les véhicules automobiles. La première partie est vouée à la détection de véhicules sur des scènes routières à l'aide d'un système embarqué de vision monoculaire. La stratégie utilisée se fonde sur une cascade de classifieurs de type Adaboost qui permet la concaténation des fonctions de classification discriminantes et génératives. Nous avons proposé aussi des méthodes pour classifier les véhicules détectés. La deuxième partie est consacrée à la recon
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Uttam, Kumar *. "Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2280.

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Geospatial analysis involves application of statistical methods, algorithms and information retrieval techniques to geospatial data. It incorporates time into spatial databases and facilitates investigation of land cover (LC) dynamics through data, model, and analytics. LC dynamics induced by human and natural processes play a major role in global as well as regional scale patterns, which in turn influence weather and climate. Hence, understanding LC dynamics at the local / regional as well as at global levels is essential to evolve appropriate management strategies to mitigate the impacts of
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"Aprendizado nebuloso híbrido e incremental para classificar pixels por cores." Tese, Biblioteca Digital de Teses e Dissertações da USP, 2005. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-03102005-095502/.

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(5929994), Xing Liu. "Feature Extraction and Image Analysis with the Applications to Print Quality Assessment, Streak Detection, and Pedestrian Detection." Thesis, 2019.

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Feature extraction is the main driving force behind the advancement of the image processing techniques infields suchas image quality assessment, objectdetection, and object recognition. In this work, we perform a comprehensive and in-depth study on feature extraction for the following applications: image macro-uniformity assessment, 2.5D printing quality assessment, streak defect detection, and pedestrian detection. Firstly, a set of multi-scale wavelet-based features is proposed, and a quality predictor is trained to predict the perceived macro-uniformity. Secondly, the 2.5D printing quality i
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Book chapters on the topic "Pixel classifier"

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Kim, Daehwan, Yeonho Kim, and Daijin Kim. "Separating Occluded Humans by Bayesian Pixel Classifier with Re-weighted Posterior Probability." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23687-7_49.

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Lietz, Holger, Jan Thomanek, Basel Fardi, and Gerd Wanielik. "Improvement of the Classifier Performance of a Pedestrian Detection System by Pixel-Based Data Fusion." In AI*IA 2009: Emergent Perspectives in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10291-2_13.

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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.
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Yaratapalli, Nitheesh Chandra, Reethesh Venkataraman, Abhishek Dinesan, Ashni Manish Bhagvandas, and Padmamala Sriram. "Pixel-Based Attack on ODENet Classifiers." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8289-9_61.

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de Vargas, Rogério R., Ricardo Freddo, Cristiano Galafassi, Sidnei L. B. Gass, Alexandre Russini, and Benjamín Bedregal. "Identifying Pixels Classified Uncertainties ckMeansImage Algorithm." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91479-4_36.

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Marcel, Sébastien, and Yann Rodriguez. "Biometric Face Authentication Using Pixel-Based Weak Classifiers." In Biometric Authentication. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25976-3_3.

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Chang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers." In Hyperspectral Imaging. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_11.

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Niroumand Jadidi, Milad, Mahmoud Reza Sahebi, and Mehdi Mokhtarzade. "Enhancing the Locational Perception of Soft Classified Satellite Imagery Through Evaluation and Development of the Pixel Swapping Technique." In Cartography from Pole to Pole. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32618-9_5.

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Satheesh Kumar, B., K. Seetharaman, and B. Sathiyaprasad. "A Novel Adaboost Regression Classifier for Video Retrieval in Video Sequence." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200194.

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This paper presents a new method for video retrieval, based on machine learning with regression. The proposed classification technique integrates Adaboost and regression classifier for significant retrieval of video frame. The proposed method consists of three stages such as key frames segmentation and gradient of pixels. In this technique, Adaboost classifier is involved in removal of noisy or blurred pixel of the segmented frame. Regression technique converts the video frame pixel either 0’s or 1’s which eliminates the noises in the frame. For the query video, the adopted classifier evaluates the machine learning system for retrieval of similar frames in the databases using proposed Adaboost Regression (ABR) classifier. Experimental analysis is conducted for video datasets to evaluate the proposed ABR classifier performance evaluation. Results stated that through proposed ABR approach incorporated in machine learning system effectively retrieve video frame for query frame. The proposed ABR classifier technique significantly improves the retrieval rate in terms of accuracy, precision.
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De, Indrajit. "A Fuzzy Relational Classifier Based Image Quality Assessment Method." In Intelligent Analysis of Multimedia Information. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0498-6.ch009.

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Fuzzy classification techniques are used for image classification for quite a long time back by allowing pixels to have membership in more than one class. However, handling information at the pixel level is time consuming and there is a high chance of biased assessment of images if class labels are assigned by a single human observer. Even considering multiple observers' opinions don't able to reflect an individual's perception in assessing quality of images, if it is crisp. In this chapter, the fuzzy relational classifier (FRC) is used to assess quality of images distorted by information loss or noise, unlike the earlier methods where images are preprocessed to remove the noise before classification.
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Conference papers on the topic "Pixel classifier"

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Varma, M. Krishna Satya, N. K. K. Rao, K. K. Raju, and G. P. S. Varma. "Pixel-Based Classification Using Support Vector Machine Classifier." In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.20.

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Bristow, Hilton, Jack Valmadre, and Simon Lucey. "Dense Semantic Correspondence Where Every Pixel is a Classifier." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.458.

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Babu, Sree Shankar Satheesh, Prakhar Jaiswal, Ehsan T. Esfahani, and Rahul Rai. "Sketching in Air: A Single Stroke Classification Framework." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34065.

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We describe a trainable, hand drawn, single stroke 3D sketch–based classification system, using a motion detecting depth sense camera. Our system captures data from a user, who is free to sketch any desired shape in a 3D environment. The overall system is based on a set of previously defined and well developed classifiers, which are, the Rubine Classifier, $1 recognizer and the Image based classifier. The novelty of this paper comes from 1) the classification of sketches drawn in a 3D environment; 2) extending the pixel based image representation to a voxel–based scheme; and 3) combining the r
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Chien, Hsiang-Jen, Chia-Yen Chen, Chi-Fa Chen, and Yih-Ming Su. "Adaptive pixel classifier for binary structured light: A probabilistic kernel approach." In 2009 24th International Conference Image and Vision Computing New Zealand (IVCNZ). IEEE, 2009. http://dx.doi.org/10.1109/ivcnz.2009.5378378.

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Xu, J., H. Ishikawa, G. Wollstein, and J. S. Schuman. "3D optical coherence tomography super pixel with machine classifier analysis for glaucoma detection." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090919.

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Jalalat, Morteza, Mansour Nejati, and Ali Majidi. "Vehicle detection and speed estimation using cascade classifier and sub-pixel stereo matching." In 2016 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2016. http://dx.doi.org/10.1109/icspis.2016.7869890.

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Zhang, Aiying, and Ping Tang. "Fusion algorithm of pixel-based and object-based classifier for remote sensing image classification." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723390.

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Melendez, Jaime, Xavier Girones, and Domenec Puig. "Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116147.

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"IMPROVEMENT OF DIFFERENTIAL CRISP CLUSTERING USING ANN CLASSIFIER FOR UNSUPERVISED PIXEL CLASSIFICATION OF SATELLITE IMAGE." In 12th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002872800210029.

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Beveridge, J. R., J. Saraf, and B. Randall. "A Comparison of Pixel, Edge andWavelet Features for Face Detection using a Semi-Naive Bayesian Classifier." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.50.

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