Relevant bibliographies by topics / Mean shift algorithm

Contents

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

Academic literature on the topic 'Mean shift algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Mean shift algorithm"

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Aliyari Ghassabeh, Youness, and Frank Rudzicz. "Modified mean shift algorithm." IET Image Processing 12, no. 12 (December 1, 2018): 2172–77. http://dx.doi.org/10.1049/iet-ipr.2018.5600.

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Zhao, Yi Zhi, Huan Wang, and Guo Cai Yin. "Research on Mean Shift Algorithm." Advanced Materials Research 756-759 (September 2013): 4021–25. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4021.

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Computer vision is a diverse and relatively new field of study. Object tracking plays a crucial role as a preliminary step for high-level image processing in the field of computer vision. However, mean shift algorithm in the target tracking has some defects, such as: the application of fixed bandwidth for probability density estimation usually causes lack of smooth or too smooth; moving target often appears partial occlusion or complete occlusion due to the complexity of the background; background pixels in object model will induce localization error of object tracking, and so on. Therefore, this paper elaborates several elegant algorithms to solve some of the problems. After discussing the application of Mean shift in the field of target tracking, this paper presented an improved Mean shift algorithm by combining Mean Shift and Kalman Filter.
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Jingxue Chen, Jingxue Chen, Jingkang Yang Jingxue Chen, Juan Huang Jingkang Yang, and Yining Liu Juan Huang. "Robust Truth Discovery Scheme Based on Mean Shift Clustering Algorithm." 網際網路技術學刊 22, no. 4 (July 2021): 835–42. http://dx.doi.org/10.53106/160792642021072204011.

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Wang, Juan. "Mean Shift Algorithm in Object Tracking." Advanced Science Letters 11, no. 1 (May 30, 2012): 768–71. http://dx.doi.org/10.1166/asl.2012.3028.

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LI, Xiang-Ru. "Convergence of a Mean Shift Algorithm." Journal of Software 16, no. 3 (2005): 365. http://dx.doi.org/10.1360/jos160365.

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WEN, Zhi-Qiang. "Convergence Analysis of Mean Shift Algorithm." Journal of Software 18, no. 2 (2007): 205. http://dx.doi.org/10.1360/jos180205.

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Chen, JianJun, SuoFei Zhang, GuoCheng An, and ZhenYang Wu. "A generalized mean shift tracking algorithm." Science China Information Sciences 54, no. 11 (September 9, 2011): 2373–85. http://dx.doi.org/10.1007/s11432-011-4359-8.

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HE, LIWEN, YONG XU, YAN CHEN, and JIAJUN WEN. "RECENT ADVANCE ON MEAN SHIFT TRACKING: A SURVEY." International Journal of Image and Graphics 13, no. 03 (July 2013): 1350012. http://dx.doi.org/10.1142/s0219467813500125.

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Though there have been many applications of object tracking, ranging from surveillance and monitoring to smart rooms, object tracking is always a challenging problem in computer vision over the past decades. Mean Shift-based object tracking has received much attention because it has a great number of advantages over other object tracking algorithms, e.g. real time, robust and easy to implement. In this survey, we first introduce the basic principle of the Mean Shift algorithm and the working procedure using the Mean Shift algorithm to track the object. This paper then describes the defects and potential issues of the traditional Mean Shift algorithm. Finally, we summarize the improvements to the Mean Shift algorithm and some hybrid tracking algorithms that researchers have proposed. The main improvements include scale adaptation, kernel selection, on-line model updating, feature selection and mode optimization, etc.
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Deng, Zheng Hong, Ting Ting Li, and Ting Ting Zhang. "An Adaptive Tracking Algorithm Based on Mean Shift." Advanced Materials Research 538-541 (June 2012): 2607–13. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.2607.

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Object tracking is to search the most similar parts to targets in video sequences. Among the various tracking algorithms, mean shift tracking algorithm has become popular due to its simplicity, efficiency and good performance. This paper focused on mean shift tracking algorithm, which is a modeling mechanism based on statistical probability density function. In practice, when the background of the tracking and characteristics of the target are similar, pixels of background occupy a large proportion in the histogram. The traditional mean shift cannot adapt to the mutative scene. Meanwhile, if there is block or disappearance, the result is not exact. Three algorithms were given for above difficulties. A weighted template background was established, that can highlight the features of target and improve real-time. Then this paper presented a selective mechanism to update the target model. Every component is updated based on the contribution to the target model. Finally, the Kalman filter was combined with mean shift algorithm. We saw the prediction points of Kalman filter as the initial point, carried out the mean shift iteration and then updated Kalman filter using the ultimate location. Extensive experimental results illustrated excellent agreement with these methods.
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Kumar, Praveen, Anisha Rani, Ashok Rawat, and Seema Rawat. "Analysis of Mean-shift Algorithm to Detect Hotspots of Dengue Fever Outbreak." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (October 31, 2019): 27–35. http://dx.doi.org/10.5373/jardcs/v11i10/20193002.

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Dissertations / Theses on the topic "Mean shift algorithm"

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Hu, Ting. "Convergence of the mean shift algorithm and its generalizations." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4925.

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Mean shift is an effective iterative algorithm widely used in image analysis tasks like tracking, image segmentation, smoothing, filtering, edge detection and etc. It iteratively estimates the modes of the probability function of a set of sample data points based in a region. Mean shift was invented in 1975, but it was not widely used until the work by Cheng in 1995. After that, it becomes popular in computer vision. However the convergence, a key character of any iterative algorithm, has been rigorously proved only very recently, but with strong assumptions. In this thesis, the method of mean shift is introduced systematically first and then the convergence is established under more relaxed assumptions. Finally, generalization of the mean shift method is also given for the estimation of probability density function using generalized multivariate smoothing functions to meet the need for more real life applications.
ID: 030423142; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Error in paging: p. vi followed by one unnumbered page followed by p. ii-iv.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 61-62).
M.S.
Masters
Mathematics
Sciences
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Xie, Qing Yan. "K-Centers Dynamic Clustering Algorithms and Applications." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644.

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MAHAJANI, RASIKA. "APPLICATION OF THE MEAN SHIFT ALGORITHM ON CLUSTERS OF ORTHOLOGOUS GROUPS AND PHYLOGENETIC IMPLICATIONS." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1131646726.

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Klvaňa, Marek. "Sledování vybraného objektu v dynamickém obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229705.

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The aim of this thesis is a description and implementation of algorithms of the tracked objects in the video feed. This thesis introduces Mean shift and Continuously adaptive mean shift algorithms which represent category based on kernel tracking. For construction of a model is used a threedimensional color histogram whose construction is described in this thesis as well. The achievements of described algorithms are compared in the testing images sequences and evaluated in details.
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Carli, Daniel Michelon de. "GERAÇÃO PROCEDURAL DE CENÁRIOS 3D DE CÂNIONS COM FOCO EM JOGOS DIGITAIS." Universidade Federal de Santa Maria, 2012. http://repositorio.ufsm.br/handle/1/5394.

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This Master s thesis proposes a non-assisted procedural method for 3D canyons scenes generation based on techniques of computer graphics, computer vision and graph search algorithm. In order to define all the features to be reproduced in our scenes, we have analyzed several images of real canyons and have categorized them in two canyon features models: a recursive and an ordinary one. The proposed approach manipulates a heightmap, created using Perlin noise, in order to imitate the geological features formation previously analyzed. Several parametrizations are used to guide and constraint the generation of terrains, canyons features, course of river, plain areas, soft slope regions, cliffs and plateaus. This work also uses the Mean Shift algorithm as mechanism of segmentation to define regions of interest. A binary mask, with plain areas, is defined based on a threshold operation by a given data set provided by the Mean Shift algorithm. Thereafter a connected-component labeling algorithm is executed using the previously binary mask. This algorithm finds all plains centroids. Right after that, the Dijkstra s algorithm is performed in order to connect all plain areas, creating a valid path between the centroids. The Dijkstra s algorithm is executed again to define the river s course. Finally, a Gaussian smoothing operation is applied to interpolate the soft slope regions. The combination of all those techniques produces as a result automatically generated feature-rich canyons.
Esta dissertação propõe um método procedural não assistido, baseado em técnicas de computação gráfica, visão computacional e busca em grafos, para a geração de cenários 3D de cânions com foco em jogos digitais. Para definir as características a serem reproduzidas, foram analisadas diversas imagens de cânions reais chegando-se em dois modelos, um comum e outro recursivo. A abordagem proposta manipula um reticulado gerado com ruído de Perlin, moldando assim as características inerentes a essa formação geológica. São levadas em conta as diversas parametrizações necessárias para permitir que o algoritmo construa cânions com curso de rio, áreas de planícies, regiões de encosta suave, estruturas de penhascos e, por fim, planaltos nas regiões mais altas. Para atingir o resultado final, o trabalho utiliza o algoritmo Mean Shift como mecanismo de segmentação, definindo dados e regiões de interesse. Munido dos dados do algoritmo de clusterizacao, é definido um limiar para a criação de uma máscara binária com a definição das planícies. Em um segundo momento, um algoritmo de rotulação de componentes conectados é executado, extraindo-se os centróides de cada planície. Por sua vez, o algoritmo de Dijkstra encaixa-se na definição de rotas que conectam estas planícies. O algoritmo de Dijkstra é, então, executado novamente, tendo por base uma função de custo de inclinação, para definir o curso do rio. Por fim, uma filtragem espacial baseada em um filtro Gaussiano é aplicada para interpolar as regiões de encostas de declive suave. A combinação dessas técnicas gera terrenos com grande variabilidade e com as características inerentes à formação geológica de cânions.
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Savas, Zafer. "Real-time Detection And Tracking Of Human Eyes In Video Sequences." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606459/index.pdf.

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Robust, non-intrusive human eye detection problem has been a fundamental and challenging problem for computer vision area. Not only it is a problem of its own, it can be used to ease the problem of finding the locations of other facial features for recognition tasks and human-computer interaction purposes as well. Many previous works have the capability of determining the locations of the human eyes but the main task in this thesis is not only a vision system with eye detection capability
Our aim is to design a real-time, robust, scale-invariant eye tracker system with human eye movement indication property using the movements of eye pupil. Our eye tracker algorithm is implemented using the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm proposed by Bradski and the EigenFace method proposed by Turk &
Pentland. Previous works for scale invariant object detection using Eigenface method are mostly dependent on limited number of user predefined scales which causes speed problems
so in order to avoid this problem an adaptive eigenface method using the information extracted from CAMSHIFT algorithm is implemented to have a fast and scale invariant eye tracking. First of all
human face in the input image captured by the camera is detected using the CAMSHIFT algorithm which tracks the outline of an irregular shaped object that may change size and shape during the tracking process based on the color of the object. Face area is passed through a number of preprocessing steps such as color space conversion and thresholding to obtain better results during the eye search process. After these preprocessing steps, search areas for left and right eyes are determined using the geometrical properties of the human face and in order to locate each eye indivually the training images are resized by the width information supplied by the CAMSHIFT algortihm. Search regions for left and right eyes are individually passed to the eye detection algortihm to determine the exact locations of each eye. After the detection of eyes, eye areas are individually passed to the pupil detection and eye area detection algorithms which are based on the Active Contours method to indicate the pupil and eye area. Finally, by comparing the geometrical locations of pupil with the eye area, human gaze information is extracted. As a result of this thesis a software named &ldquo
TrackEye&rdquo
with an user interface having indicators for the location of eye areas and pupils, various output screens for human computer interaction and controls for allowing to test the effects of color space conversions and thresholding types during object tracking has been built.
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Naeem, Asad. "Single and multiple target tracking via hybrid mean shift/particle filter algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12699/.

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This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences.
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Bilgin, Arda. "Selection And Fusion Of Multiple Stereo Algorithms For Accurate Disparity Segmentation." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610133/index.pdf.

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Fusion of multiple stereo algorithms is performed in order to obtain accurate disparity segmentation. Reliable disparity map of real-time stereo images is estimated and disparity segmentation is performed for object detection purpose. First, stereo algorithms which have high performance in real-time applications are chosen among the algorithms in the literature and three of them are implemented. Then, the results of these algorithms are fused to gain better performance in disparity estimation. In fusion process, if a pixel has the same disparity value in all algorithms, that disparity value is assigned to the pixel. Other pixels are labelled as unknown disparity. Then, unknown disparity values are estimated by a refinement procedure where neighbourhood disparity information is used. Finally, the resultant disparity map is segmented by using mean shift segmentation. The proposed method is tested in three different stereo data sets and several real stereo pairs. The experimental results indicate an improvement for the stereo analysis performance by the usage of fusion process and refinement procedure. Furthermore, disparity segmentation is realized successfully by using mean shift segmentation for detecting objects at different depth levels.
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Elsayed, Elawady Mohamed. "Reflection Symmetry Detection in Images : Application to Photography Analysis." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES006/document.

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La symétrie est une propriété géométrique importante en perception visuelle qui traduit notre perception des correspondances entre les différents objets ou formes présents dans une scène. Elle est utilisée comme élément caractéristique dans de nombreuses applications de la vision par ordinateur (comme par exemple la détection, la segmentation ou la reconnaissance d'objets) mais également comme une caractéristique formelle en sciences de l'art (ou en analyse esthétique). D’importants progrès ont été réalisés ces dernières décennies pour la détection de la symétrie dans les images mais il reste encore de nombreux verrous à lever. Dans cette thèse, nous nous intéressons à la détection des symétries de réflexion, dans des images réelles, à l'échelle globale. Nos principales contributions concernent les étapes d'extraction de caractéristiques et de représentation globale des axes de symétrie. Nous proposons d'abord une nouvelle méthode d'extraction de segments de contours à l'aide de bancs de filtres de Gabor logarithmiques et une mesure de symétrie intersegments basée sur des caractéristiques locales de forme, de texture et de couleur. Cette méthode a remporté la première place à la dernière compétition internationale de symétrie pour la détection mono- et multi-axes. Notre deuxième contribution concerne une nouvelle méthode de représentation des axes de symétrie dans un espace linéaire-directionnel. Les propriétés de symétrie sont représentées sous la forme d'une densité de probabilité qui peut être estimée, de manière non-paramétrique, par une méthode à noyauxbasée sur la distribution de Von Mises-Fisher. Nous montrons que la détection des axes dominants peut ensuite être réalisée à partir d'un algorithme de type "mean-shift” associé à une distance adaptée. Nous introduisons également une nouvelle base d'images pour la détection de symétrie mono-axe dans des photographies professionnelles issue de la base à grande échelle AVA (Aestetic Visual Analysis). Nos différentes contributions obtiennent des résultats meilleurs que les algorithmes de l'état de l'art, évalués sur toutes les bases disponibles publiquement, spécialement dans le cas multi-axes. Nous concluons que les propriétés de symétrie peuvent être utilisées comme des caractéristiques visuelles de niveau sémantique intermédiaire pour l'analyse et la compréhension de photographies
Symmetry is a fundamental principle of the visual perception to feel the equally distributed weights within foreground objects inside an image. It is used as a significant visual feature through various computer vision applications (i.e. object detection and segmentation), plus as an important composition measure in art domain (i.e. aesthetic analysis). The development of symmetry detection has been improved rapidly since last century. In this thesis, we mainly aim to propose new approaches to detect reflection symmetry inside real-world images in a global scale. In particular, our main contributions concern feature extraction and globalrepresentation of symmetry axes. First, we propose a novel approach that detects global salient edges inside an image using Log-Gabor filter banks, and defines symmetry oriented similarity through textural and color around these edges. This method wins a recent symmetry competition worldwide in single and multiple cases.Second, we introduce a weighted kernel density estimator to represent linear and directional symmetrical candidates in a continuous way, then propose a joint Gaussian-vonMises distance inside the mean-shift algorithm, to select the relevant symmetry axis candidates along side with their symmetrical densities. In addition, we introduce a new challenging dataset of single symmetry axes inside artistic photographies extracted from the large-scale Aesthetic Visual Analysis (AVA) dataset. The proposed contributions obtain superior results against state-of-art algorithms among all public datasets, especially multiple cases in a global scale. We conclude that the spatial and context information of each candidate axis inside an image can be used as a local or global symmetry measure for further image analysis and scene understanding purposes
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Kyrgyzov, Ivan. "Recherche dans les bases de donnees satellitaires des paysages et application au milieu urbain: clustering, consensus et categorisation." Phd thesis, Télécom ParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00004084.

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Les images satellitaires ont trouvées une large application pour l'analyse des ressources naturelles et des activités humaines. Les images à haute résolution, e.g., SPOT5, sont très nombreuses. Ceci donne un grand intérêt afin de développer de nouveaux aspects théoriques et des outils pour la fouille d'images. L'objectif de la thèse est la fouille non-supervisée d'images et inclut trois parties principales. Dans la première partie nous démontrons le contenu d'images à haute résolution. Nous décrivons les zones d'images par les caractéristiques texturelles et géométriques. Les algorithmes de clustering sont présentés dans la deuxième partie. Une étude de critères de validité et de mesures d'information est donnée pour estimer la qualité de clustering. Un nouveau critère basé sur la Longueur de Description Minimale (LDM) est proposé pour estimer le nombre optimal de clusters. Par ailleurs, nous proposons un nouveau algorithme hiérarchique basé sur le critère LDM à noyau. Une nouvelle méthode de ''combinaison de clustering'' est présentée dans la thèse pour profiter de différents algorithmes de clustering. Nous développons un algorithme hiérarchique pour optimiser la fonction objective basée sur une matrice de co-association. Une deuxième méthode est proposée qui converge à une solution globale. Nous prouvons que le minimum global peut être trouvé en utilisant l'algorithme de type ''mean shift''. Les avantages de cette méthode sont une convergence rapide et une complexité linéaire. Dans la troisième partie de la thèse un protocole complet de la fouille d'images est proposé. Différents clusterings sont représentés via les relations sémantiques entre les concepts.
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Book chapters on the topic "Mean shift algorithm"

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Shah, Rahul V., Amit Jain, Rutul B. Bhatt, Pinal Engineer, and Ekata Mehul. "Mean-Shift Algorithm: Verilog HDL Approach." In Lecture Notes in Electrical Engineering, 181–94. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3363-7_21.

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Fang, Hui, Aimin Zhou, and Guixu Zhang. "A Mean Shift Assisted Differential Evolution Algorithm." In Bio-inspired Computing – Theories and Applications, 163–72. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3614-9_21.

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Suárez, Yasel Garcés, Esley Torres, Osvaldo Pereira, Claudia Pérez, and Roberto Rogríguez. "Stopping Criterion for the Mean Shift Iterative Algorithm." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 383–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41822-8_48.

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Kumar, Sandeep, Rohit Raja, and Archana Gandham. "Tracking an Object Using Traditional MS (Mean Shift) and CBWH MS (Mean Shift) Algorithm with Kalman Filter." In Algorithms for Intelligent Systems, 47–65. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3357-0_4.

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Chen, Ai-hua, Ben-quan Yang, and Zhi-gang Chen. "A Timely Occlusion Detection Based on Mean Shift Algorithm." In Future Control and Automation, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31003-4_7.

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Du, Ruo, Qiang Wu, Xiangjian He, and Jie Yang. "Object Categorization Based on a Supervised Mean Shift Algorithm." In Computer Vision – ECCV 2012. Workshops and Demonstrations, 611–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_64.

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AbdAllah, Loai, and Ilan Shimshoni. "Mean Shift Clustering Algorithm for Data with Missing Values." In Data Warehousing and Knowledge Discovery, 426–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10160-6_38.

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Rodríguez, Roberto, and Ana G. Suarez. "An Image Segmentation Algorithm Using Iteratively the Mean Shift." In Lecture Notes in Computer Science, 326–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11892755_33.

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Sojka, Eduard, Jan Gaura, Tomáš Fabián, and Michal Krumnikl. "Fast Mean Shift Algorithm Based on Discretisation and Interpolation." In Advanced Concepts for Intelligent Vision Systems, 402–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17688-3_38.

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Varadarajan, V., S. V. Lokesh, A. Ramesh, A. Vanitha, and V. Vaidehi. "Face Tracking Using Modified Forward-Backward Mean-Shift Algorithm." In Communications in Computer and Information Science, 46–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8603-8_5.

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Conference papers on the topic "Mean shift algorithm"

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Rao, Sudhir, Weifeng Liu, Jose Principe, and Allan Medeiros Martins. "Information Theoretic Mean Shift Algorithm." In 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/mlsp.2006.275540.

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Zhong, Xian, Kun Tu, and Hongxia Xia. "Mean-shift algorithm fusing multi feature." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054213.

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Bo, Shukui, and Yongju Jing. "Image Clustering Using Mean Shift Algorithm." In 2012 4th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2012. http://dx.doi.org/10.1109/cicn.2012.128.

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Shih-Yu Chiu, Jia-Rui Zhang, and Leu-Shing Lan. "A dual-mode mean-shift algorithm." In 2008 51st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2008. http://dx.doi.org/10.1109/mwscas.2008.4616804.

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López Palafox, Guadalupe Desirée, Ana Luisa Sosa Ortíz, Oscar Marrufo Melendez, Orlando Morales Ballesteros, Jorge Luis Pérez González, and Juan Ramón Jiménez Alaniz. "Hippocampal segmentation using mean shift algorithm." In 12th International Symposium on Medical Information Processing and Analysis, edited by Eduardo Romero, Natasha Lepore, Jorge Brieva, and Ignacio Larrabide. SPIE, 2017. http://dx.doi.org/10.1117/12.2256810.

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"K-Centers Mean-shift Reverse Mean-shift clustering algorithm over heterogeneous wireless sensor networks." In 2014 Wireless Telecommunications Symposium (WTS). IEEE, 2014. http://dx.doi.org/10.1109/wts.2014.6835019.

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Li, Zhong-Sheng, Ren-Fa Li, Yu-Feng Liu, and Yao-Xue Zhang. "A New Improvement on Mean-Shift Algorithm." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.358.

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Liu, Jiangang, Mingyang Wang, Lingjiang Kong, and Xiaobo Yang. "Through-wall tracking using mean-shift algorithm." In 2017 IEEE Radar Conference (RadarConf17). IEEE, 2017. http://dx.doi.org/10.1109/radar.2017.7944400.

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Tzon-Liang Shieh, Jia-Rui Zhang, Shih-Yu Chiu, and Leu-Shing Lan. "0n convergence of the mean shift algorithm." In 2008 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2008. http://dx.doi.org/10.1109/isccsp.2008.4537298.

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Chao, Xing, and Wang Ling. "Asymptotic mean shift algorithm for object tracking." In 5th International Conference on Advanced Computer Control. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/icacc130851.

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Reports on the topic "Mean shift algorithm"

1

Zhang, Yunfeng, and Anthony J. Hornof. Using the Mean Shift Algorithm to Make Post Hoc Improvements to the Accuracy of Eye Tracking Data Based on Probable Fixation Locations. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada528607.

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