Conferences related to Blob Detection

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


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Periodicals related to Blob Detection

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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


Geoscience and Remote Sensing Letters, IEEE

It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...


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Most published Xplore authors for Blob Detection

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Xplore Articles related to Blob Detection

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A Survey of Blob Detection Algorithms for Biomedical Images

2016 7th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), 2016

This paper presents a survey of blob detection methods which has been applied on image processing with relation of medical images proposed by literature. “The blob detection is a mathematical method which detects regions or points in digital images”. [1] The regions or points which have noticeable difference with their surroundings is called blob. Given the increased interest in biomedical ...


Computer blob detection and tracking for highly repeatable optical fiber sensor

2014 9th International Conference on Intelligent Systems: Theories and Applications (SITA-14), 2014

Computer vision blob detection and tracking is used for precise characterization of optical fiber sensor probe chemically etched. We demonstrate a high degree of repeatability in fabrication of polarization maintaining optical fiber evanescent sensor by tracking etching rates of fiber dopants. We achieved a resolution higher than 0.1 μm by using blob detection processing and centroid algorithm of the fiber ...


FPGA implementation of blob detection algorithm for object detection in visual navigation

2013 International conference on Circuits, Controls and Communications (CCUBE), 2013

Visual navigation system is widely used in various applications such as traffic surveillance, guidance of autonomous vehicles etc. Object detection is one of the important steps which identifies obstacle and provides information about obstacle's location in the image scenario. Blob detection method has been chosen to detect object and to extract required information about the object. Implementation of blob detection ...


Blob Detection With Wavelet Maxima Lines

IEEE Signal Processing Letters, 2007

In this letter, we propose a novel approach to blob detection based on wavelet transform modulus maxima. We use maxima lines in scale-space to build a new blob detector. The algorithm we propose enables automatic blob detection and blob size determination. The robustness to noise of the blob detector we propose is also shown


A Robust Blob Detection and Delineation Method

2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, 2008

This work presents a robust method to detect blob and fit its contour in image. Previous methods for blob detection and delineation were either liable to fail with outliers and noise or computationally expensive. By incorporating the prior information of the region-of-interest and introducing the concept of the kernel MSER, the modified MSER detection method can detect the unique blob ...


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Educational Resources on Blob Detection

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IEEE.tv Videos

The Josephson Effect: SQUIDs Then and Now: From SLUGS to Axions
An FPGA-Quantum Annealer Hybrid System for Wide-Band RF Detection - IEEE Rebooting Computing 2017
Multi-Function VCO Chip for Materials Sensing and More - Jens Reinstaedt - RFIC Showcase 2018
Implantable, Insertable and Wearable Micro-optical Devices for Early Detection of Cancer - Plenary Speaker, Christopher Contag - IPC 2018
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ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
Analytics for Anomaly detection & Classification | DSBC 2020
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Developing Automated Analysis Tools for Space/Time Sidechannel Detection - IEEE SecDev 2016
IEEE Medal for Environmental and Safety Technologies - Jerome Faist and Frank K. Tittell - 2018 IEEE Honors Ceremony
An IEEE IPC Special Session with X. Chen from Nokia Bell Labs
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
Fireside Chat: Key Opinion Leaders on Pre-Symptomatic Illness Detection - IEEE EMBS at NIH, 2019
Hardware Detection in Implantable Media Devices Using Adiabatic Computing - S. Dinesh Kumar - ICRC 2018
ASC-2014 SQUIDs 50th Anniversary: 4 of 6 - Keiji Enpuku
Low Power Image Recognition: The Challenge Continues
A Recurrent Crossbar of Memristive Nanodevices Implements Online Novelty Detection - Christopher Bennett: 2016 International Conference on Rebooting Computing
Welcome to ICRA 2015: Robot Challenges
IEEE Highlight: Electronic Nose: Diagnosing Cancer Through Smell
Silicon THz: an Opportunity for Innovation

IEEE-USA E-Books

  • A Survey of Blob Detection Algorithms for Biomedical Images

    This paper presents a survey of blob detection methods which has been applied on image processing with relation of medical images proposed by literature. “The blob detection is a mathematical method which detects regions or points in digital images”. [1] The regions or points which have noticeable difference with their surroundings is called blob. Given the increased interest in biomedical image processing system, many algorithms and methods have been reported to apply but there is no systematic survey and classification of the blob detection for medical images and how they have been assessed and applied. The findings, which is the most usable methods of blob detectors in biomedical image processing has been presented. It was also investigated how these studies have been surveyed, how they evolved in the main digital libraries over the last decade, and what points deserves further attention, through new research. From this survey, practitioners and researchers can adopt the blob detection methods and analyze to use these methods in their research for further development.

  • Computer blob detection and tracking for highly repeatable optical fiber sensor

    Computer vision blob detection and tracking is used for precise characterization of optical fiber sensor probe chemically etched. We demonstrate a high degree of repeatability in fabrication of polarization maintaining optical fiber evanescent sensor by tracking etching rates of fiber dopants. We achieved a resolution higher than 0.1 μm by using blob detection processing and centroid algorithm of the fiber probe microscopes images.

  • FPGA implementation of blob detection algorithm for object detection in visual navigation

    Visual navigation system is widely used in various applications such as traffic surveillance, guidance of autonomous vehicles etc. Object detection is one of the important steps which identifies obstacle and provides information about obstacle's location in the image scenario. Blob detection method has been chosen to detect object and to extract required information about the object. Implementation of blob detection algorithm on FPGA requires more hardware resources in terms of number for logic gates etc. In this paper, a modification has been proposed for effective hardware implementation of centroid and area computations while using blob detection algorithm. The proposed approach utilizes a novel way to label the connected components and leads to effective hardware implementation. The proposed algorithm utilizes fewer resources and takes less computational time. This algorithm has been implemented in Xilinx Virtex V FPGA board which operates at 100MHz. Processing time taken by the algorithm for computing area and centroid of objects along with labeling is 0.22ms for image resolution of 100 × 100. Algorithm utilizes 4% of available hardware resource and 4 block RAM for complete processing.

  • Blob Detection With Wavelet Maxima Lines

    In this letter, we propose a novel approach to blob detection based on wavelet transform modulus maxima. We use maxima lines in scale-space to build a new blob detector. The algorithm we propose enables automatic blob detection and blob size determination. The robustness to noise of the blob detector we propose is also shown

  • A Robust Blob Detection and Delineation Method

    This work presents a robust method to detect blob and fit its contour in image. Previous methods for blob detection and delineation were either liable to fail with outliers and noise or computationally expensive. By incorporating the prior information of the region-of-interest and introducing the concept of the kernel MSER, the modified MSER detection method can detect the unique blob which is the most stable region to represent the blob. For further processing, the constrained least squares method by incorporating pruning technique is used to fit the ellipse corresponding to the contour of the detected blob. With this proposed method, we can detect and fit blob with high accuracy. The experiments show the validity of the proposed method.

  • Blob Detection in Static Camera with Gaussian Mixture and Silhouette Index for Human Counting Application

    The problem of the object counting in static camera, especially for human counting application, is blob detection. Because humans frequently interact each other, the background subtraction is not sufficient to detect the number of object. Clustering method using Gaussian Mixture Model and silhouette index to detect the number of person in static camera feed are proposed. The whole detection consists of 3 stages which are preprocess, foreground area sampling, and blob detection by clustering algorithm. The overall systems are evaluated using recoded video that was captured by camera with 30° inclination to horizontal plane. The evaluation result shows that the developed algorithm with Gaussian Mixture Model and silhouette index can distinguish 2 persons that walking side-by-side up to 95.35% detection rate and less effective to detect 2 person walking in a queue with maximum detection rate of 56.8%.

  • An Improved Real-Time Blob Detection for Visual Surveillance

    Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real- time multichannel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in an embedded system using those turns out prohibitively difficult. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and blob segmentation through connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need quite different type of processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that it can be incorporated into and run together with connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.

  • Improved algorithm for blob detection in document images

    This paper proposes a fast algorithm for blob detection for document images. Blobs are connected components in a binary image. Blobs need to be detected and extracted in order to obtain useful information from image. Blobs may be 4-connected or 8-connected. Various algorithms are proposed for detecting blobs [5]. As it is the backbone of all fundamental operations, it needs to be fast and accurate. Proposed algorithm makes use of the fact that most document images have more background units as compared to foreground units. Processing them unit by unit is more time consuming. So, algorithm works by processing them in blocks where each block consists of 8 units. In this way, numbers of comparisons are reduced significantly. Also the paper compares the proposed algorithm with five other algorithms on the basis of image size and execution time. Reduced number of comparisons makes it nearly 1.5 times faster than algorithms discussed in paper.

  • Method of Blob detection based on radon transform

    Blob has been widely used in image matching, target identification, and target tracking, etc., which makes efficient Blob detection a fundamental research subject in computer vision applications. Inspired by Radon transform and taking the geometric characteristics of Blob into account, a Blob detection method is proposed in this paper. Firstly, a definition of the Blob response function is given, which is used to implement the statistics of intensity variations along the edges on both sides of the Blob. Then taking the calculated function value as the energy of the Blob center, the Blob is extracted by comparing the empirical threshold with the function value. Some experiments are carried out, and the results indicate that the proposed method can not only locate the position of Blobs with speed and accuracy, but also possess a good robustness to noises.

  • Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images

    Automated whole breast ultrasound (ABUS) is an emerging screening tool for detecting breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor likelihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect breast tumors in ABUS images.



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