Conferences related to Corner 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.


2021 IEEE International Conference on Mechatronics (ICM)

CM focuses on recent developments and future prospects related to the synergetic integration of mechanics, electronics, and information processing.


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 Multimedia and Expo (ICME)

Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.

  • 2019 IEEE International Conference on Multimedia and Expo (ICME)

    speech, audio, image, video, text and new sensor signal processingsignal processing for media integration3D imaging, visualization and animationvirtual reality and augmented realitymulti-modal multimedia computing systems and human-machine interactionmultimedia communications and networkingmedia content analysis and searchmultimedia quality assessmentmultimedia security and content protectionmultimedia applications and servicesmultimedia standards and related issues

  • 2018 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities. ICME also features an Exposition of multimedia products and prototypes.

  • 2017 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to: – Speech, audio, image, video, text and new sensor signal processing – Signal processing for media integration – 3D visualization and animation – 3D imaging and 3DTV – Virtual reality and augmented reality – Multi-modal multimedia computing systems and human-machine interaction – Multimedia communications and networking – Media content analysis – Multimedia quality assessment – Multimedia security and content protection – Multimedia databases and digital libraries – Multimedia applications and services – Multimedia standards and related issues

  • 2016 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to:- Speech, audio, image, video, text and new sensor signal processing- Signal processing for media integration- 3D visualization and animation- 3D imaging and 3DTV- Virtual reality and augmented reality- Multi-modal multimedia computing systems and human-machine interaction- Multimedia communications and networking- Media content analysis- Multimedia quality assessment- Multimedia security and content protection- Multimedia databases and digital libraries- Multimedia applications and services- Multimedia standards and related issues

  • 2015 IEEE International Conference on Multimedia and Expo (ICME)

    With around 1000 submissions and 500 participants each year, the IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2014 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications. In 2014, an Exposition of multimedia products, prototypes and animations will be held in conjunction with the conference.Topics of interest include, but are not limited to:

  • 2013 IEEE International Conference on Multimedia and Expo (ICME)

    To promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2012 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE Societies. It exchanges the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2011 IEEE International Conference on Multimedia and Expo (ICME)

    Speech, audio, image, video, text processing Signal processing for media integration 3D visualization, animation and virtual reality Multi-modal multimedia computing systems and human-machine interaction Multimedia communications and networking Multimedia security and privacy Multimedia databases and digital libraries Multimedia applications and services Media content analysis and search Hardware and software for multimedia systems Multimedia standards and related issues Multimedia qu

  • 2010 IEEE International Conference on Multimedia and Expo (ICME)

    A flagship multimedia conference sponsored by four IEEE societies, ICME serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2009 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2008 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2007 IEEE International Conference on Multimedia and Expo (ICME)

  • 2006 IEEE International Conference on Multimedia and Expo (ICME)

  • 2005 IEEE International Conference on Multimedia and Expo (ICME)

  • 2004 IEEE International Conference on Multimedia and Expo (ICME)

  • 2003 IEEE International Conference on Multimedia and Expo (ICME)

  • 2002 IEEE International Conference on Multimedia and Expo (ICME)

  • 2001 IEEE International Conference on Multimedia and Expo (ICME)

  • 2000 IEEE International Conference on Multimedia and Expo (ICME)


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Periodicals related to Corner Detection

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Antennas and Wireless Propagation Letters, IEEE

IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.


Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


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-- ...


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Most published Xplore authors for Corner Detection

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Xplore Articles related to Corner Detection

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An Improved Corner Detection Algorithm Based on Harris

2018 Chinese Automation Congress (CAC), 2018

Aiming at the problems that the traditional Harris corner detection algorithm could extract more flase corner points and computational complexity when performing corner extraction on the image, an improved Harris corner detection algorithm is proposed. First, the B-spline function is used to replace a Gaussian window function for smoothing filtering, then the corner points are pre-selected to obtain candidate corners. ...


Multi-Scale Harris Corner Detection Algorithm Based on Canny Edge-Detection

2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET), 2018

Harris algorithm is a classical corner detection algorithm, it has affine invariant and partial rotational invariance. But it does not have the scale invariance, and it has poor real-time and adaptability. This paper will put forward a new corner detection algorithm which based on improved Canny edge detection algorithm and improved multi-scale Harris corner detection algorithm. First the edge information ...


A Novel Shi-Tomasi Corner Detection Algorithm Based on Progressive Probabilistic Hough Transform

2018 Chinese Automation Congress (CAC), 2018

The Hough transform is introduced into the image processing field to solve Line detection, curve detection Edge extraction and other problems. A novel Shi-Tomasi corner detection algorithm based on Progressive Probabilistic Hough Transform (PPHT) is proposed to meet the needs of corner detection. In the algorithm, image processing includes filtering, conversion to grayscale and edge detection, then PPHT is used ...


A Fast Corner Detection Method from Laser Readings

2018 Chinese Automation Congress (CAC), 2018

A corner detection method is addressed in this paper. It is designed in order to construct a feature-based map which is applied to the simultaneous localization and mapping problem of mobile robots. With a iterative line segments extraction algorithm, the corner detection method has low time complexity and high accuracy. Results of experiment illustrate the effectiveness of the designed corner ...


Harris corner detection based leaf image segmentation for ancient Chinese books

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017

In the image digitization process of ancient Chinese books, sometimes it is needed to extract the leaf from the original image and separate the left half and the right half. This paper proposes a leaf segmentation method based on Harris corner detection to solve this problem. Firstly, the original image is preprocessed to recognize the contour information, and the minimum ...


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Educational Resources on Corner Detection

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IEEE.tv Videos

An FPGA-Quantum Annealer Hybrid System for Wide-Band RF Detection - IEEE Rebooting Computing 2017
A Transformer-Based Inverted Complementary Cross-Coupled VCO with a 193.3dBc/Hz FoM and 13kHz 1/f3 Noise Corner: RFIC Interactive Forum
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An IEEE IPC Special Session with X. Chen from Nokia Bell Labs
Noise Enhanced Information Systems: Denoising Noisy Signals with Noise
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
Hardware Detection in Implantable Media Devices Using Adiabatic Computing - S. Dinesh Kumar - ICRC 2018
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IEEE-USA E-Books

  • An Improved Corner Detection Algorithm Based on Harris

    Aiming at the problems that the traditional Harris corner detection algorithm could extract more flase corner points and computational complexity when performing corner extraction on the image, an improved Harris corner detection algorithm is proposed. First, the B-spline function is used to replace a Gaussian window function for smoothing filtering, then the corner points are pre-selected to obtain candidate corners. Finally, in order to improve the adaptability of the algorithm, an auto-adaptive threshold method is used when the non-maximum value is suppressed. Experimental results show that this algorithm improves the detection accuracy and efficiency, and has good corner detection performance.

  • Multi-Scale Harris Corner Detection Algorithm Based on Canny Edge-Detection

    Harris algorithm is a classical corner detection algorithm, it has affine invariant and partial rotational invariance. But it does not have the scale invariance, and it has poor real-time and adaptability. This paper will put forward a new corner detection algorithm which based on improved Canny edge detection algorithm and improved multi-scale Harris corner detection algorithm. First the edge information of the target can be got by the improved Canny algorithm. Then process the original image by the Mean-Shift filter. At last adopts improved multi-scale Harris algorithm to detect the corners from the edge information, and marks the position of the corners in the original image. According to the experimental results, the new method shows that the corners distribute equably and have high accuracy. At the same time combine scale invariance and rotational invariance. The new method greatly improves image corner detection performance compared with the traditional Harris algorithm.

  • A Novel Shi-Tomasi Corner Detection Algorithm Based on Progressive Probabilistic Hough Transform

    The Hough transform is introduced into the image processing field to solve Line detection, curve detection Edge extraction and other problems. A novel Shi-Tomasi corner detection algorithm based on Progressive Probabilistic Hough Transform (PPHT) is proposed to meet the needs of corner detection. In the algorithm, image processing includes filtering, conversion to grayscale and edge detection, then PPHT is used to deal with the images and thus to combine with Shi-Tomasi algorithm for obtaining more accurate detection result. Experiment results in corner detection of images show that the proposed method helps decrease the misdetection and false corner better than the traditional Shi-Tomasi algorithm.

  • A Fast Corner Detection Method from Laser Readings

    A corner detection method is addressed in this paper. It is designed in order to construct a feature-based map which is applied to the simultaneous localization and mapping problem of mobile robots. With a iterative line segments extraction algorithm, the corner detection method has low time complexity and high accuracy. Results of experiment illustrate the effectiveness of the designed corner detection method.

  • Harris corner detection based leaf image segmentation for ancient Chinese books

    In the image digitization process of ancient Chinese books, sometimes it is needed to extract the leaf from the original image and separate the left half and the right half. This paper proposes a leaf segmentation method based on Harris corner detection to solve this problem. Firstly, the original image is preprocessed to recognize the contour information, and the minimum bounding rectangle is determined by the contour. Then, Harris corner detection is used. In order to recognize the edge of the leaf, we traverse the corner points in the special area and save those points which meet the threshold requirement. At last, the edges of left half leaf and right half leaf are determined, and the segmentation is completed. The experimental results show that the proposed method achieves good performance in a class of leaf image segmentation.

  • Corner Detection for Room Mapping of Fire Fighting Robot

    In mapping of an environment, an approach to locate points or features on the map is needed to be recognized by the robot. Corner detection is one of the methods used to find the feature of a corner. In the Indonesian fire fighting robot contest (KRPAI), the method was used to recognize the room where the robot started. The starting position was randomized, so it was difficult to determine which room the robot was placed. In this paper we want to solve the issue by using corner detection to look for corner in each mapped room and look for the characteristic in every room. Characteristics of the room obtained from the distance between the two corners and the distance between the door with the nearest corner. Based on experiments used this method can recognize the room with accuracy of 87.25%. The experiment result showed that 6 out of 48 trials require two scans by turning the robot at 90 degrees because of the limitations of the laser range finder which can only scan by 270 degrees. From all experiments the average error of the reference distance is 2.29 cm.

  • An Adaptive Corner Detection Algorithm Based on Edge Features

    Harris algorithm is classical and high speed corner detection method, but the detect result is dependent on the thresholds and the coefficients of the corner response function, and it is easy to lose and error detect corners, so it has lower accuracy of corner detection. In summary, an adaptive corner detection algorithm base on edge feature was proposed in this paper, the purpose of improve detection algorithm is to promote corner detection accuracy and ensure algorithm performance. Firstly the Canny operator is used to extract the edge information of image, and calculated the characteristic value of edge region is calculated by optimizing the corner response function; then, the adaptive threshold is obtained by the OSTU algorithm to extract candidate corner region; Finally, according to block detection and scale-invariant ideas, we use the non-maximum suppression algorithm is improved to remove false corners. Our proposed algorithm not only overcomes somewhat non-adaptive threshold and easily loss corner and error detection problem, but also improves speed detection. The experimental results show that our algorithm can improve the accuracy of corner detection, and can have ideal detection effect in different environments.

  • The Comparison of Two Typical Corner Detection Algorithms

    Corners in images represent a lot of important information. Extracting corners accurately is significant to image processing, which can reduce much of the calculations. In this paper, two widely used corner detection algorithms, SUSAN and Harris corner detection algorithms which are both based on intensity, were compared in stability, noise immunity and complexity quantificationally via stability factor eta, anti-noise factor rho and the runtime of each algorithm. It concluded that Harris corner detection algorithm was superior to SUSAN corner detection algorithm on the whole. And the comparison result was applied to an image matching experiment. It was verified that the quantitative evaluations of these two corner detection algorithms were valid through calculating match efficiency, defined as correct matching corner pairs dividing by matching time, which can reflect the performances of a corner detection algorithm comprehensively. The work of this paper can provide a direction to the improvement and the utilization of these two corner detection algorithms.

  • Adaptive Harris Corner Detection Algorithm Based on B-Spline Function

    Harris corner detection is a classic corner detection algorithm, but using Gaussian smoothing filter, there is phenomenon of corner information missing and corner migration. In this paper, to have a data-fitting and low-pass characteristics of B-spline function introduced into the algorithm, construct a new filter, while introducing the idea of image sub-block and the excluding neighboring points to build a B-spline-based adaptive Harris corner detection algorithm. The new corner detection algorithm to extract corner uniformity, positioning accuracy, and noise suppression is good. Through the experiment, the new algorithm significantly improves the image corner detection performance.

  • Real-Time Corner Detection Algorithm Based on GPU

    This paper presents a class of GPU-based real-time corner detection algorithms. On the basis of existing corner detection algorithms, GPU-based real-time corner detection algorithms are executed by GPU. To further improve the speed of the algorithm, the algorithm uses the method of corner value map compression. Experiments show that the GPU-based real time corner detection algorithm is suited to the real-time applications with resolution up to 1024 * 768 pixels.



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