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Seong-whan Lee - One of the best experts on this subject based on the ideXlab platform.

  • Accurate object contour tracking based on Boundary Edge selection
    Pattern Recognition, 2007
    Co-Authors: Myung-cheol Roh, Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    In this paper, a novel method for accurate subject tracking, by selecting only tracked subject Boundary Edges in a video stream with a changing background and moving camera, is proposed. This Boundary Edge selection is achieved in two steps: (1) removing background Edges using Edge motion, and from the output of the previous step, (2) selecting Boundary Edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant Edges, by only selecting Boundary Edge pixels. In order to remove background Edges using Edge motion, the tracked subject motion is computed and Edge motions and Edges having different motion directions from the subjects are removed. In selecting Boundary Edges using the normal contour direction, the image gradient values on every Edge pixel are computed, and Edge pixels with large gradient values are selected. Multi-level Canny Edge maps are used to obtain proper details of a scene. Multi-level Edge maps allow tracking, even though the tracked object Boundary has complex Edges, since the detail level of an Edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny Edge map and hiring strong Canny Edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.

  • ICPR (4) - Object Boundary Edge selection using normal direction derivatives of a contour in a complex scene
    2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    Nguyen proposed a method [H.T., Nguyen et al., 2002] for tracking a nonparameterized object (subject) contour in a single video stream. Nguyen's approach combined outputs of two steps: creating a predicted contour and removing background Edges. We propose a method to increase object tracking accuracy by improving the background Edge removal process. Nguyen's background Edge removal method of leaving many irrelevant Edges is subject to inaccurate contour tracking. Our accurate tracking is based on reducing affects from irrelevant Edges by selecting the Boundary Edge only. We select high-valued Edge pixels of average image intensity gradients in the contour normal direction. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

  • PRICAI - Object Boundary Edge selection for human body tracking using level-of-detail canny Edges
    PRICAI 2004: Trends in Artificial Intelligence, 2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    We propose a method for an accurate subject tracking by selecting only tracked subject Boundary Edges in a video stream with changing background and a moving camera. Our Boundary Edge selection is done in two steps; 1) remove background Edges using an Edge motion, 2) from the output of the previous step, select Boundary Edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant Edges by selecting Boundary Edge pixels only. In order to remove background Edges using the Edge motion, we compute tracked subject motion and Edge motions. The Edges with different motion direction than the subject motion are removed. In selecting Boundary Edges using the contour normal direction, we compute image gradient values on every Edge pixels, and select Edge pixels with large gradient values. We use multi-level Canny Edge maps to get proper details of a scene. Multi-level Edge maps allow us robust tracking even though the tracked object Boundary is not clear, because we can adjust the detail level of an Edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny Edge map and hiring strong Canny Edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

  • ICIAR (2) - Object Boundary Edge Selection for Accurate Contour Tracking Using Multi-level Canny Edges
    Lecture Notes in Computer Science, 2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    We propose a method of selecting only tracked subject Boundary Edges in a video stream with changing background and a moving camera. Our Boundary Edge selection is done in two steps; first, remove background Edges using an Edge motion, second, from the output of the previous step, select Boundary Edges using a normal direction derivative of the tracked contour. In order to remove background Edges, we compute Edge motions and object motions. The Edges with different motion direction than the subject motion are removed. In selecting Boundary Edges using the contour normal direction, we compute image gradient values on every Edge pixels, and select Edge pixels with large gradient values. We use multi-level Canny Edge maps to get proper details of a scene. Detailed-level Edge maps give us more scene information even though the tracked object Boundary is not clear, because we can adjust the detail level of Edge maps for a scene. We use Watersnake model to decide a new tracked contour. Our experimental results show that our approach is superior to Nguyen’s.

Yaguo Wang - One of the best experts on this subject based on the ideXlab platform.

  • accelerated carrier recombination by grain Boundary Edge defects in mbe grown transition metal dichalcogenides
    APL Materials, 2018
    Co-Authors: Ke Chen, Anupam Roy, Amritesh Rai, Hema C. P. Movva, Xianghai Meng, Sanjay K. Banerjee, Yaguo Wang
    Abstract:

    Defect-carrier interaction in transition metal dichalcogenides (TMDs) plays important roles in carrier relaxation dynamics and carrier transport, which determines the performance of electronic devices. With femtosecond laser time-resolved spectroscopy, we investigated the effect of grain Boundary/Edge defects on the ultrafast dynamics of photoexcited carrier in molecular beam epitaxy (MBE)-grown MoTe2 and MoSe2. We found that, comparing with exfoliated samples, the carrier recombination rate in MBE-grown samples accelerates by about 50 times. We attribute this striking difference to the existence of abundant grain Boundary/Edge defects in MBE-grown samples, which can serve as effective recombination centers for the photoexcited carriers. We also observed coherent acoustic phonons in both exfoliated and MBE-grown MoTe2, indicating strong electron-phonon coupling in this materials. Our measured sound velocity agrees well with the previously reported result of theoretical calculation. Our findings provide a ...

  • Accelerated Carrier Recombination by Grain Boundary/Edge Defects in MBE Grown Transition Metal Dichalcogenides
    arXiv: Materials Science, 2018
    Co-Authors: Ke Chen, Anupam Roy, Amritesh Rai, Hema C. P. Movva, Xianghai Meng, Sanjay K. Banerjee, Yaguo Wang
    Abstract:

    Defect-carrier interaction in transition metal dichalcogenides (TMDs) play important roles in carrier relaxation dynamics and carrier transport, which determines the performance of electronic devices. With femtosecond laser time-resolved spectroscopy, we investigated the effect of grain Boundary/Edge defects on the ultrafast dynamics of photoexcited carrier in MBE grown MoTe2 and MoSe2. We found that, comparing with exfoliated samples, carrier recombination rate in MBE grown samples accelerates by about 50 times. We attribute this striking difference to the existence of abundant grain Boundary/Edge defects in MBE grown samples, which can serve as effective recombination centers for the photoexcited carriers. We also observed coherent acoustic phonons in both exfoliated and MBE grown MoTe2, indicating strong electron-phonon coupling in this materials. Our measured sound velocity agrees well with previously reported result of theoretical calculation. Our findings provide useful reference for the fundamental parameters: carrier lifetime and sound velocity, reveal the undiscovered carrier recombination effect of grain Boundary/Edge defects, both of which will facilitate the defect engineering in TMD materials for high speed opto-electronics.

  • accelerated carrier recombination by grain Boundary Edge defects in mbe grown transition metal dichalcogenides
    arXiv: Materials Science, 2018
    Co-Authors: Ke Chen, Anupam Roy, Amritesh Rai, Hema C. P. Movva, Xianghai Meng, Sanjay K. Banerjee, Yaguo Wang
    Abstract:

    Defect-carrier interaction in transition metal dichalcogenides (TMDs) play important roles in carrier relaxation dynamics and carrier transport, which determines the performance of electronic devices. With femtosecond laser time-resolved spectroscopy, we investigated the effect of grain Boundary/Edge defects on the ultrafast dynamics of photoexcited carrier in MBE grown MoTe2 and MoSe2. We found that, comparing with exfoliated samples, carrier recombination rate in MBE grown samples accelerates by about 50 times. We attribute this striking difference to the existence of abundant grain Boundary/Edge defects in MBE grown samples, which can serve as effective recombination centers for the photoexcited carriers. We also observed coherent acoustic phonons in both exfoliated and MBE grown MoTe2, indicating strong electron-phonon coupling in this materials. Our measured sound velocity agrees well with previously reported result of theoretical calculation. Our findings provide useful reference for the fundamental parameters: carrier lifetime and sound velocity, reveal the undiscovered carrier recombination effect of grain Boundary/Edge defects, both of which will facilitate the defect engineering in TMD materials for high speed opto-electronics.

  • Accelerated carrier recombination by grain Boundary/Edge defects in MBE grown transition metal dichalcogenides
    APL Materials, 2018
    Co-Authors: Ke Chen, Anupam Roy, Amritesh Rai, Hema C. P. Movva, Xianghai Meng, Sanjay K. Banerjee, Yaguo Wang
    Abstract:

    Defect-carrier interaction in transition metal dichalcogenides (TMDs) plays important roles in carrier relaxation dynamics and carrier transport, which determines the performance of electronic devices. With femtosecond laser time-resolved spectroscopy, we investigated the effect of grain Boundary/Edge defects on the ultrafast dynamics of photoexcited carrier in molecular beam epitaxy (MBE)-grown MoTe2 and MoSe2. We found that, comparing with exfoliated samples, the carrier recombination rate in MBE-grown samples accelerates by about 50 times. We attribute this striking difference to the existence of abundant grain Boundary/Edge defects in MBE-grown samples, which can serve as effective recombination centers for the photoexcited carriers. We also observed coherent acoustic phonons in both exfoliated and MBE-grown MoTe2, indicating strong electron-phonon coupling in this materials. Our measured sound velocity agrees well with the previously reported result of theoretical calculation. Our findings provide a ...

Tae-yong Kim - One of the best experts on this subject based on the ideXlab platform.

  • Accurate object contour tracking based on Boundary Edge selection
    Pattern Recognition, 2007
    Co-Authors: Myung-cheol Roh, Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    In this paper, a novel method for accurate subject tracking, by selecting only tracked subject Boundary Edges in a video stream with a changing background and moving camera, is proposed. This Boundary Edge selection is achieved in two steps: (1) removing background Edges using Edge motion, and from the output of the previous step, (2) selecting Boundary Edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant Edges, by only selecting Boundary Edge pixels. In order to remove background Edges using Edge motion, the tracked subject motion is computed and Edge motions and Edges having different motion directions from the subjects are removed. In selecting Boundary Edges using the normal contour direction, the image gradient values on every Edge pixel are computed, and Edge pixels with large gradient values are selected. Multi-level Canny Edge maps are used to obtain proper details of a scene. Multi-level Edge maps allow tracking, even though the tracked object Boundary has complex Edges, since the detail level of an Edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny Edge map and hiring strong Canny Edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.

  • ICPR (4) - Object Boundary Edge selection using normal direction derivatives of a contour in a complex scene
    2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    Nguyen proposed a method [H.T., Nguyen et al., 2002] for tracking a nonparameterized object (subject) contour in a single video stream. Nguyen's approach combined outputs of two steps: creating a predicted contour and removing background Edges. We propose a method to increase object tracking accuracy by improving the background Edge removal process. Nguyen's background Edge removal method of leaving many irrelevant Edges is subject to inaccurate contour tracking. Our accurate tracking is based on reducing affects from irrelevant Edges by selecting the Boundary Edge only. We select high-valued Edge pixels of average image intensity gradients in the contour normal direction. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

  • PRICAI - Object Boundary Edge selection for human body tracking using level-of-detail canny Edges
    PRICAI 2004: Trends in Artificial Intelligence, 2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    We propose a method for an accurate subject tracking by selecting only tracked subject Boundary Edges in a video stream with changing background and a moving camera. Our Boundary Edge selection is done in two steps; 1) remove background Edges using an Edge motion, 2) from the output of the previous step, select Boundary Edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant Edges by selecting Boundary Edge pixels only. In order to remove background Edges using the Edge motion, we compute tracked subject motion and Edge motions. The Edges with different motion direction than the subject motion are removed. In selecting Boundary Edges using the contour normal direction, we compute image gradient values on every Edge pixels, and select Edge pixels with large gradient values. We use multi-level Canny Edge maps to get proper details of a scene. Multi-level Edge maps allow us robust tracking even though the tracked object Boundary is not clear, because we can adjust the detail level of an Edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny Edge map and hiring strong Canny Edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

  • ICIAR (2) - Object Boundary Edge Selection for Accurate Contour Tracking Using Multi-level Canny Edges
    Lecture Notes in Computer Science, 2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    We propose a method of selecting only tracked subject Boundary Edges in a video stream with changing background and a moving camera. Our Boundary Edge selection is done in two steps; first, remove background Edges using an Edge motion, second, from the output of the previous step, select Boundary Edges using a normal direction derivative of the tracked contour. In order to remove background Edges, we compute Edge motions and object motions. The Edges with different motion direction than the subject motion are removed. In selecting Boundary Edges using the contour normal direction, we compute image gradient values on every Edge pixels, and select Edge pixels with large gradient values. We use multi-level Canny Edge maps to get proper details of a scene. Detailed-level Edge maps give us more scene information even though the tracked object Boundary is not clear, because we can adjust the detail level of Edge maps for a scene. We use Watersnake model to decide a new tracked contour. Our experimental results show that our approach is superior to Nguyen’s.

  • ICIAR (2) - LOD Canny Edge Based Boundary Edge Selection for Human Body Tracking
    Lecture Notes in Computer Science, 2004
    Co-Authors: Jihun Park, Tae-yong Kim, Sunghun Park
    Abstract:

    We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Our method is based on level-of-detail (LOD) Canny Edge maps and graph-based routing operations on the LOD maps. LOD Canny Edge maps are generated by changing scale parameters for a given image. Simple (strong) Canny Edge map has the smallest number of Edge pixels while the most detailed Canny Edge map, Wcanny N , has the biggest number of Edge pixels. We start our basic tracking using strong Canny Edges generated from large image intensity gradients of an input image, called Scanny Edges to reduce side-effects because of irrelevant Edges. Starting from Scanny Edges, we get more Edge pixels ranging from simple Canny Edge maps until the most detailed Canny Edge maps. LOD Canny Edge pixels become nodes in routing, and LOD values of adjacent Edge pixels determine routing costs between the nodes. We find a best route to follow Canny Edge pixels favoring stronger Canny Edge pixels. Our accurate tracking is based on reducing effects from irrelevant Edges by selecting the stronger Edge pixels, thereby relying on the current frame Edge pixel as much as possible contrary to other approaches of always combining the previous contour. Our experimental results show that this tracking approach is robust enough to handle a complex-textured scene.

Jihun Park - One of the best experts on this subject based on the ideXlab platform.

  • Accurate object contour tracking based on Boundary Edge selection
    Pattern Recognition, 2007
    Co-Authors: Myung-cheol Roh, Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    In this paper, a novel method for accurate subject tracking, by selecting only tracked subject Boundary Edges in a video stream with a changing background and moving camera, is proposed. This Boundary Edge selection is achieved in two steps: (1) removing background Edges using Edge motion, and from the output of the previous step, (2) selecting Boundary Edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant Edges, by only selecting Boundary Edge pixels. In order to remove background Edges using Edge motion, the tracked subject motion is computed and Edge motions and Edges having different motion directions from the subjects are removed. In selecting Boundary Edges using the normal contour direction, the image gradient values on every Edge pixel are computed, and Edge pixels with large gradient values are selected. Multi-level Canny Edge maps are used to obtain proper details of a scene. Multi-level Edge maps allow tracking, even though the tracked object Boundary has complex Edges, since the detail level of an Edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny Edge map and hiring strong Canny Edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.

  • ICPR (4) - Object Boundary Edge selection using normal direction derivatives of a contour in a complex scene
    2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    Nguyen proposed a method [H.T., Nguyen et al., 2002] for tracking a nonparameterized object (subject) contour in a single video stream. Nguyen's approach combined outputs of two steps: creating a predicted contour and removing background Edges. We propose a method to increase object tracking accuracy by improving the background Edge removal process. Nguyen's background Edge removal method of leaving many irrelevant Edges is subject to inaccurate contour tracking. Our accurate tracking is based on reducing affects from irrelevant Edges by selecting the Boundary Edge only. We select high-valued Edge pixels of average image intensity gradients in the contour normal direction. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

  • PRICAI - Object Boundary Edge selection for human body tracking using level-of-detail canny Edges
    PRICAI 2004: Trends in Artificial Intelligence, 2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    We propose a method for an accurate subject tracking by selecting only tracked subject Boundary Edges in a video stream with changing background and a moving camera. Our Boundary Edge selection is done in two steps; 1) remove background Edges using an Edge motion, 2) from the output of the previous step, select Boundary Edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant Edges by selecting Boundary Edge pixels only. In order to remove background Edges using the Edge motion, we compute tracked subject motion and Edge motions. The Edges with different motion direction than the subject motion are removed. In selecting Boundary Edges using the contour normal direction, we compute image gradient values on every Edge pixels, and select Edge pixels with large gradient values. We use multi-level Canny Edge maps to get proper details of a scene. Multi-level Edge maps allow us robust tracking even though the tracked object Boundary is not clear, because we can adjust the detail level of an Edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny Edge map and hiring strong Canny Edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

  • ICIAR (2) - Object Boundary Edge Selection for Accurate Contour Tracking Using Multi-level Canny Edges
    Lecture Notes in Computer Science, 2004
    Co-Authors: Tae-yong Kim, Jihun Park, Seong-whan Lee
    Abstract:

    We propose a method of selecting only tracked subject Boundary Edges in a video stream with changing background and a moving camera. Our Boundary Edge selection is done in two steps; first, remove background Edges using an Edge motion, second, from the output of the previous step, select Boundary Edges using a normal direction derivative of the tracked contour. In order to remove background Edges, we compute Edge motions and object motions. The Edges with different motion direction than the subject motion are removed. In selecting Boundary Edges using the contour normal direction, we compute image gradient values on every Edge pixels, and select Edge pixels with large gradient values. We use multi-level Canny Edge maps to get proper details of a scene. Detailed-level Edge maps give us more scene information even though the tracked object Boundary is not clear, because we can adjust the detail level of Edge maps for a scene. We use Watersnake model to decide a new tracked contour. Our experimental results show that our approach is superior to Nguyen’s.

  • ICCSA (4) - Object Boundary Edge Selection Using Level-of-Detail Canny Edges
    Computational Science and Its Applications – ICCSA 2004, 2004
    Co-Authors: Jihun Park, Sunghun Park
    Abstract:

    Recently, Nguyen proposed a method[1] for tracking a nonparameterized object (subject) contour in a single video stream with a moving camera and changing background. Nguyen’s approach combined outputs of two steps: creating a predicted contour and removing background Edges. Nguyen’s background Edge removal method of leaving many irrelevant Edges is subject to inaccurate contour tracking in a complex scene. Nguyen’s method[1] of combining the predicted contour computed from the previous frame accumulates tracking error. We propose a brand-new method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Our method is based on level-of-detail (LOD) Canny Edge maps and graph-based routing operations on the LOD maps. We compute a predicted contour as Nguyen do. But to reduce side-effects because of irrelevant Edges, we start our basic tracking using simple (strong) Canny Edges generated from large image intensity gradients of an input image, called Scanny Edges. Starting from Scanny Edges, we get more Edge pixels ranging from simple Canny Edge maps untill the most detailed (weaker) Canny Edge maps, called Wcanny maps. If Scanny Edges are disconnected, routing between disconnected parts are planned using level-of-detail Canny Edges, favoring stronger Canny Edge pixels. Our accurate tracking is based on reducing effects from irrelevant Edges by selecting the strongest Edge pixels only, thereby relying on the current frame Edge pixel as much as possible contrary to Nguyen’s approach of always combining the previous contour. Our experimental results show that this tracking approach is robust enough to handle a complex-textured scene.

William B Gordon - One of the best experts on this subject based on the ideXlab platform.

  • contour integral representation for near field backscatter from a flat plate
    IEEE Transactions on Antennas and Propagation, 2012
    Co-Authors: William B Gordon
    Abstract:

    We consider the near field backscatter from a flat plate illuminated by a dipole source. The physical optics scattering integral is reduced to a contour integral evaluated around the Boundary Edge of the plate.

  • Calculating Scatter From Surfaces With Zero Curvature
    2003
    Co-Authors: William B Gordon
    Abstract:

    The far-field physical optics scattering integral for scatter from a surface element is an area integral evaluated over . This integral can be reduced to a contour integral evaluated around the Boundary Edge of when the Gaussian curvature of is everywhere equal to zero. Such surface elements include regions on planes, cylinders, cones, and, more gen- erally, any type of developable surface.

  • High frequency approximations to the physical optics scattering integral
    IEEE Transactions on Antennas and Propagation, 1994
    Co-Authors: William B Gordon
    Abstract:

    Discusses two high frequency (HF) approximations to the physical optics (PO) scattering integral for the far field radar backscatter from a general curved Edged reflecting surface viewed at arbitary aspect. The PO scattering integral is first approximated as the sum of a specular effect and an Edge effect, where the latter is represented explicitly as a certain line integral evaluated over the Boundary Edge of the reflector. A closed form result is then obtained by applying the method of stationary phase to the line integral. With the exception of singularities that can occur at caustics, or when the specular point falls on the Boundary Edge, these HF approximations are found to work reasonably well for smooth surfaces whose Gaussian curvatures have constant sign (positive or negative, but never zero). >