The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

Toshiyuki Ishii - One of the best experts on this subject based on the ideXlab platform.

  • a 1 2 w single chip mpeg2 mp ml video encoder lsi including wide search range h spl plusmn 288 v spl plusmn 96 Motion estimation and 81 mops controller
    IEEE Journal of Solid-state Circuits, 1998
    Co-Authors: Eiji Ogura, Masatoshi Takashima, Daisuke Hiranaka, T Ishikawa, Yukio Yanagita, S Suzuki, T Fukuda, Toshiyuki Ishii
    Abstract:

    An MPEG2 MP@ML video encoder large-scale integrated circuit (LSI) has been developed including an 81 MOPS controller and Motion Estimator. By using two adaptive algorithms, a wide Motion-estimation search area (/spl plusmn/288 pixels horizontal and /spl plusmn/96 pixels vertical) was achieved with computation complexity of only 0.5% (20 GOPS) of full search block-matching algorithm. By using this expanded Motion-estimation search area, there is a significant improvement in picture quality for coding fast Motion sequences. The power consumption was reduced by using an efficient pipeline architecture and optimizing the circuitry, especially in the Motion-estimation block and the data transfers for the external SDRAM. The 13.7/spl times/12.4 mm/sup 2/, 4.5-M transistor device using 0.4-/spl mu/m CMOS technology dissipates 1.2 W at 3.3 V.

Sebastiano Battiato - One of the best experts on this subject based on the ideXlab platform.

  • a robust image alignment algorithm for video stabilization purposes
    IEEE Transactions on Circuits and Systems for Video Technology, 2011
    Co-Authors: Giovanni Puglisi, Sebastiano Battiato
    Abstract:

    Today, many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (personal digital assistants, mobile phones, etc.), take videos. However, the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. Many video stabilization techniques have been hence developed with different performances but only fast strategies can be implemented on embedded devices. A fundamental issue is the overall robustness with respect to different scene contents (indoor, outdoor, etc.) and conditions (illumination changes, moving objects, etc.). In this paper, we propose a fast and robust image alignment algorithm for video stabilization purposes. Our contribution is twofold: a fast and accurate block-based local Motion Estimator together with a robust alignment algorithm based on voting. Experimental results confirm the effectiveness of both local and global Motion Estimators.

  • a robust image alignment algorithm for video stabilization purposes
    IEEE Transactions on Circuits and Systems for Video Technology, 2011
    Co-Authors: Giovanni Puglisi, Sebastiano Battiato
    Abstract:

    Today, many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (personal digital assistants, mobile phones, etc.), take videos. However, the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. Many video stabilization techniques have been hence developed with different performances but only fast strategies can be implemented on embedded devices. A fundamental issue is the overall robustness with respect to different scene contents (indoor, outdoor, etc.) and conditions (illumination changes, moving objects, etc.). In this paper, we propose a fast and robust image alignment algorithm for video stabilization purposes. Our contribution is twofold: a fast and accurate block-based local Motion Estimator together with a robust alignment algorithm based on voting. Experimental results confirm the effectiveness of both local and global Motion Estimators.

Etienne Memin - One of the best experts on this subject based on the ideXlab platform.

  • Image assimilation for Motion estimation of atmospheric layers with shallow-water model
    2016
    Co-Authors: Nicolas Papadakis, Patrick Heas, Etienne Memin
    Abstract:

    The complexity of dynamical laws governing 3D atmospheric flows associated to incomplete and noisy observations makes very difficult the recovery of atmospheric dynamics from satellite images sequences. In this paper, we face the challenging problem of joint estimation of time-consistent horizontal Motion fields and pressure maps at various atmospheric depths. Based on a vertical decomposition of the atmosphere, we propose a dense Motion Estimator relying on a multi-layer dynamical model. Noisy and incomplete pressure maps obtained from satellite images are reconstructed according to shallow-water model on each cloud layer using a framework derived from data assimilation. While reconstructing dense pressure maps, this variational process estimates time-consistent horizontal Motion fields related to the multi-layer model. The proposed approach is validated on a synthetic example and applied to a real world meteorological satellite image sequence.

  • Variational Pressure Image Assimilation for Atmospheric Motion Estimation
    IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
    Co-Authors: Thomas Corpetti, Patrick Heas, Etienne Memin, Nicolas Papadakis
    Abstract:

    The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite images sequences very difficult. In this paper, we face the challenging problem of estimating physical sound and time-consistent horizontal Motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric Motion Estimator relying on a multi-layer dynamical model. This Estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamical model consists in a simplified vorticity-divergence form of a multi-layer shallow-water model. Average horizontal Motion fields are estimated for each layer. The performance of the proposed technique is assessed on real world meteorological satellite image sequences.

  • Pressure image assimilation for atmospheric Motion estimation
    Tellus A, 2008
    Co-Authors: Thomas Corpetti, Patrick Heas, Etienne Memin, Nicolas Papadakis
    Abstract:

    The complexity of the laws of dynamics governing 3-D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite image sequences very difficult. In this paper, we address the challenging problem of estimating physical sound and time-consistent horizontal Motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric Motion Estimator relying on a multilayer dynamic model. This Estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamic model is a simplified vorticity-divergence form of a multilayer shallow-water model. Average horizontal Motion fields are estimated for each layer. The performance of the proposed technique is assessed using synthetic examples and using real world meteorological satellite image sequences. In particular, it is shown that the Estimator enables exploiting fine spatio-temporal image structures and succeeds in characterizing Motion at small spatial scales.

  • A low dimensional fluid Motion Estimator
    International Journal of Computer Vision, 2007
    Co-Authors: Anne Cuzol, Pierre Hellier, Etienne Memin
    Abstract:

    In this paper we propose a new Motion Estimator for image sequences depicting fluid flows. The proposed Estimator is based on the Helmholtz decomposition of vector fields. This decomposition consists in representing the velocity field as a sum of a divergence free component and a vorticity free component. The objective is to provide a low-dimensional parametric representation of optical flows by depicting them as deformations generated by a reduced number of vortex and source particles. Both components are approximated using a discretization of the vorticity and divergence maps through regularized Dirac measures. The resulting so called irrotational and solenoidal fields consist of linear combinations of basis functions obtained through a convolution product of the Green kernel gradient and the vorticity map or the divergence map respectively. The coefficient values and the basis function parameters are obtained by minimization of a functional relying on an integrated version of mass conservation principle of fluid mechanics. Results are provided on synthetic examples and real world sequences.

  • a consistent spatio temporal Motion Estimator for atmospheric layers
    International Conference on Scale Space and Variational Methods in computer vision, 2007
    Co-Authors: Patrick Heas, Etienne Memin, Nicolas Papadakis
    Abstract:

    In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Relying on a physically sound vertical decomposition of the atmosphere into layers, we propose a dense Motion Estimator dedicated to the extraction of multi-layer horizontal wind fields. This Estimator is expressed as the minimization of a global function including a data term and a spatiotemporal smoothness term. A robust data term relying on shallow-water mass conservation model is proposed to fit sparse observations related to each layer. A novel spatio-temporal regularizer derived from shallow-water momentum conservation model is proposed to enforce a temporal consistency of the solution along the sequence time range. These constraints are combined with a robust second-order regularizer preserving divergent and vorticity structures of the flow. In addition, a two-level Motion estimation scheme is proposed to overcome the limitations of the multiresolution incremental scheme when capturing the dynamics of fine mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by Direct Numerical Simulation of a turbulent bi-dimensional flow and then on a Meteosat infrared image sequence.

Eiji Ogura - One of the best experts on this subject based on the ideXlab platform.

  • a 1 2 w single chip mpeg2 mp ml video encoder lsi including wide search range h spl plusmn 288 v spl plusmn 96 Motion estimation and 81 mops controller
    IEEE Journal of Solid-state Circuits, 1998
    Co-Authors: Eiji Ogura, Masatoshi Takashima, Daisuke Hiranaka, T Ishikawa, Yukio Yanagita, S Suzuki, T Fukuda, Toshiyuki Ishii
    Abstract:

    An MPEG2 MP@ML video encoder large-scale integrated circuit (LSI) has been developed including an 81 MOPS controller and Motion Estimator. By using two adaptive algorithms, a wide Motion-estimation search area (/spl plusmn/288 pixels horizontal and /spl plusmn/96 pixels vertical) was achieved with computation complexity of only 0.5% (20 GOPS) of full search block-matching algorithm. By using this expanded Motion-estimation search area, there is a significant improvement in picture quality for coding fast Motion sequences. The power consumption was reduced by using an efficient pipeline architecture and optimizing the circuitry, especially in the Motion-estimation block and the data transfers for the external SDRAM. The 13.7/spl times/12.4 mm/sup 2/, 4.5-M transistor device using 0.4-/spl mu/m CMOS technology dissipates 1.2 W at 3.3 V.

Suoik Chae - One of the best experts on this subject based on the ideXlab platform.

  • new Motion estimation algorithm using adaptively quantized low bit resolution image and its vlsi architecture for mpeg2 video encoding
    IEEE Transactions on Circuits and Systems for Video Technology, 1998
    Co-Authors: Suoik Chae
    Abstract:

    This paper describes a new Motion estimation algorithm that is suitable for hardware implementation and substantially reduces the hardware cost by using a low bit-resolution image in the block matching. In the low bit-resolution image generation, adaptive quantization is employed to reduce the bit resolution of the pixel values, which is better than simple truncation of the least significant bits in preserving the dynamic range of the pixel values. The proposed algorithm consists of two search steps: in the low-resolution search, a set of candidate Motion vectors is determined, and in the full-resolution search, the Motion vector is found from these candidate Motion vectors. The hardware cost of the proposed algorithm is 1/17 times of the full search algorithm, while its peak signal-to-noise ratio is better than that of the 4:1 alternate subsampling for the search range of /spl plusmn/32/spl times//spl plusmn/32. A VLSI architecture of the proposed algorithm is also described, which can concurrently perform two prediction modes of the MPEG2 video standard with the search range of (-32.0,-32.0)-(+31.5,+31.5). We fabricated a MPEG2 Motion Estimator with a 0.5-/spl mu/m triple-metal CMOS technology. The VLSI chip includes 110 K gates of random logic and 90 K bits of SRAM in a die size of 11.5 mm/spl times/12.5 mm. The full functionality of the fabricated chip was confirmed with an MPEG2 encoder chip.