The Experts below are selected from a list of 65199 Experts worldwide ranked by ideXlab platform

Manuel Grizonnet - One of the best experts on this subject based on the ideXlab platform.

  • Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
    IEEE Transactions on Computational Imaging, 2017
    Co-Authors: Adrien Lagrange, Mathieu Fauvel, Manuel Grizonnet
    Abstract:

    A large scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and Block Matrix. The criterion function is split into two parts : one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time.

Adrien Lagrange - One of the best experts on this subject based on the ideXlab platform.

  • Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
    IEEE Transactions on Computational Imaging, 2017
    Co-Authors: Adrien Lagrange, Mathieu Fauvel, Manuel Grizonnet
    Abstract:

    A large scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and Block Matrix. The criterion function is split into two parts : one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time.

Guy Melard - One of the best experts on this subject based on the ideXlab platform.

P Hasler - One of the best experts on this subject based on the ideXlab platform.

  • matia a programmable 80 spl mu w frame cmos Block Matrix transform imager architecture
    IEEE Journal of Solid-state Circuits, 2006
    Co-Authors: Abhishek Bandyopadhyay, Jungwon Lee, Ryan Robucci, P Hasler
    Abstract:

    In this paper, we introduce our CMOS Block Matrix Transform Imager Architecture (MATIA). This imager is capable of performing programmable Matrix operations on an image. The imager architecture is both modular and programmable. The pixel used in this architecture performs Matrix multiplication while maintaining a high fill factor (46%), comparable to active pixel sensors. Floating gates are used to store the arbitrary Matrix coefficients on-chip. The chip operates in the subthreshold domain and thus has low power consumption (80 /spl mu/W/frame). We present data for different convolutions and Block transforms that were implemented using this architecture, and also present data from baseline JPEG and motion JPEG systems which we have implemented using MATIA.

  • a 80 spl mu w frame 104 spl times 128 cmos imager front end for jpeg compression
    International Symposium on Circuits and Systems, 2005
    Co-Authors: Abhishek Bandyopadhyay, Ryan Robucci, Junghee Lee, P Hasler
    Abstract:

    We present a programmable 80 /spl mu/W/frame (3.3 V supply) single-chip architecture that combines a CMOS imager and an analog image processor capable of computing separable Block Matrix transforms (DCT, Haar, etc). Floating-gate technology is used for on-chip kernel storage and also for performing low-power current-mode Matrix multiplications. We demonstrate this IC as a front-end for JPEG compression and compare the performance of this imager to fully digital approaches.

Andre Klein - One of the best experts on this subject based on the ideXlab platform.