The Experts below are selected from a list of 61023 Experts worldwide ranked by ideXlab platform

Costas Xydeas - One of the best experts on this subject based on the ideXlab platform.

  • Sensor noise effects on signal level image fusion performance
    Information Fusion, 2003
    Co-Authors: Vladimir Petrovic, Costas Xydeas
    Abstract:

    The aim of this paper is twofold: (i) to define appropriate metrics which measure the effects of Input Sensor noise on the performance of signal-level image fusion systems and (ii) to employ these metrics in a comparative study of the robustness of typical image fusion schemes whose Inputs are corrupted with noise. Thus system performance metrics for measuring both absolute and relative degradation in fused image quality are proposed when fusing noisy Input modalities. A third metric, which considers fusion of noise patterns, is also developed and used to evaluate the perceptual effect of noise corrupting homogenous image regions (i.e. areas with no salient features). These metrics are employed to compare the performance of different image fusion methodologies and feature selection/information fusion strategies operating under noisy Input conditions. Altogether, the performance of seventeen fusion schemes is examined and their robustness to noise considered at various Input signal-to-noise ratio values for three types of Sensor noise characteristics.

Vladimir Petrovic - One of the best experts on this subject based on the ideXlab platform.

  • Sensor noise effects on signal level image fusion performance
    Information Fusion, 2003
    Co-Authors: Vladimir Petrovic, Costas Xydeas
    Abstract:

    The aim of this paper is twofold: (i) to define appropriate metrics which measure the effects of Input Sensor noise on the performance of signal-level image fusion systems and (ii) to employ these metrics in a comparative study of the robustness of typical image fusion schemes whose Inputs are corrupted with noise. Thus system performance metrics for measuring both absolute and relative degradation in fused image quality are proposed when fusing noisy Input modalities. A third metric, which considers fusion of noise patterns, is also developed and used to evaluate the perceptual effect of noise corrupting homogenous image regions (i.e. areas with no salient features). These metrics are employed to compare the performance of different image fusion methodologies and feature selection/information fusion strategies operating under noisy Input conditions. Altogether, the performance of seventeen fusion schemes is examined and their robustness to noise considered at various Input signal-to-noise ratio values for three types of Sensor noise characteristics.

Luca Benini - One of the best experts on this subject based on the ideXlab platform.

  • design automation for binarized neural networks a quantum leap opportunity
    International Symposium on Circuits and Systems, 2018
    Co-Authors: Manuele Rusci, Lukas Cavigelli, Luca Benini
    Abstract:

    Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-Sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the Sensor and coupling them with binarized mixed-signal image Sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22 nm SOI technology shows a silicon area of 2.61 mm2 for implementing a combinational BNN with 32×32 binary Input Sensor receptive field and weight parameters fixed at design time. This is 2.2× smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-Sensor processing, our approach features a 10× higher energy efficiency.

  • design automation for binarized neural networks a quantum leap opportunity
    arXiv: Other Computer Science, 2017
    Co-Authors: Manuele Rusci, Lukas Cavigelli, Luca Benini
    Abstract:

    Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-Sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the Sensor and coupling it with binarized mixed-signal image Sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22nm SOI technology shows a silicon area of 2.61mm2 for implementing a combinational BNN with 32x32 binary Input Sensor receptive field and weight parameters fixed at design time. This is 2.2x smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-Sensor processing, our approach features a 10x higher energy efficiency.

Steven Bathiche - One of the best experts on this subject based on the ideXlab platform.

  • MonoFusion: Real-time 3D reconstruction of small scenes with a single web camera
    2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2013
    Co-Authors: Vivek Pradeep, Christoph Rhemann, Shahram Izadi, Christopher Zach, Michael Bleyer, Steven Bathiche
    Abstract:

    MonoFusion allows a user to build dense 3D reconstructions of their environment in real-time, utilizing only a single, off-the-shelf web camera as the Input Sensor. The camera could be one already available in a tablet, phone, or a standalone device. No additional Input hardware is required. This removes the need for power intensive active Sensors that do not work robustly in natural outdoor lighting. Using the Input stream of the camera we first estimate the 6DoF camera pose using a sparse tracking method. These poses are then used for efficient dense stereo matching between the Input frame and a key frame (extracted previously). The resulting dense depth maps are directly fused into a voxel-based implicit model (using a computationally inexpensive method) and surfaces are extracted per frame. The system is able to recover from tracking failures as well as filter out geometrically inconsistent noise from the 3D reconstruction. Our method is both simple to implement and efficient, making such systems even more accessible. This paper details the algorithmic components that make up our system and a GPU implementation of our approach. Qualitative results demonstrate high quality reconstructions even visually comparable to active depth Sensor-based systems such as KinectFusion.

Manuele Rusci - One of the best experts on this subject based on the ideXlab platform.

  • design automation for binarized neural networks a quantum leap opportunity
    International Symposium on Circuits and Systems, 2018
    Co-Authors: Manuele Rusci, Lukas Cavigelli, Luca Benini
    Abstract:

    Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-Sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the Sensor and coupling them with binarized mixed-signal image Sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22 nm SOI technology shows a silicon area of 2.61 mm2 for implementing a combinational BNN with 32×32 binary Input Sensor receptive field and weight parameters fixed at design time. This is 2.2× smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-Sensor processing, our approach features a 10× higher energy efficiency.

  • design automation for binarized neural networks a quantum leap opportunity
    arXiv: Other Computer Science, 2017
    Co-Authors: Manuele Rusci, Lukas Cavigelli, Luca Benini
    Abstract:

    Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-Sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the Sensor and coupling it with binarized mixed-signal image Sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22nm SOI technology shows a silicon area of 2.61mm2 for implementing a combinational BNN with 32x32 binary Input Sensor receptive field and weight parameters fixed at design time. This is 2.2x smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-Sensor processing, our approach features a 10x higher energy efficiency.