The Experts below are selected from a list of 35814 Experts worldwide ranked by ideXlab platform

Juha Kostamovaara - One of the best experts on this subject based on the ideXlab platform.

  • Combining the Standard Histogram Method and a Stimulus Identification Algorithm for A/D Converter INL Testing With a Low-Quality Sine Wave Stimulus
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2010
    Co-Authors: Esa Korhonen, Carsten Wegener, Juha Kostamovaara
    Abstract:

    This paper proposes combining the standard Histogram Method with a stimulus identification algorithm in order to test the integral nonlinearity (INL) of a high-resolution analog-to-digital (A/D) converter without a high-quality sine wave. The major problems in the two techniques are explained in order to appreciate the benefits of the combination. The increased INL estimation accuracy is verified with simulations of 16-b A/D converters under different conditions, and experimental results of the INL testing of a 16-b A/D converter are also quoted to support the theory. The simulations and experimental tests show that the INL of 16-b A/D converters can be measured with very simple equipment and that an accurate test stimulus is not necessary.

Esa Korhonen - One of the best experts on this subject based on the ideXlab platform.

  • Combining the Standard Histogram Method and a Stimulus Identification Algorithm for A/D Converter INL Testing With a Low-Quality Sine Wave Stimulus
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2010
    Co-Authors: Esa Korhonen, Carsten Wegener, Juha Kostamovaara
    Abstract:

    This paper proposes combining the standard Histogram Method with a stimulus identification algorithm in order to test the integral nonlinearity (INL) of a high-resolution analog-to-digital (A/D) converter without a high-quality sine wave. The major problems in the two techniques are explained in order to appreciate the benefits of the combination. The increased INL estimation accuracy is verified with simulations of 16-b A/D converters under different conditions, and experimental results of the INL testing of a 16-b A/D converter are also quoted to support the theory. The simulations and experimental tests show that the INL of 16-b A/D converters can be measured with very simple equipment and that an accurate test stimulus is not necessary.

Tadahiro Ohmi - One of the best experts on this subject based on the ideXlab platform.

  • an improved fast face recognition algorithm based on adjacent pixel intensity difference quantization Histogram
    International Conference on Wavelet Analysis and Pattern Recognition, 2008
    Co-Authors: Koji Kotani, Qiu Chen, Tadahiro Ohmi
    Abstract:

    In this paper, we present an improved face recognition algorithm based on adjacent pixel intensity difference quantization (APIDQ) Histogram Method proposed by Kotani et al. [12]. We optimize the quantization Method of APIDQ according to the maximum entropy principle (MEP), and determine the best parameters for APIDQ. Experimental results show maximum average recognition rate of 97.2% for 400 images of 40 persons (10 images per person) from the publicly available AT&T face database.

  • Region-Division Vector Quantization Histogram Method for Human Face Recognition
    Intelligent Automation & Soft Computing, 2006
    Co-Authors: Koji Kotani, Qiu Chen, Feifei Lee, Tadahiro Ohmi
    Abstract:

    Abstract We have developed a very simple yet highly reliable face recognition Method called VQ Histogram Method codevector referred (or matched) count Histogram, which is obtained by Vector Quantization (VQ) processing of facial image, is utilized as a very effective personal feature value. Furthermore, for adding the geometric information of the face to improve the recognition accuracy, aregion-division (RD) VQ Histogram Method is proposed in this paper. We divide the facial area into 5 regions relating to the facial parts (forehead, eye, nose, mouth, jaw). Recognition results with different parts are fast obtained separately and then combined by weighted averaging. Topl recognition rate of 97.4% is obtained by using FB task (1195 images) in the standard FERET database. By using the private database, which was taken in practical but yet reasonably regulated environrnent, Topl recognition rate of 100% is realized.

  • Robust Face Detection Combined Skin Color Features, Template Matching and VQ Histogram Method in Practical Environments
    Intelligent Automation & Soft Computing, 2004
    Co-Authors: Qiu Chen, Koji Kotani, Yoshiyuki Taniguchi, Zhibin Pan, Tadahiro Ohmi
    Abstract:

    Abstract The face detection in complex background is still difficult by existing techniques. We present an approach to real-time face detection in practical environrnents using multi-module integration. We combine color extraction module, geometrical Method, template-based module and face recognition module named VQ Histogram Method into a single robust face detection system. First, the skin color is used to locate the skin color areas and possible face candidates aze extracted by geometrical constraints, then template matching is carried out using appropriate-sized templates to extract the point of face, fmally a very simple yet highly reliable face recognition Method called VQ Histogram Method is utilized to verify whether it is the face or not. The Method is proved very effective in a practical environrnent.

  • face recognition using vector quantization Histogram Method
    International Conference on Image Processing, 2002
    Co-Authors: Koji Kotani, Tadahiro Ohmi
    Abstract:

    We have developed a very simple yet highly reliable face recognition Method called the VQ Histogram Method. A codevector referred (or matched) count Histogram, which is obtained by vector quantization (VQ) processing of the facial image, is utilized as a very effective personal feature. By applying appropriate low pass filtering and VQ processing to a facial image, useful features for face recognition can be extracted. Experimental results show a recognition rate of 95.6% for 400 images of 40 persons (10 images per person), which contain variations in lighting, pose, and expression, from the publicly available ORL database. Equal error rate (ERR) of 2.6% is obtained for the verification experiment. By combining multiple low pass filtering procedures, the recognition rate is increased to 97% or higher.

  • ICIP (2) - Face recognition using vector quantization Histogram Method
    Proceedings. International Conference on Image Processing, 1
    Co-Authors: Koji Kotani, Chen Qiu, Tadahiro Ohmi
    Abstract:

    We have developed a very simple yet highly reliable face recognition Method called the VQ Histogram Method. A codevector referred (or matched) count Histogram, which is obtained by vector quantization (VQ) processing of the facial image, is utilized as a very effective personal feature. By applying appropriate low pass filtering and VQ processing to a facial image, useful features for face recognition can be extracted. Experimental results show a recognition rate of 95.6% for 400 images of 40 persons (10 images per person), which contain variations in lighting, pose, and expression, from the publicly available ORL database. Equal error rate (ERR) of 2.6% is obtained for the verification experiment. By combining multiple low pass filtering procedures, the recognition rate is increased to 97% or higher.

Carsten Wegener - One of the best experts on this subject based on the ideXlab platform.

  • Combining the Standard Histogram Method and a Stimulus Identification Algorithm for A/D Converter INL Testing With a Low-Quality Sine Wave Stimulus
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2010
    Co-Authors: Esa Korhonen, Carsten Wegener, Juha Kostamovaara
    Abstract:

    This paper proposes combining the standard Histogram Method with a stimulus identification algorithm in order to test the integral nonlinearity (INL) of a high-resolution analog-to-digital (A/D) converter without a high-quality sine wave. The major problems in the two techniques are explained in order to appreciate the benefits of the combination. The increased INL estimation accuracy is verified with simulations of 16-b A/D converters under different conditions, and experimental results of the INL testing of a 16-b A/D converter are also quoted to support the theory. The simulations and experimental tests show that the INL of 16-b A/D converters can be measured with very simple equipment and that an accurate test stimulus is not necessary.

Degang Chen - One of the best experts on this subject based on the ideXlab platform.

  • ultrafast stimulus error removal algorithm for adc linearity test
    VLSI Test Symposium, 2015
    Co-Authors: Tao Chen, Degang Chen
    Abstract:

    Linearity test of an analog-to-digital converter (ADC) can be very challenging because it requires a signal generator substantially more linear than the ADC under test. For high performance ADCs, the overall manufacturing cost could be dominated by the long test time and the high-precision test instruments. This paper introduces the ultrafast stimulus error removal and segmented model identification of linearity errors (USER-SMILE) Method for high resolution ADC linearity test, allowing the stimulus signal's linearity requirement to be significantly relaxed and the test time to be reduced by orders of magnitude compared to the state-of-art Histogram Method. The USER-SMILE algorithm uses two nonlinear but functionally related input signals as ADC excitations and uses a stimulus error removal technique to recover test accuracy. The USER-SMILE algorithm also uses the ultrafast segmented model identification of linearity errors (uSMILE) approach to dramatically reduce test time while achieving test accuracy and coverage superior to the Histogram Method. The USER-SMILE algorithm is validated by extensive simulation with different types of ADCs, different resolution levels, and different types of input signals including nonlinear ramps, nonlinear sine waves and even random input signals. Statistical simulation results show that for a 16-bit SAR ADC, with two 1 hit/code nonlinear ramp signals, the INL test error is within +/− 0.4LSB.