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Using data visualization and signal processing to characterize the handwriting process

Pediatric Rehabilitation 2006年第4期9卷 404-417页

作者： Rosenblum, Sara Chevion, Dan Weiss, Patrice L. Department of Occupational Therapy Faculty of Social Welfare and Health Studies University of Haifa Haifa Israel Department of Image and Signal Processing IBM Research Lab. in Haifa University of Haifa Israel Department of Occupational Therapy University of Haifa Mount Carmel Haifa 31905 Israel

Introduction: Disturbances in handwriting legibility and speed are found among elementary school-aged children. The aim of this paper is to present a set of sophisticated analytical tools suitable for visualization and evaluation of handwriting disturbances. Methods: Handwriting samples from 30 children, 15 proficient and 15 non-proficient handwriters, aged 8-9 years were collected with the aid of a digitizing tablet. Temporal and spatial measures of the handwriting process dynamics based on signal processing methods were developed and visually presented. Results: Significant differences between proficient and non-proficient handwriters were found in handwriting characteristics such as the standard deviations of letter width (t = 2.96, p = 0.008), letter height (t = 3.24, p = 0.005) and pen elevation (t = 2.91, p = 0.008). Significant differences were also found for the number of pen lifts (t = 2.27, p = 0.03), for the value of the correlation coefficients between letter length and time (t = -6.62, p = 0.000) and between the actual and computed number of words (t = 2.79, p = 0.01). Conclusions: The techniques described in this paper provide objective measures for handwriting performance presented in a way designed to help clinicians and educators visualize handwriting difficulties during clinical evaluation and intervention. Data visualization and analysis appear to enhance information concerning the spatial and temporal dynamics of handwriting. © 2006 Informa UK Ltd.

关键词： Digitizer Evaluation Handwriting Visualization

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Wideband time reversal processing for ultrasound NDE imaging

Wideband time reversal processing for ultrasound NDE imaging

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IEEE Symposium (IUS) Ultrasonics

作者： Cindy Bernard Ramazan Demirli Cornel Ioana Signal and Image Processing Lab National Polytechnique Institute of Grenoble France Villanova University Villanova PA US

Time-reversal technique based on the decomposition of the time-reversal operator (TR-DORT) using large arrays of transducers has been used in ultrasound imaging for many years. However, this technique forms an image based on the center frequency of emission and has limitations such as poor target resolution, sensitivity to noise, and the requirement of large number of array elements with dense spatial sampling. In order to overcome these limitations, we investigate wideband TR imaging techniques emerged in radar. Wideband imaging bears clear advantages over its narrowband counter, most notably, enhanced resolution and improved robustness with noise. In this paper, we employ two wideband imaging techniques in the context of nondestructive evaluation of materials, namely, wideband Multiple signal Classification (WB-MUSIC) and the Time-Domain DORT (TD-DORT) algorithms. Both techniques utilize wideband transmitted ultrasound signals, and can be applied using monochromatic signals or time-pulse. The capability of these techniques in providing improved target localization and efficient sensor deployment in low signal-to-noise ratio environments is demonstrated.

关键词： Imaging Array signal processing Transducers Sensor arrays Wideband Ultrasonic imaging image resolution

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Design and Implementation of a Manipulator System for Roadway Crack Sealing

Design and Implementation of a Manipulator System for Roadwa...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Guijie Zhu Zhun Fan Wenzhao Chen Yugen You Sibo Huang Weixiang Liang Runzhan Fu Jiaming Xin Jingming Chen Furong Deng Youzhao Hou Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering Shantou University Shantou China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Crack detection and crack sealing are challenging tasks which have been researched for decades due to the complicated roadway conditions in real world. Especially, crack sealing is an important procedure used to extend the life of road pavement. This paper develops and implements a kind of manipulator system for roadway crack sealing, which consists of a six-degree-of-freedom(6-DoF) manipulator with a novel crack sealing end-effector, a stereo camera, an air pump, a glue barrel and a laptop. The paper presents the hardware construction and working schematic of the manipulation system. Before crack sealing, a supervised method based on deep learning is used to detect the cracks and obtain the cracks' image information, which can be merged with depth information to acquire the accurate location information of cracks. We validate the feasibility of the crack sealing end-effector which can be applied to seal cracks on uneven road surface by indoor simulation tests.

关键词： Manipulators Roads Cameras Surface cracks Acoustics Robot sensing systems Task analysis

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Iterative Fingerprint Enhancement with Matched Filtering and Quality Diffusion in Spatial-Frequency Domain

Iterative Fingerprint Enhancement with Matched Filtering and...

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International Conference on Pattern Recognition

作者： Prawit Sutthiwichaiporn Vutipong Areekul Suksan Jirachaweng Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Kasetsart University Bangkok Thailand

ISBN: (纸本)9781424475421

The proposed fingerprint enhancement algorithm utilizes power spectrum in spatial-frequency domain. The input fingerprint is partitioned and assessed as high/low quality zone by using signal-to-noise ratio (SNR) approach. For high quality zone, signal spectrum with noise suppression is used to shape an enhanced filter in frequency domain. Then, this algorithm feed neighboring enhanced zone back in order to repair unreliable low quality region. The proposed algorithm out-performs Gabor and STFT approaches by fingerprint matching experiments on FVC2004 Db2 and Db3.

关键词： Fingerprint recognition signal to noise ratio Matched filters Frequency domain analysis Algorithm design and analysis

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CANONICAL CEREBELLAR GRAPH WAVELETS AND THEIR APPLICATION TO FMRI ACTIVATION MAPPING

CANONICAL CEREBELLAR GRAPH WAVELETS AND THEIR APPLICATION TO...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Hamid Behjat Nora Leonardi Leif Sornmo Dimitri Van De Ville Signal Processing Group Department of Biomedical Engineering Lund University Medical Image Processing Lab (MIPLAB) Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781424479276

Wavelet-based statistical parametric mapping (WSPM) is an extension of the classical approach in fMRI activation mapping that combines wavelet processing with voxel-wise statistical testing. We recently showed how WSPM, using graph wavelets tailored to the full gray-matter (GM) structure of each individual's brain, can improve brain activity detection compared to using the classical wavelets that are only suited for the Euclidian grid. However, in order to perform analysis on a subject-invariant graph, canonical graph wavelets should be designed in normalized brain space. We here introduce an approach to define a fixed template graph of the cerebellum, an essential component of the brain, using the SUIT cerebellar template. We construct a corresponding set of canonical cerebellar graph wavelets, and adopt them in the analysis of both synthetic and real data. Compared to classical SPM, WSPM using cerebellar graph wavelets shows superior type-I error control, an empirical higher sensitivity on real data, as well as the potential to capture subtle patterns of cerebellar activity.

关键词： Statistical testing Functional MRI Cerebellum Spectral graph theory Graph wavelet transform Wavelet thresholding

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Fingerprint Quality Assessment Using Frequency and Orientation Subbands of Block-based Fourier Transform

Fingerprint Quality Assessment Using Frequency and Orientati...

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IAPR International Conference on Biometrics

作者： Krisada Phromsuthirak Vutipong Areekul Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Faculty of Engineering Kasetsart University

ISBN: (纸本)9781479903115

A new fingerprint quality measure based on subband analysis of spatial-frequency domain is presented. The overlapped block-based Fourier transform is projected onto a new subband platform, which is related to frequency and orientations of ridges. Based on this platform, three proposed intrinsic spectral characteristics for fingerprint quality assessment can be extracted, i.e. global spectrum, relative spectral density, and relative spectral orientation continuity. The proposed method has an advantage of evaluating ridge spectrum directly, without suffering from incorrect parameter estimation such as directional field. The experimental results show that the discrimination capability of the proposed method is superior to conventional methods, especially those that employ thermal sensors.

关键词： Fourier Transform Fingerprints THERMAL SENSOR Quality assessment Frequency-domain analysis ridges Parameter estimation Subbands Distress Orientation

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Sign Language Recognition using Spiking Neural Networks

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Procedia Computer Science 2024年 235卷 2674-2683页

作者： Pranav Chaudhari Alex Vicente-Sola Amlan Basu Davide L. Manna Paul Kirkland Gaetano Di Caterina Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow UK

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages.

关键词： Spiking Neural Networks American Sign Language Sign Language Recognition Deep Learning

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Perception as a dynamical sensori-motor attraction basin

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8th European Conference on Advances in Artificial Life, ECAL 2005

作者： Maillard, M. Gapenne, O. Hafemeister, L. Gaussier, P. Neuro-cybernetic Team Image and Signal Processing Lab. Cergy Pontoise University 6 av. du Ponceau 95014 Cergy France COSTECH Univ. de Techonologies de Compiègne Groupe Suppléances Perceptives

ISBN: (纸本)3540288481

In this paper, we propose a formal definition of the perception as a behavioral dynamical attraction basin. The perception is built from the integration of the sensori-motor flow. Psychological considerations and robotic experiments on an embodied "intelligent" system are provided to show how this definition can satisfy both psychologist and robotician point of view. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Sensory perception

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STATISTICAL PARAMETRIC MAPPING OF FUNCTIONAL MRI DATA USING WAVELETS ADAPTED TO THE CEREBRAL CORTEX

STATISTICAL PARAMETRIC MAPPING OF FUNCTIONAL MRI DATA USING ...

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IEEE International Symposium on Biomedical Imaging

作者： Hamid Behjat Nora Leonardi Dimitri Van De Ville Signal Processing Group Department of Electrical and Information Technology Lund University Medical Image Processing Lab (MIPLAB) Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781467364560

Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and evaluate the performance of the proposed approach both on simulated and real fMRI data. Compared to SPM and conventional WSPM, the graph-based WSPM shows improved detection of finely 3D-structured brain activity.

关键词： Statistical testing Functional MRI Spectral graph theory Graph wavelet transform Wavelet thresholding

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Non-linear least squares features transformation for improving the performance of probabilistic neural networks in classifying human brain tumors on MRI

Non-linear least squares features transformation for improvi...

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International Conference on Computational Science and Its Applications (ICCSA 2007)

作者： Georgiadis, Pantelis Cavouras, Dionisis Kalatzisz, Ioannis Daskalakis, Antonis Kagadis, George Sifaki, Koralia Malamas, Menelaos Nikiforidis, George Solmou, Ekaterini Univ Patras Sch Med Lab Med Phys Med Image Proc & Anal Grp GR-26500 Patras Greece Inst Educ Technol Dept Med Instruments Technol Med Signal and Image Processing Lab GR-12210 Athens Greece General Hellen Airforce Hosp MRI Unit GR-11525 Athens Greece Univ Patras Sch Med Dept Radiol Patras GR-26503 Greece

ISBN: (纸本)9783540744825

The aim of the present study was to design, implement, and evaluate a software system for discriminating between metastases, meningiomas, and gliomas on MRI. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a second degree least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 75 T1-weighted post-contrast MR images (24 metastases, 21 meningiomas, and 30 gliomas). Classification performance was evaluated employing the leave-one-out method and for all possible textural feature combinations. LSFT enhanced the performance of the PNN, achieving 93.33% in discriminating between the three major types of human brain tumors, against 89.33% scored by the PNN alone. Best feature combination for achieving highest discrimination power included the mean value and entropy, which reflect specific properties of texture, i.e. signal strength and inhomogeneity. LSFT improved PNN performance, increased class separability, and resulted in dimensionality reduction.

关键词： Feature extraction

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