TY - CONF T1 - Measuring 1st order stretchwith a single filter T2 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008 Y1 - 2008 A1 - Bitsakos,K. A1 - Domke, J. A1 - Fermüller, Cornelia A1 - Aloimonos, J. KW - Cepstral analysis KW - Educational institutions KW - filter KW - filtering theory KW - Fourier transforms KW - Frequency domain analysis KW - Frequency estimation KW - Gabor filters KW - Image analysis KW - IMAGE PROCESSING KW - linear stretch measurement KW - local signal transformation measurement KW - Nonlinear filters KW - Phase estimation KW - Signal analysis KW - Speech processing AB - We analytically develop a filter that is able to measure the linear stretch of the transformation around a point, and present results of applying it to real signals. We show that this method is a real-time alternative solution for measuring local signal transformations. Experimentally, this method can accurately measure stretch, however, it is sensitive to shift. JA - IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008 PB - IEEE SN - 978-1-4244-1483-3 M3 - 10.1109/ICASSP.2008.4517758 ER - TY - CONF T1 - Identifying and segmenting human-motion for mobile robot navigation using alignment errors T2 - 12th International Conference on Advanced Robotics, 2005. ICAR '05. Proceedings Y1 - 2005 A1 - Abd-Almageed, Wael A1 - Burns,B. J A1 - Davis, Larry S. KW - Computer errors KW - Educational institutions KW - Frequency estimation KW - human-motion identification KW - human-motion segmentation KW - HUMANS KW - Image motion analysis KW - Image segmentation KW - mobile robot navigation KW - Mobile robots KW - Motion estimation KW - Navigation KW - Object detection KW - robot vision KW - SHAPE AB - This paper presents a new human-motion identification and segmentation algorithm, for mobile robot platforms. The algorithm is based on computing the alignment error between pairs of object images acquired from a moving platform. Pairs of images generating relatively small alignment errors are used to estimate the fundamental frequency of the object's motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object's motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects JA - 12th International Conference on Advanced Robotics, 2005. ICAR '05. Proceedings PB - IEEE SN - 0-7803-9178-0 M3 - 10.1109/ICAR.2005.1507441 ER - TY - CONF T1 - The statistics of optical flow: implications for the process of correspondence in vision T2 - 15th International Conference on Pattern Recognition, 2000. Proceedings Y1 - 2000 A1 - Fermüller, Cornelia A1 - Aloimonos, J. KW - Bias KW - Computer vision KW - correlation KW - correlation methods KW - energy-based method KW - flow estimation KW - Frequency estimation KW - gradient method KW - gradient methods KW - Image analysis KW - Image motion analysis KW - Image sequences KW - least squares KW - least squares approximations KW - Motion estimation KW - Nonlinear optics KW - Optical feedback KW - optical flow KW - Optical harmonic generation KW - Optical noise KW - Statistics KW - Visual perception AB - This paper studies the three major categories of flow estimation methods: gradient-based, energy-based, and correlation methods; it analyzes different ways of compounding 1D motion estimates (image gradients, spatio-temporal frequency triplets, local correlation estimates) into 2D velocity estimates, including linear and nonlinear methods. Correcting for the bias would require knowledge of the noise parameters. In many situations, however, these are difficult to estimate accurately, as they change with the dynamic imagery in unpredictable and complex ways. Thus, the bias really is a problem inherent to optical flow estimation. We argue that the bias is also integral to the human visual system. It is the cause of the illusory perception of motion in the Ouchi pattern and also explains various psychophysical studies of the perception of moving plaids. Finally, the implication of the analysis is that flow or correspondence can be estimated very accurately only when feedback is utilized JA - 15th International Conference on Pattern Recognition, 2000. Proceedings PB - IEEE VL - 1 SN - 0-7695-0750-6 M3 - 10.1109/ICPR.2000.905288 ER -