@conference {14229, title = {Measuring 1st order stretchwith a single filter}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008}, year = {2008}, month = {2008/04/31/March}, pages = {909 - 912}, publisher = {IEEE}, organization = {IEEE}, abstract = {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.}, keywords = {Cepstral analysis, Educational institutions, filter, filtering theory, Fourier transforms, Frequency domain analysis, Frequency estimation, Gabor filters, Image analysis, IMAGE PROCESSING, linear stretch measurement, local signal transformation measurement, Nonlinear filters, Phase estimation, Signal analysis, Speech processing}, isbn = {978-1-4244-1483-3}, doi = {10.1109/ICASSP.2008.4517758}, author = {Bitsakos,K. and Domke, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.} } @conference {11897, title = {Identifying and segmenting human-motion for mobile robot navigation using alignment errors}, booktitle = {12th International Conference on Advanced Robotics, 2005. ICAR {\textquoteright}05. Proceedings}, year = {2005}, month = {2005/07//}, pages = {398 - 403}, publisher = {IEEE}, organization = {IEEE}, abstract = {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{\textquoteright}s motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object{\textquoteright}s motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects}, keywords = {Computer errors, Educational institutions, Frequency estimation, human-motion identification, human-motion segmentation, HUMANS, Image motion analysis, Image segmentation, mobile robot navigation, Mobile robots, Motion estimation, Navigation, Object detection, robot vision, SHAPE}, isbn = {0-7803-9178-0}, doi = {10.1109/ICAR.2005.1507441}, author = {Abd-Almageed, Wael and Burns,B. J and Davis, Larry S.} } @conference {14267, title = {The statistics of optical flow: implications for the process of correspondence in vision}, booktitle = {15th International Conference on Pattern Recognition, 2000. Proceedings}, volume = {1}, year = {2000}, month = {2000///}, pages = {119-126 vol.1 - 119-126 vol.1}, publisher = {IEEE}, organization = {IEEE}, abstract = {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}, keywords = {Bias, Computer vision, correlation, correlation methods, energy-based method, flow estimation, Frequency estimation, gradient method, gradient methods, Image analysis, Image motion analysis, Image sequences, least squares, least squares approximations, Motion estimation, Nonlinear optics, Optical feedback, optical flow, Optical harmonic generation, Optical noise, Statistics, Visual perception}, isbn = {0-7695-0750-6}, doi = {10.1109/ICPR.2000.905288}, author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.} }