@conference {12438,
	title = {The evolution of stochastic grammars for representation and recognition of activities in videos},
	booktitle = {2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {688 - 688},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The speaker is one of the privileged many to have been taught syntactic pattern recognition methods by the Late Prof. K.S. Fu. In this talk, I will discuss the evolution of stochastic image grammars from the early seventies to now with a focus on image and video understanding applications.},
	keywords = {activity recognition, activity representation, Conferences, Educational institutions, Grammar, Image analysis, image recognition, image representation, image understanding, pattern recognition, stochastic image grammars, syntactic pattern recognition methods, video signal processing, video understanding, Videos},
	isbn = {978-1-4673-0062-9},
	doi = {10.1109/ICCVW.2011.6130309},
	author = {Chellapa, Rama}
}
@conference {19295,
	title = {Buffer management for multi-application image processing on multi-core platforms: Analysis and case study},
	booktitle = {2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)},
	year = {2010},
	month = {2010},
	pages = {1662 - 1665},
	abstract = {Due to the limited amounts of on-chip memory, large volumes of data, and performance and power consumption overhead associated with interprocessor communication, efficient management of buffer memory is critical to multi-core image processing. To address this problem, this paper develops new modeling and analysis techniques based on dataflow representations, and demonstrates these techniques on a multi-core implementation case study involving multiple, concurrently-executing image processing applications. Our techniques are based on careful representation and exploitation of frame- or block-based operations, which involve repeated invocations of the same computations across regularly- arranged subsets of data. Using these new approaches to manage block-based image data, this paper demonstrates methods to analyze synchronization overhead and FIFO buffer sizes when mapping image processing applications onto heterogeneous, multi core architectures.},
	keywords = {block-based image data, buffer memory management, buffer storage, concurrently-executing image processing application, data flow computing, data flow representation, data subset, Dataflow, Digital signal processing, Energy consumption, Energy management, Engineering management, FIFO buffer sizes, Hardware, Image analysis, IMAGE PROCESSING, image processing application, image representation, interprocessor communication, memory architecture, Memory management, multiapplication image processing, multicore image processing, multiprocessing, on-chip memory, power consumption, power consumption overhead, Runtime, scheduling, set theory, shared memory, storage management chips, synchronization overhead},
	author = {Ko,Dong-Ik and Won, N. and Bhattacharyya, Shuvra S.}
}
@conference {14205,
	title = {Bilateral symmetry of object silhouettes under perspective projection},
	booktitle = {19th International Conference on Pattern Recognition, 2008. ICPR 2008},
	year = {2008},
	month = {2008/12/08/11},
	pages = {1 - 4},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Symmetry is an important property of objects and is exhibited in different forms e.g., bilateral, rotational, etc. This paper presents an algorithm for computing the bilateral symmetry of silhouettes of shallow objects under perspective distortion, exploiting the invariance of the cross ratio to projective transformations. The basic idea is to use the cross ratio to compute a number of midpoints of cross sections and then fit a straight line through them. The goodness-of-fit determines the likelihood of the line to be the axis of symmetry. We analytically estimate the midpointpsilas location as a function of the vanishing point for a given object silhouette. Hence finding the symmetry axis amounts to a 2D search in the space of vanishing points. We present experiments on two datasets as well as Internet images of symmetric objects that validate our approach.},
	keywords = {Automation, bilateral symmetry, Computer vision, Frequency, Image analysis, Image coding, Image reconstruction, Internet, Internet images, Object detection, object silhouettes, perspective distortion, perspective projection, SHAPE, symmetric objects},
	isbn = {978-1-4244-2174-9},
	doi = {10.1109/ICPR.2008.4761501},
	author = {Bitsakos,K. and Yi,H. and Yi,L. and Ferm{\"u}ller, Cornelia}
}
@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.}
}
@book {13327,
	title = {Shape Analysis and Structuring},
	year = {2008},
	month = {2008///},
	publisher = {Springer},
	organization = {Springer},
	abstract = {With a lot of recent developments in the field, this much-needed book has come at just the right time. It covers a variety of topics related to preserving and enhancing shape information at a geometric level. The contributors also cover subjects that are relevant to effectively capturing the structure of a shape by identifying relevant shape components and their mutual relationships.},
	keywords = {Computer Graphics, Computer vision, Computers / Computer Graphics, Computers / Image Processing, Geometrical models, Geometry, Geometry, Analytic, Image analysis, IMAGE PROCESSING, Mathematics / Functional Analysis, Mathematics / Geometry / General, Mathematics / Graphic Methods, Mathematics / Mathematical Analysis, shapes, Technology \& Engineering / Engineering (General), Visualization},
	isbn = {9783540332640},
	author = {De Floriani, Leila and Spagnuolo,Michela}
}
@article {12740,
	title = {Fast two-frame multiscale dense optical flow estimation using discrete wavelet filters},
	journal = {Journal of the Optical Society of America AJ. Opt. Soc. Am. A},
	volume = {20},
	year = {2003},
	month = {2003///},
	pages = {1505 - 1515},
	abstract = {A multiscale algorithm with complexity O(N) (where N is the number of pixels in one image) using wavelet filters is proposed to estimate dense optical flow from two frames. Hierarchical image representation by wavelet decomposition is integrated with differential techniques in a new multiscale framework. It is shown that if a compactly supported wavelet basis with one vanishing moment is carefully selected, hierarchical image, first-order derivative, and corner representations can be obtained from the wavelet decomposition. On the basis of this result, three of the four components of the wavelet decomposition are employed to estimate dense optical flow with use of only two frames. This overcomes the {\textquotedblleft}flattening-out{\textquotedblright} problem in traditional pyramid methods, which produce large errors when low-texture regions become flat at coarse levels as a result of blurring. A two-dimensional affine motion model is used to formulate the optical flow problem as a linear system, with all resolutions simultaneously (i.e., coarse-and-fine) rather than the traditional coarse-to-fine approach, which unavoidably propagates errors from the coarse level. This not only helps to improve the accuracy but also makes the hardware implementation of our algorithm simple. Experiments on different types of image sequences, together with quantitative and qualitative comparisons with several other optical flow methods, are given to demonstrate the effectiveness and the robustness of our algorithm.},
	keywords = {digital image processing, Image analysis, optical flow},
	doi = {10.1364/JOSAA.20.001505},
	url = {http://josaa.osa.org/abstract.cfm?URI=josaa-20-8-1505},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld,Azriel}
}
@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.}
}
