We propose a new method for tracking rigid objects in image sequences using template matching. A Kalman filter is used to make the template adapt to changes in object orientation or illumination. This approach is novel since the Kalman filter has been used in tracking mainly for smoothing the object trajectory. The performance of the Kalman filter is further improved by employing a robust and a...

-This paperdescribes the various image filtering algorithms and techniques used for image filtering/smoothing. Image smoothing is one of the most important and widely used operation in image processing .We have explained various algorithms and techniques for filter the images and which algorithm is the be the best for smoothing and filtering the images, especially we have mainly concentrate on ...

We describe a novel deterministic approximate inference technique for conditionally Gaussian state space models, i.e. state space models where the latent state consists of both multinomial and Gaussian distributed variables. The method can be interpreted as a smoothing pass and iteration scheme symmetric to an assumed density filter. It improves upon previously proposed smoothing passes by not ...

We depict the interpolation strategies for the concatenation of inventory demisyllables in our hybrid concatenative/LPC speech synthesizer. Inventory elements for vowels and nasals are cut in the steady state of the phoneme. Concatenating elements in the synthesis stage requires smoothing of spectral content and energy to avoid annoying discontinuities in these parameters, which is of vital imp...

Introduction. When dealing with time series, the application of a smoothing filter (to get rid of random fluctuations and better recognise the relevant structure) is usually one of the first steps. In the literature on time series similarity measures, however, the impact of smoothing is not explicitly or systematically considered – despite extensive experiments in, e.g., [2]. Instead, complex s...

We adapt smoothing methods to histogram-valued time series (HTS) by introducing a barycentric histogram that emulates the "average" operation, which is the key to any smoothing filter. We show that, due to its linear properties, only the Mallows-barycenter is acceptable if we wish to preserve the essence of any smoothing mechanism. We implement a barycentric exponential smoothing to forecast th...

Introduction: Filtering can greatly affect the quality of clinical images. Determining the best filter and the proper degree of smoothing can help to ensure the most accurate diagnosis. Methods: Forty five patient’s data aquired during brain phantom SPECT studies were reconstructed using filtered back-projection technique. The ramp, Shepp-Logan, Cosine, Hamming, Hanning, Butterworth, Metz...

Support in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in R to help th...

Surface models derived from medical image data often exhibit artifacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strong...