Jan Vlach - Academia.edu (original) (raw)
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Papers by Jan Vlach
In audio applications it is often necessary to process the signal in "real time". The m... more In audio applications it is often necessary to process the signal in "real time". The method of segmented wavelet transform (SegWT) makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, not using the classical "window-ing". This means that the method could be utilized for wavelet-type processing of an audio signal in real time, or alternatively in case we just need to process a long signal, but there is insufficient computational memory capacity for it (e.g. in the DSPs). In the paper, the principle of the segmented forward wavelet transform is explained and the algorithm is described in detail.
In this paper, we propose optimized method of discrete wavelet transform. There is many use of wa... more In this paper, we propose optimized method of discrete wavelet transform. There is many use of wavelet transform in digital signal processing (compression, wireless sensor networks, etc.). In those fields, it is necessary to have digital signal processing as fast as it possible. The new segmented discrete wavelet transform (SegWT) has been developed to process in real-time. It is possible to process the signal part-by-part with low memory costs by the new method. In the paper, the principle and benefits if the segmented wavelet transform is explained. Full Text at Springer, may require registration or fee
Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006, 2006
ABSTRACT The new method of segmented wavelet transform (SegWT) makes it possible to exactly compu... more ABSTRACT The new method of segmented wavelet transform (SegWT) makes it possible to exactly compute the discrete-time wavelet transform of a signal segment-by-segment. This means that the method could be utilized for wavelet-type processing of a signal in "real time", or in case we need to process a long signal (not necessarily in real time), but there is insufficient memory capacity for it (for example in the signal processors). Then it is possible to process the signal part-by-part with low memory costs by the new method. The method is suitable for universal utilization in any place where the signal has to be processed via modification of its wavelet coefficients (e.g. signal denoising, compression, music or speech processing, alternative modulation techniques for xDSL systems, image processing and compression). In the paper, the principle of the forward segmented wavelet transform is described
This paper deals with an approach to detect face from image with complex background. That can be ... more This paper deals with an approach to detect face from image with complex background. That can be use in many areas (eg. security systems, biometrics, telecommunications, etc.).In the first part there is presented brief overview of common used method for face localization in image. On the basis of comparison of its properties there was developed appropriate combination of several methods with particular improvements. The goal was to find suitable accuracy to speed rate. Methods were implemented in MATLAB and accuracy rate was tested on Georgia Tech face database.
In audio applications it is often necessary to process the signal in "real time". The m... more In audio applications it is often necessary to process the signal in "real time". The method of segmented wavelet transform (SegWT) makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, not using the classical "window-ing". This means that the method could be utilized for wavelet-type processing of an audio signal in real time, or alternatively in case we just need to process a long signal, but there is insufficient computational memory capacity for it (e.g. in the DSPs). In the paper, the principle of the segmented forward wavelet transform is explained and the algorithm is described in detail.
In this paper, we propose optimized method of discrete wavelet transform. There is many use of wa... more In this paper, we propose optimized method of discrete wavelet transform. There is many use of wavelet transform in digital signal processing (compression, wireless sensor networks, etc.). In those fields, it is necessary to have digital signal processing as fast as it possible. The new segmented discrete wavelet transform (SegWT) has been developed to process in real-time. It is possible to process the signal part-by-part with low memory costs by the new method. In the paper, the principle and benefits if the segmented wavelet transform is explained. Full Text at Springer, may require registration or fee
Proceedings of the 7th Nordic Signal Processing Symposium - NORSIG 2006, 2006
ABSTRACT The new method of segmented wavelet transform (SegWT) makes it possible to exactly compu... more ABSTRACT The new method of segmented wavelet transform (SegWT) makes it possible to exactly compute the discrete-time wavelet transform of a signal segment-by-segment. This means that the method could be utilized for wavelet-type processing of a signal in "real time", or in case we need to process a long signal (not necessarily in real time), but there is insufficient memory capacity for it (for example in the signal processors). Then it is possible to process the signal part-by-part with low memory costs by the new method. The method is suitable for universal utilization in any place where the signal has to be processed via modification of its wavelet coefficients (e.g. signal denoising, compression, music or speech processing, alternative modulation techniques for xDSL systems, image processing and compression). In the paper, the principle of the forward segmented wavelet transform is described
This paper deals with an approach to detect face from image with complex background. That can be ... more This paper deals with an approach to detect face from image with complex background. That can be use in many areas (eg. security systems, biometrics, telecommunications, etc.).In the first part there is presented brief overview of common used method for face localization in image. On the basis of comparison of its properties there was developed appropriate combination of several methods with particular improvements. The goal was to find suitable accuracy to speed rate. Methods were implemented in MATLAB and accuracy rate was tested on Georgia Tech face database.