Block matching is a well {known technique to estimate the motion eld between two consecutive fram... more Block matching is a well {known technique to estimate the motion eld between two consecutive frames of a sequence, and is eg used in the predictive coding of macroblocks in the MPEG standard 1]. In this contribution the motion eld is rst estimated with a coarser accuracy than required, using a traditional exhaustive search algorithm, and subsequently improved in a nonlinear di usion process. It is shown that the performance, ie the energy in the displaced frame di erence (DFD), can be improved while speeding up the estimation ...
International Symposium on Image and Signal Processing and Analysis, Oct 17, 2011
Abstract A novel asymmetric stereoscopic video coding method is presented in this paper. The prop... more Abstract A novel asymmetric stereoscopic video coding method is presented in this paper. The proposed coding method is based on uneven sample domain quantization for different views and is typically applied together with a reduction of spatial resolution for one of the views. Any transform-based video compression, such as the Advanced Video Coding (H. 264/AVC) standard, can be used with the proposed method. We investigate whether the binocular vision masks the coded views of different types of degradations caused by the ...
In this study, in order to find out the best ECG classification performance we realized comparati... more In this study, in order to find out the best ECG classification performance we realized comparative evaluations among the state-of-the-art classifiers such as Convolutional Neural Networks (CNNs), multi-layer perceptrons (MLPs) and Support Vector Machines (SVMs). Furthermore, we compared the performance of the learned features from the last convolutional layer of trained 1-D CNN classifier against the hand-crafted features that are extracted by Principal Component Analysis, Hermite Transform and Dyadic Wavelet Transform. Experimental results over the MIT-BIH arrhythmia benchmark database demonstrate that the single channel (raw ECG data based) shallow 1D CNN classifier over the learned features in general achieves the highest classification accuracy and computational efficiency. Finally, it is observed that the use of the learned features on either SVM or MLP classifiers does not yield any performance improvement.
1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996
This paper introduces a new structure for stack filtering, where the filter adapts to the local c... more This paper introduces a new structure for stack filtering, where the filter adapts to the local characteristics encountered in data. Both supervised and unsupervised techniques for optimal design are investigated. We split the image into small regions and select the stack filter to process each region according to the spatial domain or threshold level domain characteristics of the input signal. This method provides a significant improvement potential over the global stack filtering approach. Some local statistics are computed, to build a reduced input space which efficiently describes the most important local characteristics of data. Vector quantization is used for clustering the reduced input space into a small number of regions, and then finding a mapping between reduced input space clusters and the filter space, will result in rules for selecting the best suited stack filter for a given region. The supervised clustering procedures are shown to surpass significantly the global fil...
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first kn... more Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset...
Block matching is a well {known technique to estimate the motion eld between two consecutive fram... more Block matching is a well {known technique to estimate the motion eld between two consecutive frames of a sequence, and is eg used in the predictive coding of macroblocks in the MPEG standard 1]. In this contribution the motion eld is rst estimated with a coarser accuracy than required, using a traditional exhaustive search algorithm, and subsequently improved in a nonlinear di usion process. It is shown that the performance, ie the energy in the displaced frame di erence (DFD), can be improved while speeding up the estimation ...
International Symposium on Image and Signal Processing and Analysis, Oct 17, 2011
Abstract A novel asymmetric stereoscopic video coding method is presented in this paper. The prop... more Abstract A novel asymmetric stereoscopic video coding method is presented in this paper. The proposed coding method is based on uneven sample domain quantization for different views and is typically applied together with a reduction of spatial resolution for one of the views. Any transform-based video compression, such as the Advanced Video Coding (H. 264/AVC) standard, can be used with the proposed method. We investigate whether the binocular vision masks the coded views of different types of degradations caused by the ...
In this study, in order to find out the best ECG classification performance we realized comparati... more In this study, in order to find out the best ECG classification performance we realized comparative evaluations among the state-of-the-art classifiers such as Convolutional Neural Networks (CNNs), multi-layer perceptrons (MLPs) and Support Vector Machines (SVMs). Furthermore, we compared the performance of the learned features from the last convolutional layer of trained 1-D CNN classifier against the hand-crafted features that are extracted by Principal Component Analysis, Hermite Transform and Dyadic Wavelet Transform. Experimental results over the MIT-BIH arrhythmia benchmark database demonstrate that the single channel (raw ECG data based) shallow 1D CNN classifier over the learned features in general achieves the highest classification accuracy and computational efficiency. Finally, it is observed that the use of the learned features on either SVM or MLP classifiers does not yield any performance improvement.
1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996
This paper introduces a new structure for stack filtering, where the filter adapts to the local c... more This paper introduces a new structure for stack filtering, where the filter adapts to the local characteristics encountered in data. Both supervised and unsupervised techniques for optimal design are investigated. We split the image into small regions and select the stack filter to process each region according to the spatial domain or threshold level domain characteristics of the input signal. This method provides a significant improvement potential over the global stack filtering approach. Some local statistics are computed, to build a reduced input space which efficiently describes the most important local characteristics of data. Vector quantization is used for clustering the reduced input space into a small number of regions, and then finding a mapping between reduced input space clusters and the filter space, will result in rules for selecting the best suited stack filter for a given region. The supervised clustering procedures are shown to surpass significantly the global fil...
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first kn... more Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset...
In this paper we propose an image matching approach that selects the method of matching for each ... more In this paper we propose an image matching approach that selects the method of matching for each region in the image based on the region properties. This method can be used to find images similar to a query image from a database, which is useful for automatic image and video annotation. In this approach, each image is first divided into large homogeneous areas, identified as " texture areas " , and non-texture areas. Local descriptors are then used to match the keypoints in the non-texture areas, while texture regions are matched based on low level visual features. Experimental results prove that while exclusion of texture areas from local descriptor matching increases the efficiency of the whole process, utilization of appropriate measures for different regions can also increase the overall performance.
Local image features around interest-points have been widely used in order to exploit the similar... more Local image features around interest-points have been widely used in order to exploit the similarities between different views of an object in different images. While there are numerous algorithms on detecting the interest-points and defining the local features, few have focused on the importance of the matching process. In this paper, we presented a method that matches interest-points detected via any algorithm. The method is motivated from human perceptual rules, particularly the Gestalt Psychology, and realizes the fact that " The whole is different from the sum of its parts ". The efficacy of the algorithm is not only the ability to decrease the number of false positive matches but also to increase the number of true positives, yielding rock-steady results for any algorithm based on matching local features.
Uploads