Fast morphological attribute operations using Tarjan's union-find algorithm

Morphological attribute openings and closings and related operators are generalizations of the area opening and closing, and allow filtering of images based on a wide variety of shape or size based criteria. A fast union-find algorithm for the computation of these operators is presented in this paper. The new algorithm has a worst case time complexity of O(N logN) where N is the image size, as opposed to O(N^2 logN) for the existing algorithm. Memory requirements are O(N) for both algorithms

Fast morphological attribute operations using Tarjan's union-find algorithm

Morphological attribute openings and closings and related operators are generalizations of the area opening and closing, and allow filtering of images based on a wide variety of shape or size based criteria. A fast union-find algorithm for the computation of these operators is presented in this paper. The new algorithm has a worst case time complexity of O(N log N) where N is the image size, as opposed to O (N^2 log N) for the existing algorithm. Memory requirements are O(N) for both algorithms.

Fetal ECG Extraction from Multichannel Abdominal ECG Recordings for Health Monitoring During Labor

AbstractExtracting clean fetal electrocardiogram (fECG) signals from non-invasive abdominal ECG recordings for monitoring the health of the fetus during pregnancy and labor remains a big challenge. The proposed system for facing extraction, processing and morphological feature estimation was implemented in LabVIEW 2013 with preinstalled Biosignal Filtering, Advanced Signal Processing and Digital Filter Design Toolboxes. The present approach is based on the using of FastICA algorithm for fECG extraction. In order to improve fECG extraction performance, it was applied here a combination of Undecimated Wavelet Transform (UWT) and Fast Fourier Transform (FFT) – Inverse Fast Fourier Transform (IFFT) algorithm as post-processing tool. Fetal ECG morphological indicators like heart rate, T/QRS ratio and QT interval could be estimated from fECG post-processed signals of two patients and some considerations regarding to the fetal stress during labor could be made in these two cases

Impulsive noise removal from color images with morphological filtering

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.Comment: The 6th international conference on analysis of images, social networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi

Component-Tree Simplification through Fast Alpha Cuts

Tree-based hierarchical image representations are commonly used in connected morphological image filtering, segmentation and multi-scale analysis. In the case of component trees, filtering is generally based on thresholding single attributes computed for all the nodes in the tree. Alternatively, so-called shapings are used, which rely on building a component tree of a component tree to filter the image. Neither method is practical when using vector attributes. In this case, more complicated machine learning methods are required, including clustering methods. In this paper I present a simple, fast hierarchical clustering algorithm based on cuts of α-trees to simplify and filter component trees

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Beyond Stemming and Lemmatization: Ultra-stemming to Improve Automatic Text Summarization

In Automatic Text Summarization, preprocessing is an important phase to reduce the space of textual representation. Classically, stemming and lemmatization have been widely used for normalizing words. However, even using normalization on large texts, the curse of dimensionality can disturb the performance of summarizers. This paper describes a new method for normalization of words to further reduce the space of representation. We propose to reduce each word to its initial letters, as a form of Ultra-stemming. The results show that Ultra-stemming not only preserve the content of summaries produced by this representation, but often the performances of the systems can be dramatically improved. Summaries on trilingual corpora were evaluated automatically with Fresa. Results confirm an increase in the performance, regardless of summarizer system used.Comment: 22 pages, 12 figures, 9 table