Rank Order Filters Research Articles

A Comparative Study on X-Ray image Enhancement Based on Neutrosophic Set

Medical image noise, ambiguity, and fuzziness pose challenges to the medical image analysis process. To lessen these problems, fuzzy sets are utilized; however, they frequently ignore the pixel's spatial context. The neutrosophic set (NS) is employed in picture denoising to get over these restrictions. Neutronosophic theory, in particular the NS Bilateral filter, NS Wiener filter, NS Median filter, NS gaussian filter, and NS rank-ordered filter, is covered in this paper. The Lung-cancer dataset of X-Ray images is used to assess the performance of different denoising techniques. The effectiveness of NS-based denoising techniques over conventional techniques is demonstrated through comparisons with other approaches, demonstrating the NS's function in X-ray image denoising.

In-ADC, Rank-Order Filter for Digital Pixel Sensors

This paper presents a new implementation of the rank-order filter, which is established on a parallel-operated array of single-slope (SS) analog-to-digital converters (ADCs). The SS ADCs use an “on-the-ramp processing” technique, i.e., filtration is performed along with analog-to-digital conversion, so the final states of the converters represent a filtered image. A proof-of-concept 64 × 64 array of SS ADCs, integrated with MOS photogates, was fabricated using a standard 180 nm CMOS process. The measurement results demonstrate the full functionality of the novel filter concept, with image acquisition in both single-sampling and correlated-double-sampling (CDS) modes (CDS is digitally performed using ADCs). The experimental, massively parallel rank-order filter can process 650 frames per second with a power consumption of 4.81 mW.

An Efficient Low Complexity Region-of-Interest Detection for Video Coding in Wireless Visual Surveillance

Following its use in several applications, including video coding in wireless surveillance, moving object detection (MOD) has become a popular video analysis topic. Despite the considerable progress in the accuracy of MOD for video coding, its implementation in constrained sensors is a real challenge owing to their high complexity and energy consumption. Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems. In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed. The algorithm ensures a trade-off between detection accuracy and computational complexity. To this end, we propose constructing an activity map by measuring each block activity between successive frames. The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-the-art techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.

Enhancing the Performance of One Dimensional Median Filter

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

Modular Discrete and CMOS Integrated Implementations of High-Speed Analog Rank-Order Filters

Modular discrete and integrated implementations of a continuous-time analog rank-order N input filter are presented that uses one operational transconductance amplifier per input. Rank is programmable for all values from $$k=1$$ (min case) to $$k=N$$ (max case). The implementation is based on transconductance cells with high transconductance gain and symmetrical output current saturating characteristics. Experimental results for both discrete and CMOS integrated implementations are presented that verify functionality and the high-frequency characteristics of the proposed implementations.

Noise removal using statistical operators for efficient leaf identification

Plant identification based on leaf shape is becoming a popular trend, since each leaf carries substantial information that can be used to identify plant. This is difficult because the features of a leaf shape can be influenced by other leaves that have similar features but different categories. This paper presents the most popular statistical operators: mean filtering technique (MFT), median filtering technique (MDFT), Wiener filtering technique (WFT), rank order filtering technique (ROFT) and adaptive two-pass rank order filtering technique (ATRFT) for enhancing preprocessing stage. The performance of these techniques was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). Ten features were extracted from the pre-processed leaf images and identification performance was also evaluated using precision and recall. It is found that WFT is the best filtering technique and gives the best identification accuracy of 95.1%.

Parallel rank-order filtering based on impulse K-WINNERS-TAKE-ALL neural network.

Представлено нейронну мережу (НМ) неперервного часу типу “K-winners-take-all” (KWTA), яка ідентифікує К найбільші з-поміж N входів, де керуючий сигнал 1 £ K < N . Мережа описується рівнянням стану з розривною правою частиною і вихідним рівнянням. Рівняння стану містить шлейф імпульсів, які описуються сумою дельта- функцій Дірака. Головною перевагою мережі порівняно з іншими близькими аналогами є відсутність обмежень на швидкість збіжності. Описано застосування мережі для паралельного фільтрування рангу. Отримані теоретичні результати проілюстровано прикладом комп’ютерного моделювання, який демонструє ефективність мережі.

CMOS Analog Rank Order Filters using positive feedback comparators

This work presents the design and experimental results of two Analog CMOS Rank Order Filters (ROF's), both of them based on the same operation principle and working with a specific analog CMOS comparator. The design of the two ROF's were performed with ON Semi 0.5µm CMOS technology through MOSIS. Both comparators present a high gain due to the use of positive feedback. An Slew Rate (SR) of 2.6V/μs@30pF, including the parasitic capacitances of the experimental measurements setup, is shown.

Design of univariate alarm systems via rank order filters

Filtering is one of the techniques used in alarm system design to improve the performance of an alarm system. Due to the fact that the filtered data is no longer independent, computation of the performance indexes (false alarm rate (FAR), missed alarm rate (MAR) and expected detection delay (EDD)) is hard for filters. In this paper, rank order filters are applied in alarm system design. The output of rank order filters is restricted to one of the input samples, thus the probability density function (PDF) of the filtered data can be computed directly from the PDF of the raw data. This feature makes it possible to compute FAR and MAR for rank order filters directly. Further, a method to compute the expected detection delay is proposed for rank order filters despite the dependence of the filtered data. Simulation results shows that the order of rank order filters provide another degree-of-freedom in alarm system design besides the window size, which can be used to improve the alarm performance.

Consensus-based trajectory estimation for ball detection in calibrated cameras systems

This paper considers the detection of the ball in team sport scenes observed with still or motion-compensated calibrated cameras. Foreground masks do provide primary cues to identify circular moving objects in the scene, but are shown to be too noisy to achieve reliable detections of weakly contrasted balls, especially when a single viewpoint is available, as often desired for reduced deployment cost. In those cases, trajectory analysis has been shown to provide valuable complementary information to differentiate true and false positives among the candidates detected by the foreground mask(s). In this paper, we focus on the detection of ball trajectory segments, exclusively from visual cues, without considering semantic reasoning about team play to connect those segments into long trajectories. We revisit several recent works, and introduce a publicly available dataset to compare them. We conclude that randomized consensus-based methods are competitive compared to the alternative deterministic graph-based solutions, while offering the additional advantage to naturally extend to the cost-effective single-view scenario. As an original contribution, we also introduce a procedure to efficiently clean up the foreground mask in correlation-based methods and a nonlinear rank-order filter to merge the foreground cues from multiple viewpoints. We also derive recommendations regarding the camera positioning and the buffering needs of a real-time acquisition system.

Quantile Filtering of Colour Images via Symmetric Matrices

Abstract Quantile filters, or rank-order filters, are local image filters which assign quantiles of intensities of the input image within neighbourhoods as output image values. Combining a multivariate quantile definition developed in matrix-valued morphology with a recently introduced mapping between the RGB colour space and the space of symmetric 2 × 2 matrices, we state a class of colour image quantile filters, along with a class of morphological gradient filters derived from these.We consider variants of these filters based on three matrix norms – the nuclear, Frobenius, and spectral norm – and study their differences. We investigate the properties of the quantile and gradient filters and their links to dilation and erosion operators. Using amoeba structuring elements,we devise image-adaptive versions of our quantile and gradient filters. Experiments are presented to demonstrate the favourable properties of the filters, and compare them to existing approaches in colour morphology.

Rank-ordered filter for edge enhancement of cellular images using interval type II fuzzy set.

An edge-enhancement technique using an interval type II fuzzy set that uses rank-ordered filter to enhance the edges of cellular images is proposed. When cellular images from any laboratory are digitized, scanned, and stored, some kind of degradation occurs, and directly using a rank-ordered filter may not produce clear edges. These images contain uncertainties, present in edges or boundaries of the image. Fuzzy sets that take into account these uncertainties may be a good tool to process these images. However, a fuzzy set sometimes does not produce better results. We used an interval type II fuzzy set, which considers the uncertainty in a different way. It considers the membership function in the fuzzy set as "fuzzy," so the membership values lie within an interval range. A type II fuzzy set has upper and lower membership levels, and with the two levels, a new membership function is computed using Hamacher t-conorm. A new fuzzy image is formed. A rank-ordered filter is applied to the image to obtain an edge-enhanced image. The proposed method is compared with the existing methods visually and quantitatively using entropic method. Entropy of the proposed method is higher (0.4418) than the morphology method (0.2275), crisp method (0.3599), and Sobel method (0.2669), implying that the proposed method is better.

Hybrid Projection 함수와 Rank Order 필터를 이용한 눈동자 검출

이 논문에서는 hybrid projection 함수와 rank order 필터를 이용하여 눈동자를 검출하는 방법을 제안한다. 눈썹을 눈동자로 검출하는 오류를 줄이기 위하여, hybrid projection 함수를 이용하여 얼굴 영역에서 눈썹을 검출하고 눈썹이 포함되지 않도록 눈 영역을 설정한다. 눈 영역에서 rank order 필터를 사용하여 눈동자 후보점을 찾고 위치를 보정한다. 두 눈동자 후보점을 기하학적인 제약조건을 기반으로 쌍으로 묶고 각 쌍의 두 눈에 대한 유사도를 정합을 이용하여 측정하여가장작은값을 갖는 쌍을 최종눈동자로 결정한다. BioID 얼굴데이터베이스의 얼굴 영상 700개에 대한 실험 결과 92.4%의 검출율을 얻었으며 기존 방법보다 약 21.5% 개선된 결과를 얻었다. In this paper, we propose a pupil detection method using hybrid projection function and rank order filter. To reduce error to detect eyebrows as pupil, eyebrows are detected using hybrid projection function in face region and eye region is set to not include the eyebrows. In the eye region, potential pupil candidates are detected using rank order filter and then the positions of pupil candidates are corrected. The pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using template matching, we select a pair with the smallest similarity measure as final two pupils. The experiments have been performed for 700 images of the BioID face database. The pupil detection rate is 92.4% and the proposed method improves about 21.5% over the existing method..

Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

Rank Order Filter와 상호상관을 이용한 강인한 눈동자 검출

In this paper, we propose a robust pupil detection method using rank order filter and cross-correlation. Potential pupil candidates are detected using rank order filter. Eye region is binarized using variable threshold to find eyebrow, and pupil candidates at the eyebrow are removed. The positions of pupil candidates are corrected, the pupil candidates are grouped into pairs based on geometric constraints. A similarity measure is obtained for two eye of each pair using cross-correlation, we select a pair with the largest similarity measure as a final pupil. The experiments have been performed for 500 images of the BioID face database. The results show that it achieves the high detection rate of 96.8% and improves about 11.6% than existing method.

A New Intelligent Fault Recognition Method for Rotating Machinery

In this paper, a novel method to recognize rotor fault pattern was proposed based on rank-order morphological filter, harmonic window decomposition, sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Then, the six feature frequency bands which contain the typical fault information were extracted by harmonic window decomposition that needs not decomposition; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different rotor vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in fault diagnosis of rotating machinery effectively.

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

In this paper, a novel method to recognize gear fault pattern was approached based on harmonic wavelet package (HWP), sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

Interest point detection using rank order LoG filter

This paper proposes a novel nonlinear filter, named rank order Laplacian of Gaussian (ROLG) filter, based on which a new interest point detector is developed. The ROLG filter is a weighted rank order filter. It is used to detect the image local structures where a significant majority of pixels are brighter or darker than a significant majority of pixels in their corresponding surroundings. Compared to linear filter based detectors, e.g. SIFT detector, the proposed rank order filter based detector is more robust to abrupt variations of images caused by illumination and geometric changes. Experiments on the benchmark databases demonstrate that the proposed ROLG detector achieves superior performance comparing to four state-of-the-art detectors. Evaluation experiments are also conducted on face recognition problems. The results on five face databases further demonstrate that the ROLG detector significantly outperforms the other detectors.

A VLSI Implementation of Rank-Order Searching Circuit Employing a Time-Domain Technique

We present a compact and low-power rank-order searching (ROS) circuit that can be used for building associative memories and rank-order filters (ROFs) by employing time-domain computation and floating-gate MOS techniques. The architecture inherits the accuracy and programmability of digital implementations as well as the compactness and low-power consumption of analog ones. We aim to implement identification function as the first priority objective. Filtering function would be implemented once the location identification function has been carried out. The prototype circuit was designed and fabricated in a 0.18 μm CMOS technology. It consumes only 132.3 μW for an eight-input demonstration case.

Rank Order Filter와 화소값 차이를 이용한 강인한 눈동자 검출

In this paper, we propose a robust pupil detection method using rank order filter and pixel value difference in facial image. We have detected the potential pupil candidates using rank order filter. Many false pupil candidates found at eyebrow are removed using the fact that the pixel difference is much at the boundary between pupil and sclera. The rest pupil candidates are grouped into pairs. Each pair is verified according to geometric constraints such as the angle and the distance between two candidates. A fitness function is obtained for each pair using the pixel values of two pupil regions, we select a pair with the smallest fitness value as a final pupil. The experiments have been performed for 400 images of the BioID face database. The results show that it achieves more than 90% accuracy, and especially the proposed method improves the detection rate and high accuracy for face with spectacle.