Speed Up Temporal Median Filter for Background Subtraction

2010 ◽  
Author(s):  
Mao-Hsiung Hung ◽  
Jeng-Shyang Pan ◽  
Chaur-Heh Hsieh
Keyword(s):  
Background Subtraction ◽  
Median Filter ◽  
Speed Up

Universal Liver Extraction Algorithm: An Improved Chan–Vese Model

2018 ◽  
Vol 29 (1) ◽  
pp. 237-250 ◽  
Author(s):  
Sangeeta K. Siri ◽  
Mrityunjaya V. Latte
Keyword(s):  
Ct Scan ◽  
Median Filter ◽  
Disease Diagnosis ◽  
Initial Contour ◽  
Mri Scan ◽  
Pixel Intensity ◽  
Abdominal Mri ◽  
Speed Up ◽  
Magnetic Resonance Imaging Mri ◽  
Liver Image

Abstract Liver segmentation is important to speed up liver disease diagnosis. It is also useful for detection, recognition, and measurement of objects in liver images. Sufficient work has been carried out until now, but common methodology for segmenting liver image from CT scan, MRI scan, PET scan, etc., is not available. The proposed methodology is an effort toward developing a general algorithm to segment liver image from abdominal computerized tomography (CT) scan and magnetic resonance imaging (MRI) scan images. In the proposed algorithm, pixel intensity range of the liver portion is obtained by cropping a random section of the liver. Using its histogram, threshold values are calculated. Further, threshold-based segmentation is performed, which separates liver from abdominal CT scan image/abdominal MRI scan image. Noise in the liver image is reduced using median filter, and the quality of the image is improved by sigmoidal function. The image is then converted into binary image. The Chan–Vese (C–V) model demands an initial contour, which evolves outward. A novel algorithm is proposed to identify the initial contour inside the liver without user intervention. This initial contour propagates outward and continues until the boundary of the liver is identified accurately. This process terminates by itself when the entire boundary of the liver is detected. The method has been validated on CT images and MRI images. Results on the variety of images are compared with existing algorithms, which reveal its robustness, effectiveness, and efficiency.

scholarly journals Efficient FPGA Implementation of Human Detection from Video Sequences

2020 ◽  
Vol 9 (6) ◽  
pp. 242-248
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Median Filter ◽  
Region Of Interest ◽  
Video Stream ◽  
Human Detection ◽  
Video Sequences ◽  
Computer Vision Applications ◽  
And Performance ◽  
Better Than

Detection of Human is a vital and difficult task in computer vision applications like a police investigation, vehicle tracking, and human following. Human detection in video stream is very important in public security management. In such security related cases detecting an object in the video, sequences are very important to understand the behavior of moving objects which normally used in the background subtraction technique. The input data is preprocessed using a modified median filter and Haar transform. The region of interest is extracted using a background subtraction algorithm with remaining spikes removed using threshold technique. The proposed architecture is coded using standard VHDL language and performance is checked in the Spartan-6 FPGA board. The comparison result shows that the proposed architecture is better than the existing method in both hardware and image quality

A Novel Image Denoising Method Based on Adaptive Median Filter Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 2486-2490
Author(s):  
Kai Xie ◽  
Fen Zhang ◽  
Ying Zhou
Keyword(s):  
Image Denoising ◽  
Median Filter ◽  
Window Size ◽  
Denoising Method ◽  
Advantages And Disadvantages ◽  
Noise Image ◽  
Adaptive Median Filter ◽  
Image Blurring ◽  
Speed Up ◽  
Denoised Image

Image denoising is a very important part in image pre-processing. Due to the problem, which the edge details in the denoised image easily lost with the increase of the template-window size, the image blurring is increased. This paper presents comprehensive analysis on the advantages and disadvantages of existing algorithms and proposes a new algorithm which is called as adaptive median filter algorithm. This algorithm combines the selected mask filter algorithm and median filtering algorithm together. Experimental results show that the algorithm can speed up and the image blurring can be reduced. It also gets a satisfactory effect on the high-density noise image.

scholarly journals An effective approach for video condensation by ribbon carving

2018 ◽  
Vol 7 (2.21) ◽  
pp. 427
Author(s):  
M Latha ◽  
U V. Anbazhagu ◽  
. .
Keyword(s):  
Processing Speed ◽  
Background Subtraction ◽  
Median Filter ◽  
Memory Allocation ◽  
Seam Carving ◽  
Time Intervals ◽  
Kernel Model ◽  
Temporal Direction ◽  
Non Parametric

The proposed system aims in developing a method to stream video based on time intervals. Regular or irregular sub-sampling is not sufficient for scaling of video in time. Seam carving has been used where an Image can be resized either by inserting or removing pixels. Ribbon carving is an extension of seam carving which resizes the video in temporal direction. The non-parametric kernel model used for background subtraction has been replaced with temporal median filter. It enhances the processing speed and memory allocation has been more efficient, thus reducing the time of video without loss of data.  

scholarly journals WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 621 ◽  
Author(s):  
Sang-ha Lee ◽  
Gyu-cheol Lee ◽  
Jisang Yoo ◽  
Soonchul Kwon
Keyword(s):  
Motion Detection ◽  
Processing Speed ◽  
Background Subtraction ◽  
Median Filter ◽  
False Positives ◽  
Subtraction Method ◽  
Robust Performance ◽  
Background Region ◽  
Dynamic Background ◽  
F Measure

In this paper, we propose a method for calculating the dynamic background region in a video and removing false positives in order to overcome the problems of false positives that occur due to the dynamic background and frame drop at slow speeds. Therefore, we need an efficient algorithm with a robust performance value including processing speed. The foreground is separated from the background by comparing the similarities between false positives and the foreground. In order to improve the processing speed, the median filter was optimized for the binary image. The proposed method was based on a CDnet 2012/2014 dataset and we achieved precision of 76.68%, FPR of 0.90%, FNR of 18.02%, and an F-measure of 75.35%. The average ranking across categories is 14.36, which is superior to the background subtraction method. The proposed method was operated at 45 fps (CPU), 150 fps (GPU) at 320 × 240 resolution. Therefore, we expect that the proposed method can be applied to current commercialized CCTV without any hardware upgrades.

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

Quantitative EPMA of nitrogen : A tricky element in the electron-probe microanalyzer

1992 ◽  
Vol 50 (2) ◽  
pp. 1622-1623
Author(s):  
G.F. Bastin ◽  
H.J.M. Heijligers
Keyword(s):  
Background Subtraction ◽  
Electron Probe ◽  
Detection System ◽  
Higher Order ◽  
Light Elements ◽  
Strong Suppression ◽  
Electron Probe Microanalyzer ◽  
Quantitative Epma ◽  
Peak Count ◽  
Lead Stearate

Among the ultra-light elements B, C, N, and O nitrogen is the most difficult element to deal with in the electron probe microanalyzer. This is mainly caused by the severe absorption that N-Kα radiation suffers in carbon which is abundantly present in the detection system (lead-stearate crystal, carbonaceous counter window). As a result the peak-to-background ratios for N-Kα measured with a conventional lead-stearate crystal can attain values well below unity in many binary nitrides . An additional complication can be caused by the presence of interfering higher-order reflections from the metal partner in the nitride specimen; notorious examples are elements such as Zr and Nb. In nitrides containing these elements is is virtually impossible to carry out an accurate background subtraction which becomes increasingly important with lower and lower peak-to-background ratios. The use of a synthetic multilayer crystal such as W/Si (2d-spacing 59.8 Å) can bring significant improvements in terms of both higher peak count rates as well as a strong suppression of higher-order reflections.

Direct measurement of electron-diffraction-pattern intensities using an energy loss spectrometer

1989 ◽  
Vol 47 ◽  
pp. 668-669
Author(s):  
A. G. Jackson ◽  
M. Rowe
Keyword(s):  
Thermal Effects ◽  
Dynamic Range ◽  
Scattering Amplitude ◽  
Dynamical Theory ◽  
Site Symmetry ◽  
Proof Of Concept ◽  
Parallel Acquisition ◽  
Kinematic Approximation ◽  
Speed Up ◽  
Structure Calculations

Diffraction intensities from intermetallic compounds are, in the kinematic approximation, proportional to the scattering amplitude from the element doing the scattering. More detailed calculations have shown that site symmetry and occupation by various atom species also affects the intensity in a diffracted beam. [1] Hence, by measuring the intensities of beams, or their ratios, the occupancy can be estimated. Measurement of the intensity values also allows structure calculations to be made to determine the spatial distribution of the potentials doing the scattering. Thermal effects are also present as a background contribution. Inelastic effects such as loss or absorption/excitation complicate the intensity behavior, and dynamical theory is required to estimate the intensity value.The dynamic range of currents in diffracted beams can be 104or 105:1. Hence, detection of such information requires a means for collecting the intensity over a signal-to-noise range beyond that obtainable with a single film plate, which has a S/N of about 103:1. Although such a collection system is not available currently, a simple system consisting of instrumentation on an existing STEM can be used as a proof of concept which has a S/N of about 255:1, limited by the 8 bit pixel attributes used in the electronics. Use of 24 bit pixel attributes would easily allowthe desired noise range to be attained in the processing instrumentation. The S/N of the scintillator used by the photoelectron sensor is about 106 to 1, well beyond the S/N goal. The trade-off that must be made is the time for acquiring the signal, since the pattern can be obtained in seconds using film plates, compared to 10 to 20 minutes for a pattern to be acquired using the digital scan. Parallel acquisition would, of course, speed up this process immensely.

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