Robust Online Video Background Reconstruction Using Optical Flow and Pixel Intensity Distribution

2008 ◽  
Author(s):  
X. Cai ◽  
F. H. Ali ◽  
E. Stipidis
Keyword(s):  
Optical Flow ◽  
Intensity Distribution ◽  
Online Video ◽  
Pixel Intensity ◽  
Background Reconstruction

Accurate Liver Border Identification Model in CT Scan Images

2021 ◽  
pp. 2150039
Author(s):  
Sangeeta K. Siri ◽  
S. Pramod Kumar ◽  
Mrityunjaya V. Latte
Keyword(s):  
Ct Scan ◽  
Intensity Distribution ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Pixel Intensity ◽  
Chain Code ◽  
Computer Based ◽  
Scanned Images ◽  
Liver Region ◽  
Liver Image

The liver is an important organ in human body with certain variations in its edges, color, shape and pixel intensity distribution. These uncertainties may be because of various liver pathologies, hereditary or both. Along with it, liver has close proximity to its nearby organs. Hence, identifying liver in scanned images is a challenging step in image processing. This task becomes more imprecise when liver diseases are present at the edges. The liver segmentation is prerequisite for liver volumetry, computer-based surgery planning, liver surgery modelling, surgery training, 3D view generation, etc. The proposed hybrid segmentation method overcomes the problems and identifies liver boundary in Computed-Tomography (CT) scan images accurately. In this paper, the first step is to study statistics of pixel intensity distribution within liver image, and novel methodology is designed to obtain thresholds. Then, threshold-based segmentation is applied which separates the liver from abdominal CT scan images. In the second step, liver edge is corrected using improved chain code and Bresenham pixel interconnection methods. This provides a precise liver image. The initial points are located inside the liver region without user interventions. These initial points evolve outwardly using Fast Marching Method (FMM), identifying the liver boundary accurately in CT abdominal scan images.

scholarly journals Determination of Geometric Parameters of Cracks in Concrete by Image Processing

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yuriy Vashpanov ◽  
Jung-Young Son ◽  
Gwanghee Heo ◽  
Tatyana Podousova ◽  
Yong Suk Kim
Keyword(s):  
Intensity Distribution ◽  
Concrete Surface ◽  
Optical Microscope ◽  
Geometrical Parameters ◽  
Mathematical Relationship ◽  
Pixel Intensity ◽  
Cracked Concrete ◽  
Close Distance ◽  
Rgb Image

The 8-bit RGB image of a cracked concrete surface, obtained with a high-resolution camera based on a close-distance photographing and using an optical microscope, is used to estimate the geometrical parameters of the crack. The parameters such as the crack’s width, depth, and morphology can be determined by the pixel intensity distribution of the image. For the estimation, the image is transformed into 16-bit gray scale to enhance the geometrical parameters of the crack and then a mathematical relationship relating the intensity distribution with the depth and width is derived based on the enhanced image. This relationship enables to estimate the width and depth with ±10% and ±15% accuracy, respectively, for the crack samples used for the experiments. It is expected that the accuracy can be further improved if the 8-bit RGB image is synthesized by the images of the cracks obtained with different illumination directions.

scholarly journals Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow

2018 ◽  
Vol 4 (7) ◽  
pp. 90 ◽  
Author(s):  
Srivatsa Prativadibhayankaram ◽  
Huynh Luong ◽  
Thanh Le ◽  
André Kaup
Keyword(s):  
Optical Flow ◽  
Prior Information ◽  
Online Video

Structure images of Si, Ge and Mos2 crystals and some application to radiation damage studies

1978 ◽  
Vol 36 (1) ◽  
pp. 292-293 ◽  
Author(s):  
K. Izui ◽  
T. Nishida ◽  
S. Furuno ◽  
H. Otsu ◽  
S. Kuwabara
Keyword(s):  
Radiation Damage ◽  
Exposure Time ◽  
Gaussian Distribution ◽  
Intensity Distribution ◽  
Chromatic Aberration ◽  
Magnetic Lens

Recently we have observed the structure images of silicon in the (110), (111) and (100) projection respectively, and then examined the optimum defocus and thickness ranges for the formation of such images on the basis of calculations of image contrasts using the n-slice theory. The present paper reports the effects of a chromatic aberration and a slight misorientation on the images, and also presents some applications of structure images of Si, Ge and MoS2 to the radiation damage studies.(1) Effect of a chromatic aberration and slight misorientation: There is an inevitable fluctuation in the amount of defocus due to a chromatic aberration originating from the fluctuations both in the energies of electrons and in the magnetic lens current. The actual image is a results of superposition of those fluctuated images during the exposure time. Assuming the Gaussian distribution for defocus, Δf around the optimum defocus value Δf0, the intensity distribution, I(x,y) in the image formed by this fluctuation is given by

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Nicolas Schulwitz: Alles auf eine Karte

2016 ◽  
Vol 21 (06) ◽  
pp. 70-73
Author(s):  
Christian Schünemann
Keyword(s):  
Online Video ◽  
Start Up

Bei einer Hochzeit hörte Nicolas Schulwitz (32) von einer zündenden Idee: Wie wäre es, mit einer Online-Video-Sprechstunde das Verhältnis von Arzt und Patient zu revolutionieren? Gesagt, getan. Schulwitz kündigte einen sicheren Job und gründete das Start-up-Unternehmen Patientus.

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