Reserch on Removal Technology of Snowflakes in Video Images

2013 ◽  
Vol 448-453 ◽  
pp. 3654-3657
Author(s):  
Hui Ying Ning
Keyword(s):  
Clustering Method ◽  
Processing Effect ◽  
Video Images ◽  
Removal Technology ◽  
Heavy Snow ◽  
Background Brightness

Using improved K-means clustering method to remove snowflakes in video images getted in snowy day is put forward. According to the brightness changes of the image, we clustering estimate brightness changes of all the pixels in the image by using background brightness values instead of the brightness values of the snowflakes to achieve the goal of removing snowflakes. The results of experiment show that the processing effect of the method is very obvious for light snow and heavy snow, and even storm snow.It can get good results.

Research of Snow-Degraded Image Clearness Methods

2013 ◽  
Vol 423-426 ◽  
pp. 2452-2455
Author(s):  
Kai Song ◽  
Dong Yang ◽  
Xin Nie
Keyword(s):  
Video Image ◽  
Luminous Intensity ◽  
Temporal Difference ◽  
Absolute Value ◽  
Average Value ◽  
Video Images ◽  
Degraded Image ◽  
Heavy Snow ◽  
The Absolute

An improvement of coterminous frames differencing is proposed. By using the improved algorithm, the snow point from video image obtained in the snow day can be removed significantly. The snow regions are extracted by the temporal difference of pixels from the five adjacent frames in video images, The absolute value of differential luminance of each frame is then obtained. The luminous intensity of the background and effects by snow can be further computed. Finally, the intensity of the contaminated pixels is replaced by the average value of future and past frames. The results of the experiment show that this algorithm can improve the quality of video images in light snow, heavy snow even heavy snowstorm strongly.

Computing cell tractions from particle displacements on silicone rubber substrata

1994 ◽  
Vol 52 ◽  
pp. 162-163
Author(s):  
Tim Oliver ◽  
Akira Ishihara ◽  
Ken Jacobsen ◽  
Micah Dembo
Keyword(s):  
Plane Stress ◽  
Linear Elasticity ◽  
Silicone Rubber ◽  
Mathematical Analysis ◽  
Elastic Recovery ◽  
Video Images ◽  
Cell Traction Forces ◽  
Latex Beads ◽  
Cell Traction ◽  
Better Than

In order to better understand the distribution of cell traction forces generated by rapidly locomoting cells, we have applied a mathematical analysis to our modified silicone rubber traction assay, based on the plane stress Green’s function of linear elasticity. To achieve this, we made crosslinked silicone rubber films into which we incorporated many more latex beads than previously possible (Figs. 1 and 6), using a modified airbrush. These films could be deformed by fish keratocytes, were virtually drift-free, and showed better than a 90% elastic recovery to micromanipulation (data not shown). Video images of cells locomoting on these films were recorded. From a pair of images representing the undisturbed and stressed states of the film, we recorded the cell’s outline and the associated displacements of bead centroids using Image-1 (Fig. 1). Next, using our own software, a mesh of quadrilaterals was plotted (Fig. 2) to represent the cell outline and to superimpose on the outline a traction density distribution. The net displacement of each bead in the film was calculated from centroid data and displayed with the mesh outline (Fig. 3).

Depth-of-Processing Effect on Speed of Recollection in Word-Stem Cued Recall

2008 ◽  
Author(s):  
Severine Fay ◽  
Laurence Taconnat ◽  
Badiaa Bouazzaoui ◽  
David Clarys ◽  
Michel Isingrini
Keyword(s):  
Cued Recall ◽  
Depth Of Processing ◽  
Processing Effect

“Are You Religious or are You Saved?”

2012 ◽  
Vol 6 (1) ◽  
pp. 47-63
Author(s):  
Stephen Pihlaja
Keyword(s):  
Multimodal Approach ◽  
Membership Categorization Analysis ◽  
Evangelical Christian ◽  
Video Images ◽  
User Comments ◽  
Membership Categorization ◽  
Longitudinal Observation

Using membership categorization analysis, this article investigates membership categories in a YouTube video made by an Evangelical Christian in which he differentiates between “saved” and “religious” users. Analysis will take a discourse-centred, multimodal approach grounded in longitudinal observation, using analysis of video discourse to instruct analysis of video images and user comments. Findings will show that categorization is accomplished by using recognized categories with ambiguous descriptions of category-bound activities that include metaphors, such as “being hungry for God” and not “hanging out with atheists.” These categories are recognized by commenters on the video, but the category bound activities applied to the category members are disputed. Findings will also show that scriptural reference plays an important role in categorization in the video, drawing on direct Bible quotes as well as paraphrases of key passages.

Sign in / Sign up

Export Citation Format

Share Document