During the stage of image processing, “composite contour recognition technology” is applied and realized in this system, which makes the detection result continuous single pixel edge, and complete segmentation is fulfilled.
We propose a method to estimate the InSAR(Interferometric Synthetic Aperture Radar) interferometric phase based on the model of joint single pixel. In the method we construct the optimal weighted data vector by different coregistration error size and direction.
Experimental results have verified the veracity of solving contradiction of hold down noise and abstract edge with the method. Single-pixel detected with zero crossings by Marr edge detection operator can reduce complex of calculation in the next procedure of objects tracking.
This paper present the digital image processing that get the one-pixel width laser stripe image from the image got by the CCD camera such as filtering waves, threshold image segmentation algorithm, throw the 2-D laser stripe project to 3-D scatter dots.
Another idea is to replace central difference gradient by morphological gradient in diffusion coefficient discretization, the latter applying in anisotropic diffusion can enhance one-pixel line feature in noisy image while the former can not process efficiently.
Experiments showed that the proposed algorithm could generate a path one pixel wide with continuous edges, and the proposed algorithm had a better edge-detection accuracy than the 4-connected, 8-connected, and the Sobel techniques.
However, in case of an even number of voxels, a centerline one pixel wide cannot be exactly centered within the object.
It shows the standard deviation value computed at each time on a one pixel wide ring belonging to the myocardium.
In order to improve the accuracy, one pixel wide buffer zone was generated around the building polygons and the damage assessments were re-performed.
Note the input centroid scales are one pixel wide while the output centroid determinations are different for the two plots.
The experimental results demonstrate that the thinned curve is almost located in the middle of the original curve connectively with single pixel width and the processing speed is high.
Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles.
In order to apply the spatial structure information to remote sensing interpretation through fractal theory, an algorithm is introduced to compute the single pixel fractal dimension in remote sensing images.
Finally, we introduce a super-resolution technique: two or more medical images, which are shifted against each other in a subpixel region, are combined to resolve structures smaller than the size of a single pixel.
The technique is appropriate for microbubbly flows where the bubble size is much smaller than the area imaged by a single pixel and where there are many bubbles attenuating light within each pixel.
Single-pixel resolution ensemble correlation for micro-PIV applications
A new correlation method for particle image velocimetry (PIV) is proposed that yields velocity data at single-pixel spatial resolution.
This 'single-pixel ensemble correlation' method is particularly suited for (quasi-) stationary and periodic flows, which are typically encountered in many micro-PIV applications, such as microfluidics and micro-scale biological flows.
Both ensemble correlation and single-pixel correlation are applied to micro-channel flow.
With single-pixel ensemble correlation we obtained a spatial resolution of 300?nm.