First, if you don't know featurepoints it's basically an app (ios/android) where you download others free apps to get points, there a many rewards available such as paypal money or amazon, itunes, steam and others gift cards. (wordlwide)
Feature Points Hack
Co-registration is the process of transforming multiple LIDAR scanning data into a common reference coordinate system. For high precision of co-registration, a sufficient number of matched points in pairwise point clouds is required in addition to the geometry. Co-registration method and stability of scanners and targets at the scan site also affect the final accuracy of multi-scanned data. Moreover, the occlusion area of a scanning site must be minimized while the scanning coverage is maximized. In this regard, square planar targets were mainly utilized, and subsidiary target-free techniques were applied to improve time efficiency and co-registration precision.
In LIDAR point cloud data processing, georeferencing follows co-registration. A procedure that converts the relative coordinates of a point cloud into an absolute coordinate system, georeferencing is one of the most important processes in accurate topographic mapping with terrestrial LIDAR data. For a robust georeferencing process, a significant number of fixed control points whose 3D coordinates are known is required. However, since the terrain is very complex and irregular in Antarctica, it is difficult to extract well-defined 3D coordinates of feature points from LIDAR scanning data. To acquire accurate 3D coordinates of feature points, we developed a practical georeferencing module consisting of a GNSS receiver and planar target. The modules were installed during LIDAR scanning and used for georeferencing after the scanning. Triangulated Irregular Network-based (TIN-based) linear interpolation was applied to generate a DEM from the processed point cloud [44,45].
Considering the pros and cons of the co-registration methods and the environment of Antarctica, target-based co-registration using the square planar targets (7.62 7.62 cm) as matching points was selected and conducted. The research team also avoided strong winds over 10 m/s and secured tripods with ropes and turnbuckles to achieve sufficient precision of co-registration. When unpredictable strong winds affected the stability of artificial targets, we extracted matching points from natural feature points in overlapped point clouds to apply the target-based algorithm. The ICP algorithm was additionally applied to improve co-registration precision.
Description of data processing for DEM generation: (a) TIN model (red circles are points of an observed point cloud, red lines are generated TIN, and green triangles represent the removed TIN elements with long edges and consisting of a large area around the point cloud); (b) TIN-based interpolation for DEM generation: blue lines are DEM grids, and the 3D coordinates of a white circle (G) are xy location (xg, yg) and interpolated height (zg) from the vertices of the TIN element (P1, P2 and P3).
Since no planar target was detected in the point cloud observed in station 4 because of an unpredictable blizzard on February 10, natural feature points were used for initial co-registration between the point clouds of stations 3 and 4, and the ICP algorithm was then applied to improve the precision of co-registration. Figure 10 represents the extracted feature points in overlapped point clouds and the result of co-registration between a pair of point clouds observed at station 3 and station 4. Initial co-registration parameters were estimated using the extracted features. Then, the ICP algorithm was applied based on the estimated parameter. As shown in Figure 10b, although no artificial target was used, co-registration was successful. The average RMSE of co-registration could be achieved by 5 mm.
Co-registration between point clouds of stations 3 and 4: (a) examples of extracted natural feature points used for co-registration; (b) co-registration results (left and top-right: merged point cloud with color information, bottom-right: red colored point cloud and blue-colored point cloud were achieved at stations 3 and 4, respectively).
Figure 14 shows the elevation error for the point cloud acquired at station 4. Locational inconsistency between the GNSS data and the point cloud of station 4 was only 32.1 cm of RMSE, and the error trend was similar to that of the whole point cloud dataset. If there are adequate feature points for co-registration and a sufficiently overlapped area between a pair of point clouds, the co-registration method based on feature points extracted in overlapped point clouds and the ICP algorithm can achieve accuracy as high as that of co-registration using artificial targets.
This study proposes and demonstrates the following practical solutions for observations using a terrestrial LIDAR system in Antarctica: (1) a lagging cover with a heating pack to maintain the temperature of the terrestrial LIDAR system; (2) co-registration using square planar targets and two-step point-merging methods based on extracted feature points and the ICP algorithm; and (3) the georeferencing module consisting of an artificial target and GNSS. From the in situ observations, it is evident that the proposed methods and devices ensured stable operation of the terrestrial LIDAR system and accurate observations. Despite the complex terrain, irregular winds, and thick snow cover, there was only 27.7 cm of RMSE between the checkpoints observed by RTK GNSS and the DEM obtained from the LIDAR scanning data.
Nancy and Rachel provide the particulars into the spearfishing hack, which is being claimed by the "Fancy Bears Hack Team," a Russian espionage group that insists U.S. athletes have been given free passes on positive drug tests.
Perhaps the largest annoyance in YouTube - other than those pesky ads - is the proliferation of annotations. You know the text boxes that pop up at random points in videos, often coercing you to subscribe or comment or give the clip a thumbs up? Yeah, those things. It seems like most videos are flooded with annotations, enough so that the original video can barely be seen under it all.
One way to deal with this is to disable annotations on a video-to-video basis, but if you want a more global fix, YouTube actually has an account feature that lets you disable annotations by default. Just go to your Account Playback settings and uncheck the "Show Annotations on Videos" option.
The irony here, of course, is that it's often the great products that are user-oriented enough to add data download options. Case in point: the online gradebook LearnBoost which has just added a new feature today to make it easier for users to export their data. The new feature, called Backup lets you download all the data that you've ever posted to LearnBoost -- grades, attendance, events, policies, and profile information.
The generated comparison, is between your feature branchmy-new-feature, and the place in master from which you branchedmy-new-feature. In other words, you can keep updating masterwithout interfering with the output from the comparison. More detail?Note the three dots in the URL above (master...my-new-feature) andsee Two and three dots in difference specs.
April was indeed a cruel month for Sony, which admitted that hackers had gained access to names, addresses, email addresess, birth dates, passwords and IDs for over 100 million PlayStation Network, Qrocity, and Online Entertainment customers.
Twitter has seen no end of high-profile hacks in recent months. Burger King had its account commandeered, as did Jeep. Meanwhile, the feeds of several media outlets, including the Financial Times, the BBC, and the Associated Press, have been taken over by a group calling itself the Syrian Electronic Army. Now, in the wake of these attacks, Twitter is introducing a beefed-up (and totally optional) user authentication feature.
Once you activate the verification feature, Twitter will send you an SMS message each time you attempt to sign on to Twitter. That message will contain a six-digit pin that you'll use to complete the log-in process. Of course, all this means you always need to have your phone nearby, which can be a bit of a pain.
Today we are going to talk about a new feature right here on AVC. It's been running for a day or so, so some of you may have already noticed it. Right next to the comments link, there is a new link that says "disqus commenter breakdown". It looks like this:
Given how long and busy some of the comment threads are here on AVC, I can imagine a number of alternative views that one could construct that would be useful. If anyone else wants to hack on the Disqus API and create something useful, I am happy to give them similar real estate.
It is a terrific hack especially for blogs that elicit a lot of comments. Nice job Kevin. I agree with others that these need to be displayed and highlighted by disqus. Services such as WordPress have shown what an open and active community can bring in terms of values. Perhaps some disqus advocates can start a group on google or another platform and keep track of these.
Hello Fred,I love that you practice what you preach and I love the way this blog is open to innovation.But at some point us readers might have to stage an intervention.The front page of AVC has so many added features and widgets that it has slowed to a crawl.The front page of AVC makes *over 400 http requests* with a first view page load time of 10-15 seconds (see attached waterfall chart). The front page is over 3MB (the average webpage in 2012 was around 1MB). The page loads 50 individual javascript files totaling 800kb+.At this rate I estimate that at some point in 2014 the weight of the AVC home page will cause it to collapse into itself in some sort of black hole scenario sucking all of us down along with its inescapable gravity and possible also the rest of the universe.(Apologies to Kevin, I like your work and it is certainly not the cause of the page performance. This was just a trigger to my widget rant.) 2ff7e9595c
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