We wanted to send you a card but didn’t have your mailing address. So we just ordered you a “New Year” present from L.L. Bean. Unfortunately – it will take 3-4 weeks to be shipped to you. Please let us know when it arrives and if you like it.
去年の夏にホームステイしたファミリーからのメールなんだけど、これって「３，４週間でプレゼント届くと思うよ」ってメールだよね？ １２月の終わりに送ったのならもう届くハズなんだけど・・・まだ来ない。 let them knowできねーーーー！！！
>>190をやってみた。 When I was masterbating and about to climax, I tried to grab a tissue paper, but the box is empty. So I pulled out my penus skin to hold the ejaculation and rushed to the bathroom, but fell down at a step. My mom just saw me spreading out the contents of my penus on the floor. I said it was undiluted Calpico, but obviously it was sperm. That was the end of my life. 「本当にありがとうございました」のニュアンスがうまく訳せていないかも。
>>228 Campus notebooks contain the best ruled foolscap suitable for writing. 直訳すると「キャンパスノートには筆記に適した最高の罫線入り用紙が入っています」 だと思う。ちなみにfoolscapは、特定のサイズの筆記用紙のことらしい。
Then took the other, as just as fair, And having perhaps the better claim, Because it was grassy and wanted wear; Though as for that the passing there Had worn them really about the same, 長文ですがお願いします。自分でもやったんですがさっぱりです。
Is "T" nothing more than another too simple to be true Hollywood fantasy? Many people thought this movie was a failure because it was unbelievable. How can we learn to appreciate this movie? The director, F, made to other very popular movies, "The Shawshank Redemption" and "The Greem Mile" . All three movies tell us that we have end potential to exstend the natural goodness of our humanity. The message of "T" is that ordinary people are good and they deserve happiness. That's the way things should be. Jim, the star of the movie, plays pete, a Hollywood scrip writer, who has an accident and wake up with amnesia. P has no memory of anything that happened before the accident. He does not know who he is. In Lawson because P looks like Luke, a Lawson man lost in war, he gets a second chance at life. The dog-eat-dog world of Hollywood business had started to shut down P's dreams. The Wizerd of Oz song ask "Brids fly over the rainbow. Why then, oh why can't I ?" As Luke, P flies over the rainbow. In Lawson, dreams survive, sweetness is genuine, and troubles don't harden people's hearts 訳してプリーズ
Real-time appearance-based Monte Carlo localization
A new technique for vision processing is presented which lets a mobile robot equipped with an omnidirectional camera perform appearancebased global localization in real time. The technique is applied directly to the omnidirectional camera images, producing low-dimensional rotation invariant feature vectors without any training or set-up phase. Using the feature vectors, particle filters can accurately estimate the location of a continuously moving real robot, processing 5000 simultaneous localization hypotheses on-line. Estimated body positions overlap the actual ones in over 95% of the time steps. The feature vectors show a graceful degradation against increasing levels of simulated noise and occlusion.
Robot localization in indoor environments, using long-range distance sensors like laser range finders [56], millimeter-wave radar [12] or sonars [32,7,54], is now generally considered as a solved problem. Localization using vision is however still an open problem. Besides being an interesting area of research relating to neuroscience and cognition, vision as a primary sensor has a number of advantages. Cameras have a virtually unlimited range and can cover large fields of view at high update rates. Due to the passive nature, multiple cameras do not interfere with each other when operating even in the same area. Information like color and texture is readily available in the images, and camera systems are available at relatively low cost and have a limited power consumption.
いやーありがとうございます！ 助かります＾＾たまりません！！！ これも助けてください！！続きです＾＾； In this article, we present a novel technique for image processing which enables a mobile robot equipped with an omnidirectional camera to perform localization in real time. The technique extracts features from camera images based on higher-order local auto-correlations, enabling storage of a large set of visited locations in the form of feature vectors. The key idea is a novel polar higher-order local auto-correlation (PHLAC) feature extractor. The PHLAC was outlined in [33]; we present here the details behind the design and a larger set of experiments, including results on orientation estimation and robustness against noise and occlusion. The PHLAC is based on HLAC, which is known to be translation invariant. It is also to be noted that translation invariance on a panoramic view is equivalent to rotation invariance on an omnidirectional view. The combination of these properties gives rotation invariance of the polar HLAC directly on the omnidirectional view. As the PHLAC vectors are also low dimensional, the image matching is substantially simplified, enabling a large set of localization hypotheses to be matched on-line in real time. As the localization space is continuous, but computation is to take place on-line using limited resources, particle filters are here used for estimating the approximate position and orientation of the robot. Our system lets a mobile robot perform global localization and recover from kidnappings based on only visual data and odometry readings.
Hardly one mature adult in a thousand, or ten thousand, could in any three years of his life learn as much and grow as much in his understanding of the world around him, as every infant learns and frows in his first three years.
As previously discussed, two clients can have the same identification. If the identification is a user-supplied text string (for example, a host name), a collision occurs when two users choose the same name. For example, if The Lord of the Rings (a popular fantasy trilogy) has a lot of fans at any given site, several users may choose the name Gandalf for their computer.
In Section 2, existing works on vision-based localization are reviewed, with focus on appearance-based techniques. Section 3 introduces polar higher-order local auto-correlation (PHLAC) which is then used for probabilistic localization based on Sequential Monte Carlo, described in Section 4. Results from experiments using a real robotic system are then presented in Section 5, along with an analysis of the PHLAC vectors and their robustness against noise and occlusion. Section6 deliberates on how the extracted vectors can be made more distinct for different locations and how invariance against illumination can be introduced directly into the extraction process. Finally, in Section 7, main contributions and results are summarized, and major strengths and weaknesses of our current system are discussed. 2. Vision-based localization A camera-based equivalent of a typical distance-based localization system would be acquiring a detailed threedimensional model of the environment [39]. This 3D model can then be used during localization to generate the expected 2D projections (camera images) at different locations. Instead of being able to internally generate complete camera images, the system could settle for being able to predict what features [49] would be detected at each location. The features in this case make up a sort of sparse 3D map of the environment. However, the 3D model-based approaches regularly depend on auxiliary distance sensors like stereo [8] and trinocular [10] cameras. An alternative to the Cartesian 3D models is to use an appearance-based approach [22]. Appearance-based localization, in its simplest form, involves taking raw snapshots at various locations and storing them along with positioning information. The current camera image can then subsequently be matched against these memorized images to find the most probable current location. お願いします＾＾；
