[起诉书扫图]因为Street View,Google坐上被告席

美国一对夫妇因Google的StreetView街景服务侵犯其隐私权提起诉讼,声称该服务破坏了他们购房追求隐私的目的,并导致精神痛苦及房产贬值,要求赔偿25000美元。

美国匹兹堡消息,一对夫妇最近将Google送上了被告席,他们宣称Google的"Street View"街景服务侵犯了他们的隐私权,甚至是"蓄意或粗暴的鲁莽入侵".
Google 的地图和街景服务上的内容在美国已经非常完整,Google接下来可能会遇上类似于本案的更多麻烦.这对夫妇称购买目前所居住的房子本身就是为了隐私考虑,而Google的加入彻底粉碎了他们的投资目的,引起他们精神痛苦,削弱了房屋的物业价值,他们正在寻求25000美元的损害赔偿.以下是起诉书全文.












Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief (Dean et al., 2012) implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over 96% accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-theart, achieving 97.84% accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over 90% accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to transcribing synthetic distorted text from a popular CAPTCHA service, reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text as one of the cues to distinguish humans from bots. With the proposed approach we report a 99.8% accuracy on transcribing the hardest category of reCAPTCHA puzzles. Our evaluations on both tasks, the street number recognition as well as reCAPTCHA puzzle transcription, indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值