Learning deep representation from coarse to fine for face alignment
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Puducherry.
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Matlab abstract 2016
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
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Telephone: 0413-2211159.
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Telephone: 0413-2211159.
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Learning similarity functions from qualitative feedback
The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure,
and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information,
the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to
a reference case. Experimental results, focusing on the ranking performance
of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information. See more at www.chengweiwei.com
Face alignment by deep convolutional network with adaptive learning rate
This paper is published in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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2. It develops a cognitive cost model based on how wedge shape impacts user estimation error and accounts for bias and individual differences.
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The document describes a final project for an EE368 class on face detection. The group developed methods to identify faces in images including color segmentation, morphological processing, template matching, and eigenfaces. They also attempted to classify detected faces by gender. Their best results came from RGB vector quantization for segmentation, morphological processing to find face centroids, and template matching with illumination correction, which gave near perfect detection. They achieved approximately 95% accuracy on a test set of 7 images.
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The document summarizes a modified CNN-based face recognition system that achieves improved accuracy rates over traditional CNN models. Preprocessing techniques like histogram equalization, self-quotient image, locally tuned inverse sine nonlinear, gamma intensity correction, and difference of Gaussian are applied to CNN models to further improve accuracy. On the Extended Yale B database, the proposed CNN model achieves an accuracy of 96.2% without preprocessing, and 99.8% with preprocessing. On the FERET database, accuracy improves from 71.4% without preprocessing to 76.3% with preprocessing.
A Modified CNN-Based Face Recognition System
In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract
unique facial features and softmax classifier is applied to classify facial images in a fully connected layer
of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes
and low value of learning rate, showed that the proposed model has improved the face recognition
accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B
database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and
DoG are applied to the CNN model. After the application of preprocessing techniques, the improved
accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET
Database with frontal face, before the application of preprocessing techniques, CNN model yields the
maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy
is improved to 76.3%
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ServiceNow CIS-ITSM Exam Dumps & Questions [2024]
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1.) Introduction
Our Movement is not new; it is the same as it was for Freedom, Justice, and Equality since we were labeled as slaves. However, this movement at its core must entail economics.
2.) Historical Context
This is the same movement because none of the previous movements, such as boycotts, were ever completed. For some, maybe, but for the most part, it’s just a place to keep your stable until you’re ready to assimilate them into your system. The rest of the crabs are left in the world’s worst parts, begging for scraps.
3.) Economic Empowerment
Our Movement aims to show that it is indeed possible for the less fortunate to establish their economic system. Everyone else – Caucasian, Asian, Mexican, Israeli, Jews, etc. – has their systems, and they all set up and usurp money from the less fortunate. So, the less fortunate buy from every one of them, yet none of them buy from the less fortunate. Moreover, the less fortunate really don’t have anything to sell.
4.) Collaboration with Organizations
Our Movement will demonstrate how organizations such as the National Association for the Advancement of Colored People, National Urban League, Black Lives Matter, and others can assist in creating a much more indestructible Black Wall Street.
5.) Vision for the Future
Our Movement will not settle for less than those who came before us and stopped before the rights were equal. The economy, jobs, healthcare, education, housing, incarceration – everything is unfair, and what isn’t is rigged for the less fortunate to fail, as evidenced in society.
6.) Call to Action
Our movement has started and implemented everything needed for the advancement of the economic system. There are positions for only those who understand the importance of this movement, as failure to address it will continue the degradation of the people deemed less fortunate.
No, this isn’t Noah’s Ark, nor am I a Prophet. I’m just a man who wrote a couple of books, created a magnificent website: http://www.thearkproject.llc, and who truly hopes to try and initiate a truly sustainable economic system for deprived people. We may not all have the same beliefs, but if our methods are tried, tested, and proven, we can come together and help others. My website: http://www.thearkproject.llc is very informative and considerably controversial. Please check it out, and if you are afraid, leave immediately; it’s no place for cowards. The last Prophet said: “Whoever among you sees an evil action, then let him change it with his hand [by taking action]; if he cannot, then with his tongue [by speaking out]; and if he cannot, then, with his heart – and that is the weakest of faith.” [Sahih Muslim] If we all, or even some of us, did this, there would be significant change. We are able to witness it on small and grand scales, for example, from climate control to business partnerships. I encourage, invite, and challenge you all to support me by visiting my website.
一比一原版(unc毕业证书)美国北卡罗来纳大学教堂山分校毕业证如何办理
原版一模一样【微信：741003700 】【(unc毕业证书)美国北卡罗来纳大学教堂山分校毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(unc毕业证书)美国北卡罗来纳大学教堂山分校毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(unc毕业证书)美国北卡罗来纳大学教堂山分校毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(unc毕业证书)美国北卡罗来纳大学教堂山分校毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(unc毕业证书)美国北卡罗来纳大学教堂山分校毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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But, how safe is a team? How can we determine if there are any factors that make the team unsafe or have an impact on the team’s culture?
In this mini-workshop, we’ll play games for psychological safety and team culture utilizing a deck of coaching cards, The Psychological Safety Cards. We will learn how to use gamification to gain a better understanding of what’s going on in teams. Individuals share what they have learned from working in teams, what has impacted the team’s safety and culture, and what has led to positive change.
Different game formats will be played in groups in parallel. Examples are an ice-breaker to get people talking about psychological safety, a constellation where people take positions about aspects of psychological safety in their team or organization, and collaborative card games where people work together to create an environment that fosters psychological safety.
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【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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This presentation was uploaded with the author’s consent.
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Learning deep representation from coarse to fine for face alignment
- 1. Company Add Your Company Slogan Learning deep representation from coarse to fine for face alignment Zhiwen Shao
- 3. Approach Dividing landmarks into two sets, backbone and remainder A few key landmarks can coarsely determine face shape • brow corners, eye corners, nose tip, mouth corners and chin tip
- 4. Approach
- 5. Approach Deep convolutional network outputs landmarks location the loss of backbone and remainder controls the relative weight of backbone
- 6. Approach the vector concatenating the ground truth coordinate of landmarks the vector concatenating the predicted coordinate of landmarks inter-pupil distance
- 7. Approach
- 8. Approach is initialized with (0.995) close to 1 • primarily predict backbone coordinates while slightly consider remaining landmarks With the reduction of , the network searches the optimal solution smoothly without missing fairly good intermediate solutions.
- 9. Approach
- 10. Approach
- 11. Results
- 12. Results CFT produces a error reduction of 21.37% on the COFW comparing to TCDCN
- 13. Results Example alignment results on Helen and IBUG
- 14. Results The results of RCPR and CFT on several challenge images from COFW
- 15. Conclusion Coarse-to-fine training algorithm Our network directly predicts the coordinates of landmarks using single network without any other additional operations The training algorithm can also be applied to other problems using deep convolutional network
- 16. Company Add Your Company Slogan Thanks!
Editor's Notes
- Good afternoon, everyone. Today I introduce my recent work about face alignment using deep convolutional network.
- Next, I will introduce my approach in detail.
- dozens of I find that there are a few key landmarks which can coarsely determine face shape including brow corners, eye corners, nose tip, mouth corners and chin tip.
- Because different face alignment datasets have different annotation of landmarks, so I fix the location of backbone set eye centers are also key points, but I don’t choose them because many face alignment datasets such as Helen and 300-W don’t contain eye center landmarks.
- Train a deep convolutional network. So I define this loss function. E consists of two items E sub b
- Since the approach is evaluated based on alignment error measured by the distances between estimated landmarks and ground truths normalized with the inter-pupil distance, we use normalized Euclidean distance In this way, during training, I can directly know the performance of my network from the loss value. F hat sub b
- I propose a coarse-to-fine training algorithm Firstly control parameter lambda equals lambda sub 0 and is decreased gradually to 0.5. for each circulation, using current lambda value, the network is trained until convergence and the network parameters theta is updated.
- is very close to 1 but isn’t equal to 1 If we choose lambda sub 0 to be 1, then subsequent search process will not be smooth.
- It’s clearly that trained model is optimized stage by stage and the prediction of landmarks location using finally learned model theta sup star is very accurate Therefore, different from other coarse-to-fine methods,
- Although the mean error of CFT tested on Helen and 300-W is slightly bigger than CFSS and TCDCN, CFT performs better on challenging COFW whose faces are taken with severe occlusion. Specifically, CFT produces a significant error reduction of 21.37% on the challenging COFW in comparison to the state-of-the-art TCDCN
- That’s all, Thank you. Any questions?