Buy Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Home > Mathematics and Science Textbooks > Science: general issues > Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.
Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.

Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.


     0     
5
4
3
2
1



Out of Stock


Notify me when this book is in stock
X
About the Book

Energy minimization is an important technique in computer vision that has been applied to practically all early vision problems. Several methods have been proposed to solve the problem and to date, the most popular ones have been discrete optimization approaches such as graph cuts and belief propagation. While convex programming approaches have been proposed, the resulting problems were practically unsolvable due to their size. This thesis describes a novel approach to applying convex optimization, in particular interior point barrier methods, to solve some energy minimization problems in an efficient manner. The methods exploit the fact that computer vision problems usually have specific structure to them. Two problems are handled in this thesis. In the first, a popular energy minimization technique, graph cuts, is reduced to an unconstrained l1 norm minimization problem. This problem is then solved using linear programming. The resultant algorithm is parallelizable and the thesis provides one possible implementation using a graphical processing unit. Results of using this approach on interactive foreground-background segmentation problems are provided. In the second problem, the energy function associated with stereo matching is directly converted into a convex function via an approximation of the data penalties. The resultant linear program is again solved using interior point methods and exhibits very similar structure to the first approach. Results on the standard Middlebury stereo matching dataset will be discussed.


Best Sellers


Product Details
  • ISBN-13: 9781243494221
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 246 mm
  • Weight: 308 gr
  • ISBN-10: 1243494220
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Spine Width: 9 mm
  • Width: 189 mm


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.
Proquest, Umi Dissertation Publishing -
Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Applying Convex Optimization Techniques to Energy Minimization Problems in Computer Vision.

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    Fresh on the Shelf


    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!