{
  "id": 11355,
  "title": "A call for help!",
  "url": "/competitions/datasciencebowl/discussion/11355",
  "author_name": "",
  "post_date": "2014-12-25T20:48:26.973Z",
  "votes": null,
  "comment_count": 1,
  "views": 966,
  "content": "<p>I am new to vision problems and have limited experience with image processing, and I'm using this competition as a way to familiarize myself with this type of problem as well as have an opportunity to try some of the exciting new techniques I've been reading&nbsp;about (Deep Nets, RBMs, Sparse-Coding, De-noising Auto-encoders, etc.).</p>\n<p><strong>My questions are:</strong></p>\n<p>1- What are the potentially useful techniques for this task and where are the good (stable and easy to use) implementations? (I use R and python, but I'm willing to explore&nbsp;alien territories)</p>\n<p>2- What are the common pre-processing steps that people take? (noise reduction, transformation invariance, bumping and blurring, etc.) and do they matter?</p>\n<p>3- Is it worth it to use SIFT and similar heuristic feature extractors?</p>\n<p>I know I am asking for almost everything, and I don't expect anyone to give away their secret ingredient, but I think the problem is fairly different than identifying digits or detecting faces (by far the most common examples out there) and therefore merits a discussion. I also noticed that the scores on&nbsp;the LB are varying a lot, which means there are some approaches out there that are WAY better&nbsp;than others, so I thought it would be interesting for people to share their thoughts and experiences.</p>",
  "messages": [
    {
      "id": "61876",
      "postDate": "12/25/2014 20:48:26",
      "content": "<p>I am new to vision problems and have limited experience with image processing, and I'm using this competition as a way to familiarize myself with this type of problem as well as have an opportunity to try some of the exciting new techniques I've been reading&nbsp;about (Deep Nets, RBMs, Sparse-Coding, De-noising Auto-encoders, etc.).</p>\n<p><strong>My questions are:</strong></p>\n<p>1- What are the potentially useful techniques for this task and where are the good (stable and easy to use) implementations? (I use R and python, but I'm willing to explore&nbsp;alien territories)</p>\n<p>2- What are the common pre-processing steps that people take? (noise reduction, transformation invariance, bumping and blurring, etc.) and do they matter?</p>\n<p>3- Is it worth it to use SIFT and similar heuristic feature extractors?</p>\n<p>I know I am asking for almost everything, and I don't expect anyone to give away their secret ingredient, but I think the problem is fairly different than identifying digits or detecting faces (by far the most common examples out there) and therefore merits a discussion. I also noticed that the scores on&nbsp;the LB are varying a lot, which means there are some approaches out there that are WAY better&nbsp;than others, so I thought it would be interesting for people to share their thoughts and experiences.</p>",
      "rawMarkdown": "",
      "votes": null
    },
    {
      "id": "61880",
      "postDate": "12/25/2014 23:13:20",
      "content": "<p><strong>3- Is it worth it to use SIFT and similar heuristic feature extractors?</strong></p>\n<p>Even-though, it comes with OpenCV which is an OSI approved BSD license, SIFT is patented and nonfree so you probably don't want to use that for this comp. &nbsp;Unless an admin states otherwise.</p>",
      "rawMarkdown": "",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 61880,
      "author_name": "sammiller",
      "author_url": "",
      "post_date": "12/25/2014 23:13:20",
      "content": "<p><strong>3- Is it worth it to use SIFT and similar heuristic feature extractors?</strong></p>\n<p>Even-though, it comes with OpenCV which is an OSI approved BSD license, SIFT is patented and nonfree so you probably don't want to use that for this comp. &nbsp;Unless an admin states otherwise.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "61876": "",
    "61880": ""
  },
  "source": "meta"
}