uncleflo
Registered since September 28th, 2017
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GelPlane Professional Osmosis Treatment, Gel Plane, Gel-Plane, strips, peels gelcoat, stripper
Saved by uncleflo on December 19th, 2019.
The removal of the gelcoat is the first stage of any Osmosis blistering treatment and peeling is widely accepted as the most cost effective system for guaranteeing an assured repair. The GelPlane has well documented advantages of speed, economy and quality of finish but further, with the dust extraction system the workplace remains clean. GelPlane International provides the product not the peeling service allowing the yard to benefit from convenient work scheduling, quality control and the profitability of working in house.
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How TF-IDF algorithm determines keyword importance - arbitrue Blog
Saved by uncleflo on December 23rd, 2018.
There are many tools in the developer’s toolbox when it comes to automatic data extraction. A good example is TF-IDF algorithm (Term Frequency – Inverse Document Frequency) which helps the system understand the importance of keywords extracted using OCR. Here’s how TF-IDF can be used for invoice and receipt recognition. In this article we focus on other techniques in order to make this text file “understandable” to a computer. For this purpose, we must delve into the world of NLP or Natural Language Processing. We will focus mainly on how we can transform our file of raw text into a format that will easily be understandable by our algorithm. In a nutshell, TF-IDF is a technique for understanding how important a word is in a document which is often used as a weighting factor for numerous use cases. TF-IDF takes under consideration how frequent a word appears in a single document in relation to how frequent that word is in general. Search engines can use TF-IDF to determine which results are the most relevant for a search query.
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Weighting words using Tf-Idf - NLP-FOR-HACKERS
Saved by uncleflo on December 23rd, 2018.
If I ask you “Do you remember the article about electrons in NY Times?” there’s a better chance you will remember it than if I asked you “Do you remember the article about electrons in the Physics books?”. Here’s why: an article about electrons in NY Times is far less common than in a collection of physics books. It is less likely to stumble upon the “electron” concept in NY Times than in a physics book. Let’s consider now the scenario of a single article. Suppose you read an article and you’re asked to rank the concepts found in the article by importance. The chances are you’ll basically order the concepts by frequency. The reason is simply that important stuff would be mentioned repeatedly because the narrative gravitates around them. Combining the 2 insights, given a term, a document and a collection of documents we can loosely say that:importance ~ appearances(term, document) / count(documents containing term in collection).
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INEOS is a global manufacturer of petrochemicals, speciality chemicals and oil products
Saved by uncleflo on October 23rd, 2018.
INEOS is a young company. It has grown to become a leading chemical company with sales today of around $60 billion. Most of our employees have spent all their working lives in the chemical or oil industry. We continue unashamedly to extract best practices from this very impressive group of people in all spheres of activity. We believe INEOS is a refreshing place to work and we are prepared to embrace new approaches to business.
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A battle of three descriptors: SURF, FREAK and BRISK
Saved by uncleflo on August 27th, 2012.
I think developers and research guys who works with object recognition, image registration and other areas that uses keypoint extraction can find this post useful. Recently (from 2.4.2) a new feature descriptor algorithm was added to OpenCV library. FREAK descriptor is claimed to be superior to ORB and SURF descriptors, yet it's very fast (comparable to ORB). Also people in comments on my blog mentioned BRISK descriptor which is also new and more efficient than SURF. Well, finally i find a time to compare them and publish my research results.
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