key point
Универсальный англо-русский словарь . Академик.ру . 2011 .
Смотреть что такое «key point» в других словарях:
key point — index landmark (significant change) Burton s Legal Thesaurus. William C. Burton. 2006 … Law dictionary
key point — svarbiausiasis taškas statusas T sritis Gynyba apibrėžtis Vietovė ar bazė, kurių sunaikinimas ar užėmimas padarytų didelį poveikį karo eigai ar karinės operacijos sėkmei. atitikmenys: angl. key point pranc. point sensible … NATO terminų aiškinamasis žodynas
key point — A concentrated site or installation, the destruction or capture of which would seriously affect the war effort or the success of operations … Military dictionary
key point — site or installation whose destruction by the enemy would damage the course of the war … English contemporary dictionary
Point Blanc — infobox Book | name = Point Blank title orig = translato = image caption = First edition cover author = Anthony Horowitz country = United Kingdom language = English series = Alex Rider series genre = Adventure, spy publisher = Walker Books… … Wikipedia
point sensible — svarbiausiasis taškas statusas T sritis Gynyba apibrėžtis Vietovė ar bazė, kurių sunaikinimas ar užėmimas padarytų didelį poveikį karo eigai ar karinės operacijos sėkmei. atitikmenys: angl. key point pranc. point sensible … NATO terminų aiškinamasis žodynas
Key West — is an island in the Straits of Florida on the North American continent at the southernmost tip of the Florida Keys.Key West is politically within the limits of the city of Key West, Monroe County, Florida, United States. The city also occupies… … Wikipedia
Key West — Beach … Wikipédia en Français
Key West (Floride) — Key West Key West Beach Key West … Wikipédia en Français
Key Biscayne — is an island located in Miami Dade County, Florida, United States, between the Atlantic Ocean and Biscayne Bay. It is the southernmost of the barrier islands along the Atlantic coast of Florida, and lies south of Miami Beach and southeast of… … Wikipedia
Key deer — A male Key Deer on No Name Key in the Florida Keys Conservation status … Wikipedia
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key point
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Тематики
20 key event
См. также в других словарях:
key point — index landmark (significant change) Burton s Legal Thesaurus. William C. Burton. 2006 … Law dictionary
key point — svarbiausiasis taškas statusas T sritis Gynyba apibrėžtis Vietovė ar bazė, kurių sunaikinimas ar užėmimas padarytų didelį poveikį karo eigai ar karinės operacijos sėkmei. atitikmenys: angl. key point pranc. point sensible … NATO terminų aiškinamasis žodynas
key point — A concentrated site or installation, the destruction or capture of which would seriously affect the war effort or the success of operations … Military dictionary
key point — site or installation whose destruction by the enemy would damage the course of the war … English contemporary dictionary
Point Blanc — infobox Book | name = Point Blank title orig = translato = image caption = First edition cover author = Anthony Horowitz country = United Kingdom language = English series = Alex Rider series genre = Adventure, spy publisher = Walker Books… … Wikipedia
point sensible — svarbiausiasis taškas statusas T sritis Gynyba apibrėžtis Vietovė ar bazė, kurių sunaikinimas ar užėmimas padarytų didelį poveikį karo eigai ar karinės operacijos sėkmei. atitikmenys: angl. key point pranc. point sensible … NATO terminų aiškinamasis žodynas
Key West — is an island in the Straits of Florida on the North American continent at the southernmost tip of the Florida Keys.Key West is politically within the limits of the city of Key West, Monroe County, Florida, United States. The city also occupies… … Wikipedia
Key West — Beach … Wikipédia en Français
Key West (Floride) — Key West Key West Beach Key West … Wikipédia en Français
Key Biscayne — is an island located in Miami Dade County, Florida, United States, between the Atlantic Ocean and Biscayne Bay. It is the southernmost of the barrier islands along the Atlantic coast of Florida, and lies south of Miami Beach and southeast of… … Wikipedia
Key deer — A male Key Deer on No Name Key in the Florida Keys Conservation status … Wikipedia
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keypoint
Новый англо-русский словарь . 2013 .
Смотреть что такое «keypoint» в других словарях:
Keypoint — Key|point 〈[ki:pɔınt] m.; Gen.: s, Pl.: s; meist Pl.〉 entscheidender Faktor, wichtigster Umstand, Hauptpunkt [Etym.: <engl. key point »wichtigster Punkt«] … Lexikalische Deutsches Wörterbuch
KeyPoint Federal Credit Union — is a credit union headquartered in Baton Rouge, Louisiana and chartered and regulated under the authority of the National Credit Union Administration (NCUA). Like all credit unions, KeyPoint Federal is owned by its membership, governed by a Board … Wikipedia
Digitalsoft Keypoint — Developer(s) Digitalsoft Stable release 3.5.2.4 (3.5) / Jan 01, 2009 Operating system Microsoft Windows Type Presentation … Wikipedia
Scale-invariant feature transform — Feature detection Output of a typical corner detection algorithm … Wikipedia
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KPW — may mean: * North Korean won, the currency of North Korea * Kiwi Pro Wrestling, a professional wrestling promotion based in Wellington, New Zealand * A Keypoint file .KPW is a file extension produced by the Keypoint application from Cambridge… … Wikipedia
Nicoll Highway MRT Station — CC5 Nicoll Highway MRT Station 尼诰大道地铁站 நிக்கல் நெடுஞ்சாலை Stesen MRT Nicoll Highway Rapid transit Platform leve … Wikipedia
Keyline design — For the graphics design term see Keyline. Keyline design is a technique for maximizing beneficial use of water resources of a piece of land, and the Keyline refers to a specific topographic feature linked to water flow. Beyond that however,… … Wikipedia
Sensing Murder — Infobox Television show name = Sensing Murder caption = format = Supernatural Crime Solving Reality camera = picture format = runtime = 90 min creator = David Baldock developer = executive producer = starring = Rebecca Gibney (host) narrated =… … Wikipedia
Presentation program — A slide created by the first presentation graphics company, VCN ExecuVision, in 1982. A presentation program (also called a presentation graphics program) is a computer software package used to display information, normally in the form of a slide … Wikipedia
Building material — For other kinds of building materials, see Hardware, Biology, and Star formation. Concrete and metal rebar used to build a floor Building material is any material which is used for a construction purpose. Many naturally occurring substances, such … Wikipedia
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What are keypoints in image processing?
When using OpenCV for example, algorithms like SIFT or SURF are often used to detect keypoints. My question is what actually are these keypoints?
I understand that they are some kind of «points of interest» in an image. I also know that they are scale invariant and are circular.
Also, I found out that they have orientation but I couldn’t understand what this actually is. Is it an angle but between the radius and something? Can you give some explanation? I think I need what I need first is something simpler and after that it will be easier to understand the papers.
2 Answers 2
Those are some very good questions. Let’s tackle each point one by one:
My question is what actually are these keypoints?
Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. Interest point detection is actually a subset of blob detection, which aims to find interesting regions or spatial areas in an image. The reason why keypoints are special is because no matter how the image changes. whether the image rotates, shrinks/expands, is translated (all of these would be an affine transformation by the way. ) or is subject to distortion (i.e. a projective transformation or homography), you should be able to find the same keypoints in this modified image when comparing with the original image. Here’s an example from a post I wrote a while ago:
The image on the right is a rotated version of the left image. I’ve also only displayed the top 10 matches between the two images. If you take a look at the top 10 matches, these are points that we probably would want to focus on that would allow us to remember what the image was about. We would want to focus on the face of the cameraman as well as the camera, the tripod and some of the interesting textures on the buildings in the background. You see that these same points were found between the two images and these were successfully matched.
Therefore, what you should take away from this is that these are points in the image that are interesting and that they should be found no matter how the image is distorted.
I understand that they are some kind of «points of interest» of an image. I also know that they are scale invariant and I know they are circular.
You are correct. Scale invariant means that no matter how you scale the image, you should still be able to find those points.
Now we are going to venture into the descriptor part. What makes keypoints different between frameworks is the way you describe these keypoints. These are what are known as descriptors. Each keypoint that you detect has an associated descriptor that accompanies it. Some frameworks only do a keypoint detection, while other frameworks are simply a description framework and they don’t detect the points. There are also some that do both — they detect and describe the keypoints. SIFT and SURF are examples of frameworks that both detect and describe the keypoints.
Descriptors are primarily concerned with both the scale and the orientation of the keypoint. The keypoints we’ve nailed that concept down, but we need the descriptor part if it is our purpose to try and match between keypoints in different images. Now, what you mean by «circular». that correlates with the scale that the point was detected at. Take for example this image that is taken from the VLFeat Toolbox tutorial:
You see that any points that are yellow are interest points, but some of these points have a different circle radius. These deal with scale. How interest points work in a general sense is that we decompose the image into multiple scales. We check for interest points at each scale, and we combine all of these interest points together to create the final output. The larger the «circle», the larger the scale was that the point was detected at. Also, there is a line that radiates from the centre of the circle to the edge. This is the orientation of the keypoint, which we will cover next.
Also I found out that they have orientation but I couldn’t understand what actually it is. It is an angle but between the radius and something?
Basically if you want to detect keypoints regardless of scale and orientation, when they talk about orientation of keypoints, what they really mean is that they search a pixel neighbourhood that surrounds the keypoint and figure out how this pixel neighbourhood is oriented or what direction this patch is oriented in. It depends on what descriptor framework you look at, but the general jist is to detect the most dominant orientation of the gradient angles in the patch. This is important for matching so that you can match keypoints together. Take a look at the first figure I have with the two cameramen — one rotated while the other isn’t. If you take a look at some of those points, how do we figure out how one point matches with another? We can easily identify that the top of the cameraman as an interest point matches with the rotated version because we take a look at points that surround the keypoint and see what orientation all of these points are in. and from there, that’s how the orientation is computed.
Usually when we want to detect keypoints, we just take a look at the locations. However, if you want to match keypoints between images, then you definitely need the scale and the orientation to facilitate this.
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Understanding Keypoints and ORB algorithm
A key point is a region of an image which is particularly distinct and identifies a unique feature
Key points are used to identify key regions of an object that are used as the base to later match and identify it in a new image. Image of the same object can be taken in varying conditions like the varying lighting conditions, angle, scale, and background. A good keypoint is one which is invariant to all these conditions.
Applications
Few applications are listed below
- Real-time face matching
- Object tracking
- Image contouring
- Images stitching
- Motion tracking in robotics
Keypoint in detail
A keypoint is calculated by considering an area of certain pixel intensities around it. Keypoints are calculated using various different algorithms, ORB(Oriented FAST and Rotated BRIEF) technique uses the FAST algorithm to calculate the keypoints. FAST stands for Features from Accelerated Segments Test. FAST calculates keypoints by considering pixel brightness around a given area. Consider a pixel area in an image and lets test if a sample pixel p becomes a keypoint.
Considering an area of 16 pixels around the pixel p. In the image, the intensity of pixel p is represented as ip and predefined threshold as h. A pixel is brighter if the brightness of it is higher than ip+h and it is of lower brightness if its brightness falls lower than ip-h and the pixel is considered the same brightness if its brightness is in the range ip+h and ip-h.
FAST decides the point p as keypoint if at least 8 pixels have higher brightness than the pixel p in 16 pixels intensities marked in a circle around it or the same result can be achieved by comparing 4 equidistant pixels on the circle i.e., pixels 1,5,9 and 13. This reduces the time taken to calculate keypoints by 4 times.
Keypoints provide us the locations where the pixel intensities are varying. We get the prominent corners of an object from which we can identify an object from opposed to any other object in an image.
We can see that keypoints are present around the eyes, lips and nose. we can use the keypoint and its surround pixel area to create a numerical feature that can be called a feature descriptor. ORB uses the BRIEF algorithm which stands for Binary Robust Independent Elementary Features. Consider reading OpenCV page for more details
To achieve the scale invariance ORB constructs an image pyramid with different versions of the same image by scaling it to different levels
By calculating keypoints on different scales of the same object, ORB effectively calculates the object features at different scales and ORB assigns an orientation for each image based on the direction of the image gradients. This effectively works when an object is presented with different scales or orientations
Alternate algorithms
- Speeded-up Robust features(SURF)
- Scale Invariant Feature Transform(SIFT)
Using ORB to detect keypoints
We can use the ORB class in the OpenCV library to detect the keypoints and compute the feature descriptors. First keypoints are identified and then it computes binary feature vectors and groups them all in ORB descriptor. we shall consider a sample image shown below to detect the key points
It’s a good idea that we normalize the image using the standard normalization techniques and then convert it to grayscale and pass it to ORB class for keypoint detection. The output obtained is shown below
ORB is a good alternative to the SURF and the SIFT algorithms.
Playing with the ORB
Once you have the keypoints and ORB descriptor try matching it with some test images by scaling, Rotating and increasing the brightness of the image and by adding the random noise.
I observed that ORB is clearly able to recognize the face with all the conditions applied
Further Reference
- OpenCV ORB class reference
- OpenCV tutorial
- ORB implementation notebook ORB.ipynb from Udacity CVND nano degree
- Tutorial on keypoints imageMatching
Images used are referenced from CVND nano degree Udacity
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