Polynomial identity ring: Difference between revisions
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{{FeatureDetectionCompVisNavbox}} | |||
The '''principal curvature-based region detector''', also called '''PCBR''' <ref name=Hongli01>{{cite conference | url = http://www.cs.washington.edu/homes/shapiro/cvpr07final.pdf | last1 = Deng | first1 = H. | last2 = Zhang | first2 = W. | last3 = Mortensen | first3 = E. | last4 = Dietterich | first4 = T. | last5 = Shapiro | first5 = L. | title = Principal Curvature-based Region Detector for Object Recognition | conference = IEEE Conference on Computer Vision and Pattern Recognition | year = 2007 }}</ref> is a [[Feature detection|feature detector]] used in the fields of [[computer vision]] and [[image analysis]]. Specifically the '''PCBR''' detector is designed for object recognition applications. | |||
Local region detectors can typically be classified into two categories: '''intensity-based detectors''' and '''structure-based detectors'''. | |||
*'''Intensity-based detectors''' depend on analyzing local differential geometry or intensity patterns to find points or regions that satisfy some uniqueness and stability criteria. These detectors include [[Scale-invariant feature transform|SIFT]], [[Hessian-affine]], [[Harris-Affine]] and [[MSER]] etc. | |||
*'''Structure-based detectors''' depend on structural image features such as lines, edges, curves, etc. to define interest points or regions. These detectors include ''edge-based region'' (EBR) and ''scale-invariant shape features'' (SISF) | |||
From the detection invariance point of view, feature detectors can be divided into fixed scale detectors such as normal ''Harris corner detector'', scale invariant detectors such as [[Scale-invariant feature transform|SIFT]] and affine invariant detectors such as [[Hessian-affine]]. | |||
In | The '''PCBR''' detector is a ''structure-based'' ''affine-invariant'' detector. | ||
==Why a new detector?== | |||
In many object recognition tasks, within-class changes in pose, lighting, color, and texture can cause considerable variation in local intensities. Consequently, local intensity no longer provides a stable detection cue. As such, intensity-based interest operators (e.g., [[Scale-invariant feature transform|SIFT]], [[Harris-Affine]])–and the object recognition systems based on them–often fail to identify discriminative features. An alternative to local intensity cues is to capture semi-local structural cues such as edges and curvilinear shapes. These structural cues tend to be more robust to intensity, color, and pose variations. As such, they provide the basis for a more stable interest operator, which in turn improves object recognition accuracy. ''PCBR'' detector was developed to exploit these more reliable image structural cues. | |||
==Algorithm description== | |||
===Step 1: Curvilinear structure detections=== | |||
As a structure-based detector, ''PCBR'' does not use edges, instead, it uses curvilinear structures, also called [[Ridge detection|ridges]]. Curvilinear structures detection generates a single response for both lines and edges, producing a clearer structural sketch of an image than is usually provided by the gradient magnitude image. The Steger's algorithm <ref name=Steger01>{{cite journal | first = C. | last = Steger | title = An unbiased detector of curvilinear structures | journal = PAMI | volume = 20 | issue = 2 | pages = 113–125 | year = 1998 | id = {{citeseerx|10.1.1.42.2266}} }}</ref> is modified to get the curvilinear images. As only the first step of this algorithm is used which is to calculate the principal curvature images, the principal curvature is adopted as the name of this detector. To get the principal curvature, the Hessian matrix is calculated: | |||
<math> | |||
H(\mathbf{x}) = | |||
\begin{bmatrix} | |||
L_{xx}(\mathbf{x}) & L_{xy}(\mathbf{x})\\ | |||
L_{xy}(\mathbf{x}) & L_{yy}(\mathbf{x})\\ | |||
\end{bmatrix} | |||
</math> | |||
where <math>L_{aa}(\mathbf{x})</math> is second partial derivative of the image evaluated at point x in the <math>a</math> direction and <math>L_{ab}(\mathbf{x})</math> is the mixed partial second derivative of the image evaluated at point x in the <math>a</math> and <math>b</math> directions. The maximum and minimum eigenvalues of this matrix form two images which correspond to white lines on black background and black lines on white background. | |||
===Step 2: Seeking characteristics and robustness in scale space=== | |||
To make this detector scale invariance and improve the detection robustness, the process of David Lowe's <ref name=DavidLowe01>{{cite journal | url = http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf | first = D. G. | last = Lowe | title = Distinctive image features from scale-invariant keypoints | journal = IJCV | volume = 60 | issue = 2 | pages = 91–110 | year = 2004 }}</ref> [[Scale-invariant feature transform|SIFT]] detector is simulated to detect principal curvilinear structure in scale space. Local maximum images of principal curvature values are used to define regions. | |||
===Step 3: Defining regions by enhanced watershed algorithms=== | |||
The principal curvature images are cleaned by a morphological closing and eigenvector-flow guided hysteresis thresholding. Then traditional watershed algorithm is applied on images to acquire regions. | |||
===Step 4: Stable region selections=== | |||
Similar to the process of selecting stable regions via thresholding in [[MSER]],<ref>{{cite book | chapterurl = http://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/matas_bmvc2002.pdf | first1 = J. | last1 = Matas | first2 = O. | last2 = Chum | first3 = M. | last3 = Urban | first4 = T. | last4 = Pajdla | chapter = Robust wide baseline stereo from maximally stable extremal regions | title = BMVC | pages = 384–393 | year = 2002 }}</ref> stable regions are selected across local scale changes. To achieve this, overlap error is computed across each triplet of consecutive scales. If the region overlap error is greater than 90%, only one region is kept. If the error is greater than 70% and less than 90%, all regions are kept. If overlap is less than 70%, discard these regions. These numbers are determined by the analysis of sensitivity of the [[Scale-invariant feature transform|SIFT]] descriptor. | |||
==How does the PCBR differ?== | |||
*It is a structure-based detector. | |||
*It is designed to handle within-class variance. | |||
*It is used when local intensity is not stable. | |||
*It detects a semi-local characteristic region. | |||
==Software Packages== | |||
Binary code of an implementation of ''PCBR'' can be downloaded from Tom Dietterich's webpage.<ref>{{cite web | url = http://web.engr.oregonstate.edu/~tgd/software/pcbrRun.zip | format = ZIP file | title = PCBR | first = Tom | last = Dietterich }}</ref> | |||
==See also== | |||
*[[Scale-invariant feature transform|SIFT]] | |||
*[[MSER]] | |||
*[[Hessian-Affine]] | |||
*[[Harris-Affine]] | |||
*[[Scale space]] | |||
*[[Corner detection]] | |||
*[[Blob detection]] | |||
*[[Interest point detection]] | |||
*[[Computer vision]] | |||
==References== | |||
{{reflist}} | |||
[[Category:Feature detection]] | |||
Revision as of 03:32, 19 August 2013
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Template:FeatureDetectionCompVisNavbox
The principal curvature-based region detector, also called PCBR [1] is a feature detector used in the fields of computer vision and image analysis. Specifically the PCBR detector is designed for object recognition applications.
Local region detectors can typically be classified into two categories: intensity-based detectors and structure-based detectors.
- Intensity-based detectors depend on analyzing local differential geometry or intensity patterns to find points or regions that satisfy some uniqueness and stability criteria. These detectors include SIFT, Hessian-affine, Harris-Affine and MSER etc.
- Structure-based detectors depend on structural image features such as lines, edges, curves, etc. to define interest points or regions. These detectors include edge-based region (EBR) and scale-invariant shape features (SISF)
From the detection invariance point of view, feature detectors can be divided into fixed scale detectors such as normal Harris corner detector, scale invariant detectors such as SIFT and affine invariant detectors such as Hessian-affine.
The PCBR detector is a structure-based affine-invariant detector.
Why a new detector?
In many object recognition tasks, within-class changes in pose, lighting, color, and texture can cause considerable variation in local intensities. Consequently, local intensity no longer provides a stable detection cue. As such, intensity-based interest operators (e.g., SIFT, Harris-Affine)–and the object recognition systems based on them–often fail to identify discriminative features. An alternative to local intensity cues is to capture semi-local structural cues such as edges and curvilinear shapes. These structural cues tend to be more robust to intensity, color, and pose variations. As such, they provide the basis for a more stable interest operator, which in turn improves object recognition accuracy. PCBR detector was developed to exploit these more reliable image structural cues.
Algorithm description
Step 1: Curvilinear structure detections
As a structure-based detector, PCBR does not use edges, instead, it uses curvilinear structures, also called ridges. Curvilinear structures detection generates a single response for both lines and edges, producing a clearer structural sketch of an image than is usually provided by the gradient magnitude image. The Steger's algorithm [2] is modified to get the curvilinear images. As only the first step of this algorithm is used which is to calculate the principal curvature images, the principal curvature is adopted as the name of this detector. To get the principal curvature, the Hessian matrix is calculated:
where is second partial derivative of the image evaluated at point x in the direction and is the mixed partial second derivative of the image evaluated at point x in the and directions. The maximum and minimum eigenvalues of this matrix form two images which correspond to white lines on black background and black lines on white background.
Step 2: Seeking characteristics and robustness in scale space
To make this detector scale invariance and improve the detection robustness, the process of David Lowe's [3] SIFT detector is simulated to detect principal curvilinear structure in scale space. Local maximum images of principal curvature values are used to define regions.
Step 3: Defining regions by enhanced watershed algorithms
The principal curvature images are cleaned by a morphological closing and eigenvector-flow guided hysteresis thresholding. Then traditional watershed algorithm is applied on images to acquire regions.
Step 4: Stable region selections
Similar to the process of selecting stable regions via thresholding in MSER,[4] stable regions are selected across local scale changes. To achieve this, overlap error is computed across each triplet of consecutive scales. If the region overlap error is greater than 90%, only one region is kept. If the error is greater than 70% and less than 90%, all regions are kept. If overlap is less than 70%, discard these regions. These numbers are determined by the analysis of sensitivity of the SIFT descriptor.
How does the PCBR differ?
- It is a structure-based detector.
- It is designed to handle within-class variance.
- It is used when local intensity is not stable.
- It detects a semi-local characteristic region.
Software Packages
Binary code of an implementation of PCBR can be downloaded from Tom Dietterich's webpage.[5]
See also
- SIFT
- MSER
- Hessian-Affine
- Harris-Affine
- Scale space
- Corner detection
- Blob detection
- Interest point detection
- Computer vision
References
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The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more
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In its statement, the singapore property listing - website link, government claimed that the majority citizens buying their first residence won't be hurt by the new measures. Some concessions can even be prolonged to chose teams of consumers, similar to married couples with a minimum of one Singaporean partner who are purchasing their second property so long as they intend to promote their first residential property. Lower the LTV limit on housing loans granted by monetary establishments regulated by MAS from 70% to 60% for property purchasers who are individuals with a number of outstanding housing loans on the time of the brand new housing purchase. Singapore Property Measures - 30 August 2010 The most popular seek for the number of bedrooms in Singapore is 4, followed by 2 and three. Lush Acres EC @ Sengkang
Discover out more about real estate funding in the area, together with info on international funding incentives and property possession. Many Singaporeans have been investing in property across the causeway in recent years, attracted by comparatively low prices. However, those who need to exit their investments quickly are likely to face significant challenges when trying to sell their property – and could finally be stuck with a property they can't sell. Career improvement programmes, in-house valuation, auctions and administrative help, venture advertising and marketing, skilled talks and traisning are continuously planned for the sales associates to help them obtain better outcomes for his or her shoppers while at Knight Frank Singapore. No change Present Rules
Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.
A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running
The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more
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