Principal Curvature-Based Region Detector
Encyclopedia
The principal curvature-based region detector, also called PCBR 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.
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.
, 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.
where
is second partial derivative in the 
direction and 
is the mixed partial second derivative 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.
Feature detection
In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not...
used in the fields of computer vision
Computer vision
Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...
and image analysis
Image analysis
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques...
. 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 SIFTScale-invariant feature transformScale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
, 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
Scale-invariant feature transform
Scale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
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., SIFTScale-invariant feature transform
Scale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
, 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 ridgesRidge detectionThe ridges of a smooth function of two variables is a set of curves whose points are, in one or more ways to be made precise below, local maxima of the function in at least one dimension. For a function of N variables, its ridges are a set of curves whose points are local maxima in N-1 dimensions...
. 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 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 in the direction and is the mixed partial second derivative 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 SIFTScale-invariant feature transformScale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
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, 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 SIFTScale-invariant feature transformScale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
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.http://web.engr.oregonstate.edu/~tgd/software/pcbrRun.zipSee also
- SIFTScale-invariant feature transformScale-invariant feature transform is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999....
- MSER
- Hessian-Affine
- Harris-Affine
- Scale-space
- Corner detectionCorner detectionCorner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3D modelling and object...
- Blob detectionBlob detectionIn the area of computer vision, blob detection refers to visual modules that are aimed at detecting points and/or regions in the image that differ in properties like brightness or color compared to the surrounding...
- Interest point detectionInterest point detectionInterest point detection is a recent terminology in computer vision that refers to the detection of interest points for subsequent processing...
- Computer visionComputer visionComputer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...