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Feature detection
Edge detection
Canny
Deriche
Differential
Sobel
Prewitt
Roberts cross
Corner detection
Harris operator
Shi and Tomasi
Level curve curvature
Hessian feature strength measures
SUSAN
FAST
Blob detection
Laplacian of Gaussian (LoG)
Difference of Gaussians (DoG)
Determinant of Hessian (DoH)
Maximally stable extremal regions
PCBR
Ridge detection
Hough transform
Hough transform
Generalized Hough transform
Structure tensor
Structure tensor
Generalized structure tensor
Affine invariant feature detection
Affine shape adaptation
Harris affine
Hessian affine
Feature description
SIFT
SURF
GLOH
HOG
Scale space
Scale-space axioms
Implementation details
Pyramids
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In computer vision, blob detection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is by using convolution.
Given some property of interest expressed as a function of position on the image, there are two main classes of blob detectors: (i) differential methods, which are based on derivatives of the function with respect to position, and (ii) methods based on local extrema, which are based on finding the local maxima and minima of the function. With the more recent terminology used in the field, these detectors can also be referred to as interest point operators, or alternatively interest region operators (see also interest point detection and corner detection).
There are several motivations for studying and developing blob detectors. One main reason is to provide complementary information about regions, which is not obtained from edge detectors or corner detectors. In early work in the area, blob detection was used to obtain regions of interest for further processing. These regions could signal the presence of objects or parts of objects in the image domain with application to object recognition and/or object tracking. In other domains, such as histogram analysis, blob descriptors can also be used for peak detection with application to segmentation. Another common use of blob descriptors is as main primitives for texture analysis and texture recognition. In more recent work, blob descriptors have found increasingly popular use as interest points for wide baseline stereo matching and to signal the presence of informative image features for appearance-based object recognition based on local image statistics. There is also the related notion of ridge detection to signal the presence of elongated objects.
In computer vision, blobdetection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, compared...
algorithm Treatment of the difference of Gaussians approach in blobdetection. Blobdetection Gaussian pyramid Scale space Scale-invariant feature transform...
The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by...
Edge detection includes a variety of mathematical methods that aim at identifying edges, defined as curves in a digital image at which the image brightness...
interest points such as corners, blobs or points. More complex features may be related to texture, shape, or motion. Detection/segmentation – At some point...
vision, maximally stable extremal regions (MSER) are used as a method of blobdetection in images. This technique was proposed by Matas et al. to find correspondences...
robust features). In SURF, the DOG is replaced with a Hessian matrix-based blob detector. Also, instead of evaluating the gradient histograms, SURF computes...
De Blob is a puzzle-platform game developed by Blue Tongue Entertainment and published by THQ for the Wii. Players explore and liberate an alien city from...
uses a deep CNN model with branches for joint detection and feature learning to discover the detection mask and exact discriminative feature without background...
Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos...
health-care, retail, automotive, transport, home automation, flame and smoke detection, safety, and security. The algorithms can be implemented as software on...
Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences...
Gaussian filter. Then edges (mid) are found from it using canny edge detection. After this, all the edge points are used by the Circle Hough Transform...
image processing techniques for lens distortion removal, etc. Feature detection: define interest operators, and match features across frames and construct...
color, and isolate features using color. Blobdetection and extraction: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in...
used in image processing and computer vision, particularly within edge detection algorithms where it creates an image emphasising edges. It is named after...
method of face detection is using skin tone, edge detection, face shape, and feature of a face (like eyes, mouth, etc.) to achieve face detection. The skin...
camera images. This step can use feature detection methods like corner detection, blobdetection, edge detection or thresholding, and other image processing...
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