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The power of multiple scales: How to automatically identify blobs of different sizes?
In computer vision, blob detection techniques aim to identify regions in digital images that differ in properties (such as brightness or color) from surrounding areas. In general, a blob is an image region where some properties are approximately constant; this means that all points in the blob are similar to each other in some sense. This article will explore various techniques for blob detection, especially how to automatically identify blobs of different sizes through a multi-scale approach.
Why focus on blob detection technology?
One of the main reasons for studying and developing blob detectors is to provide complementary information that cannot be obtained by edge detectors or corner detectors. Early blob detection work is used to obtain regions of interest for further processing. These regions can indicate the presence of objects or object parts in the image domain, and their applications include object recognition and target tracking. In other areas such as histogram analysis, blob descriptors can also be used for peak detection, which is crucial for segmentation. Another common use of blob descriptors is as a basis for texture analysis and texture recognition.
Recent studies have shown that blob descriptors play an increasingly important role in appearance-based object recognition, which is based on local image statistics.
Laplace-Gaussian Detection
Laplacian of Gaussian (LoG) is one of the earliest and most common blob detection methods. Given an input image, the image is first convolved with a Gaussian kernel to obtain a scale-space representation. Next, the result after applying the Laplacian operator is calculated, which generally gives a strong positive response for active low-brightness blobs and a strong negative response for high-brightness blobs. However, when applying this operator at a single scale, the response is strongly affected by the relationship between the size of the blob structure in the image domain and the size of the Gaussian kernel used.
Therefore, automatically capturing blobs of varying (unknown) sizes in the image domain requires a multi-scale approach.
Application of Difference of Gaussian Method
The Difference of Gaussian (DoG) based method is used to extract blobs from the edited image in scale space. It can approximate the performance of the Laplacian operator and is widely discussed in most computer vision literature. The feature of this method is that it can be calculated by the difference between two Gaussian smoothed images, which makes blob detection more effective.
Determinant of the Hessian Matrix
By considering the scale-normalized Hessian matrix determinant, we are able to obtain a new way of blob detection. This method can then be used for automatic scale selection and also performs well in response to saddle points. In addition, the proposed method performs better than the traditional Laplacian operator in scale selection under non-Euclidean affine transformations.
Hybrid Detection Method
In the study of blob detection, some people have proposed a hybrid operator of Laplace and Hessian determinant. This method combines the advantages of space selection and scale selection and has been applied to many fields such as image matching, object recognition and texture analysis.
Affine Adapted Blob Detector
Considering that the input image may be affected by perspective distortion, developing a blob detector that is invariant to affine transformations has naturally become a research hotspot. The core of this method is to perform affine shape adjustment on the blob descriptor to obtain blobs in more complex environments. These affine adapted versions of the Laplace method, DoG method and Hessian determinant provide more stable detection results.
Evolution of spatiotemporal blob detectors
Importantly, the operators of the Hessian determinant have been extended to the space-time domain. An advanced scale-normalized differential expression provides new possibilities for blob detection, making blob identification in dynamic processes more common.
Among these advances, we can see the transformative impact of blob detection technology on computer vision. Today's blob detection not only excels in static images, but also becomes increasingly important in videos and capturing dynamic processes with the emergence of spatiotemporal analysis. However, there are still many challenges to be solved in this field in the future. How to better face these challenges may be our next important topic?
