Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

In a planar free-hand drawing of an ellipse, the speed of movement is proportional to the −1/3 power of the local curvature, which is widely thought to hold for general curved shapes. We investigated this phenomenon for general curved hand movements by analyzing an optimal control model that maximizes a smoothness cost and exhibits the −1/3 power for ellipses. For the analysis, we introduced a new representation for curved movements based on a moving reference frame and a dimensionless angle coordinate that revealed scale-invariant features of curved movements. The analysis confirmed the power law for drawing ellipses but also predicted a spectrum of power laws with exponents ranging between 0 and −2/3 for simple movements that can be characterized by a single angular frequency. Moreover, it predicted mixtures of power laws for more complex, multifrequency movements that were confirmed with human drawing experiments. The speed profiles of arbitrary doodling movements that exhibit broadband curvature profiles were accurately predicted as well. These findings have implications for motor planning and predict that movements only depend on one radian of angle coordinate in the past and only need to be planned one radian ahead.

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Analysis of the Repeatability of SIFT and SURF Descriptors Techniques for Underwater Image Preprocessing

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-v4-i3-004 ◽

2021 ◽

pp. 24-31

Author(s):

Shubhangi N. Ghate ◽

Dr. Mangesh Nikose

Keyword(s):

Prior Knowledge ◽

State Of The Art ◽

Rotation Invariant ◽

Image Preprocessing ◽

Retrieval Time ◽

Retrieval Accuracy ◽

Invariant Features ◽

Underwater Image ◽

Geometrical Constraints ◽

Time Required

To improve the repeatability of SIFT and SURF descriptors, we conducted research to find two methods: first, a method for pre-processing underwater images that does not require prior knowledge of the scene, and second, a method for computing distances that is less expensive in terms of execution time for finding corresponding points. SIFTs (Scale and Rotation Invariant Features) are new features that have been developed. SIFTs (Scale and Rotation Invariant Features) are newly developed features that are based on geometrical constraints between pairs of nearby points around a key point. SIFT is contrasted with cutting-edge local features. SIFT outperforms the state-of-the-art in terms of retrieval time and retrieval accuracy. We have discussed the time required to extract key point features of SIFT and SURF Descriptor.

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Image Detection System Using Image Processing

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36190 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 5356-5362

Author(s):

Mohini Gawande

Keyword(s):

Image Processing ◽

Computer Vision ◽

Detection System ◽

Estimation Method ◽

Scale Invariant ◽

Matrix Estimation ◽

Speeded Up Robust Features ◽

Invariant Features ◽

Marketing Analysis ◽

New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efﬁciently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

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