A local feature vector for an adaptive hybrid fingerprint matcher

2021 ◽

Vol 11 (6) ◽

pp. 4972

Author(s):

Samer Kais Jameel ◽

Sezgin Aydin ◽

Nebras H. Ghaeb

Keyword(s):

Feature Vector ◽

Outer Part ◽

Local Binary Patterns ◽

Local Information ◽

Machine Learning Algorithms ◽

Local Feature ◽

Human Vision ◽

Human Cornea ◽

Image Center ◽

Information Pattern

<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may aﬀect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>

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Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis.

10.54294/nbrfiw ◽

2008 ◽

Author(s):

Dirk-jan Kroon ◽

Erik van Oort ◽

Kees Slump

Keyword(s):

Multiple Sclerosis ◽

Feature Vector ◽

Principal Component ◽

Bias Field ◽

Magnetic Resonance Images ◽

Local Feature ◽

Training Set ◽

Edge Preserving ◽

Vector Method ◽

Components Analysis

This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training set from the MICCAI 2008 challenge on MS, and 24 test datasets. Our local feature vector method contains neighbourhood voxel intensities, histogram and MS probability atlas information. Principal Component Analysis(PCA) with log-likelihood ratio is used to classify each voxel. MRI suffers from intensity inhomogenities. We try to correct this ‘’bias field’’ with 3 methods: a genetic algorithm, edge preserving filtering and atlas based correction. A large observer variability exist between expert classifications, but the similarity scores between model and expert classifications are often lower. Our model gives the best classification results with raw data, because bias correction gives artifacts at the edges and flatten large MS lesions.

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Proposal of local feature vector focusing on the differences among neighboring ROI’s

2018 International Workshop on Advanced Image Technology (IWAIT) ◽

10.1109/iwait.2018.8369637 ◽

2018 ◽

Cited By ~ 1

Author(s):

Koki Muto ◽

Takuma Matsubara ◽

Hiroyasu Koshimizu

Keyword(s):

Feature Vector ◽

Local Feature

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An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

Mathematical Problems in Engineering ◽

10.1155/2018/1917421 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Luan Xidao ◽

Xie Yuxiang ◽

Zhang Lili ◽

Zhang Xin ◽

Li Chen ◽

...

Keyword(s):

Sparse Coding ◽

Feature Detection ◽

Feature Vector ◽

Detection Efficiency ◽

Detection Algorithm ◽

Local Feature ◽

Image Similarity ◽

Sift Feature ◽

Similarity Detection ◽

Overcomplete Dictionary

Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. The image similarity detection result is obtained by comparing the sparse coefficients. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection.

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A Simple Deconvolutional Mechanism for Point Clouds and Sparse Unordered Data (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7217 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13889-13890

Author(s):

Thomas Paniagua ◽

John Lagergren ◽

Greg Foderaro

Keyword(s):

Feature Vector ◽

Point Clouds ◽

Local Feature ◽

Unstructured Data ◽

Feature Vectors ◽

Convolution Operation ◽

Input Feature ◽

Feature Information ◽

Irregular Data

This paper presents a novel deconvolution mechanism, called the Sparse Deconvolution, that generalizes the classical transpose convolution operation to sparse unstructured domains, enabling the fast and accurate generation and upsampling of point clouds and other irregular data. Specifically, the approach uses deconvolutional kernels, which each map an input feature vector and set of trainable scalar weights to the feature vectors of multiple child output elements. Unlike previous approaches, the Sparse Deconvolution does not require any voxelization or structured formulation of data, it is scalable to a large number of elements, and it is capable of utilizing local feature information. As a result, these capabilities allow for the practical generation of unstructured data in unsupervised settings. Preliminary experiments are performed here, where Sparse Deconvolution layers are used as a generator within an autoencoder trained on the 3D MNIST dataset.

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