Face Recognition using Symmetric Local Graph Structure

Ayoob, Movina R.; Mathusoothana, R.; Kumar, Sandeep

doi:10.17485/ijst/2015/v8i24/80876

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…Figure 4 shows a sample of collected data from Mahdha farms. Then, the pictures divided into parts and each part contains a matrix with a fixed size, a binary value takes features from the neighbor pixel and calculated decimal value for each matric (Ayoob & Kumar, 2015). The local graph structure is a relation between X and Y pixel in each image with another neighbor (Bashier, et al, 2013…”

Section: Research Experimental Setupmentioning

confidence: 99%

Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

Abusham

2021

airdj

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Detecting plant diseases using the traditional method such as the naked eye can sometimes lead to incorrect identification and classification of the diseases. Consequently, this traditional method can strongly contribute to the losses of the crop. Image processing techniques have been used as an approach to detect and classify plant diseases. This study aims to focus on the diseases affecting the leaves of al-berseem and how to use image processing techniques to detect al-berseem diseases. Early detection of diseases important for finding appropriate treatment quickly and avoid economic losses. Detect the plant disease is based on the symptoms and signs that appear on the leaves. The detection steps include image preprocessing, segmentation, and identification. The image noise is removed in the preprocessing stage by using the MATLAB features energy, mean, homogeneity, and others. The k-mean-clustering is used to detect the affected area in leaves. Finally, KNN will be used to recognize unhealthy leaves and determines disease types (fungal diseases, pest diseases (shall), leaf minor (red spider), and deficiency of nutrient (yellow leaf)); these four types of diseases will detect in this thesis. Identification is the last step in which the disease will identify and classified.

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Section: Research Experimental Setupmentioning

confidence: 99%

Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

Abusham

2021

airdj

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“…So, LGS is extracting features in asymmetric way. Improvement over LGS is made as Symmetric local graph structure (SLGS) [2], [5], which is more symmetric than LGS, taking into account equal number of reference pixels in both the left and right side of interest source pixel to extract features. For weighted value calculation of each interest source pixel, LGS takes reference of 5 neighborhood pixels from a 3×4 adjacent neighbor area of source pixel, whereas SLGS operates on a 3×5 adjacent neighbor area of the source pixel taking reference of 6 neighborhood pixels to form an 8-bit binary sequence, which is converted to decimal weighted value for the corresponding interest source pixel.…”

Section: Literature Surveymentioning

confidence: 99%

Fingerprint Matching using Graph Structure based Symmetric Ternary Pattern

Dev¹,

Panigrahi²,

Bose³

et al. 2021

IJCDS

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Fingerprint matching, one of the sophisticated biometric authentication techniques, is popular for its easy implementation, persistent nature of the fingerprint and non-similarity nature of two fingerprints. Uniqueness of fingerprint is characterized by distinctive features present in fingerprint image. This paper presents a novel relational descriptor based fingerprint matching process using pattern matching concept called Multi-Variant Symmetric Ternary Pattern (MVSTP). Orientation and illumination invariant local descriptor MVSTP extract distinct features from fingerprint image by referring non-overlapping neighbor pixels in symmetric way with respect to source pixel positioned at the center of 5×5 pixel area. After feature extraction from query fingerprint image and stored fingerprint images in the database, features are compared to find similarity match. MVSTP aims to increase fingerprint matching accuracy in contrast with other processes by addressing challenges related to fingerprint pattern's appearance variation with slight orientation and the variations present in image properties. The computational proficiency of the proposed fingerprint matching process is tested on FVC 2004 database and local database of fingerprint images with higher note of matching accuracy, manifesting its intensity in the process.

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