Recognition Algorithms Based on Radial Functions

Mirzaev, N. M.; Saliev, Ergash

doi:10.1109/rpc.2018.8482213

Cited by 11 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They are defined as some digital characteristics of the considered part (fragment) of the image; for example, texture marks can be obtained based on the first-order statistical characteristics of the part of the image. They are invariant with respect to pixel shifts [21,24]. As diagnostic features of an image, in addition to features that characterize the texture, entropy, autocorrelation, moments, etc.…”

Section: Proposed Solution Methodsmentioning

confidence: 99%

“…It should be noted that each 𝔏 𝑖 leaf image corresponds to a set of pixels specified in the form of the numerical matrix Τ [21,24,25]: The main task is to develop the recognition algorithm А that determines the value of the predicate 𝑃 𝑗 (𝔏 𝑖 ) according to initial data (2): 𝐴(𝐸 0 , 𝔏 𝑖 ) = 𝛽 ̃(𝔏 𝑖 ), 𝛽 ̃(𝔏 𝑖 ) = (𝛽 𝑖1 , … , 𝛽 𝑖𝑗 , … , 𝛽 𝑖𝑘 ), 𝛽 𝑖𝑗 = 𝑃 𝑗 (𝔏 𝑖 ), 𝛽 𝑖𝑗 ∈ {0,1,2}.…”

Section: Statement Of the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Algorithms for plant disease diagnostics by leaf image

Mirzaev,

Radjabov,

Nurmukhamedov

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

This article discusses the task of detecting diseases of cultivated plants. When determining the phytosanitary status of cultivated plants, images of their leaves are considered as initial data. To solve the problem under consideration, a model of diagnostic algorithms based on two-dimensional threshold functions is proposed. The main idea of the proposed algorithms is to form a set of preferred diagnostic features and make decisions aimed at making a diagnosis based on a comparison of these features. The classification stages of the diagnostic algorithm model are presented. An assessment of the applicability of the proposed model is demonstrated using the example of solving the problem of diagnosing wheat diseases by leaf images. Keywords: diagnostic algorithms, basic image slices, diagnostic features, preferred features, calculation of the overall score.

show abstract

Section: Proposed Solution Methodsmentioning

confidence: 99%

Section: Statement Of the Problemmentioning

confidence: 99%

Algorithms for plant disease diagnostics by leaf image

Mirzaev,

Radjabov,

Nurmukhamedov

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

“…Boʻsagʻaviy funksiyalarni qurishga asoslangan ekstensional tipdagi tanib olish operatorlarining birinchi bosqichda qaralayotgan belgilar toʻplami elementlarini "oʻzaro bogʻliq boʻlmagan" qism-toʻplamlari aniqlanadi. Mazkur bosqichning asosiy gʻoyasi quyidagidan iborat [3,31,32]. Agar koʻrib chiqilayotgan belgilarning qism-toʻplami birbiriga yetarlicha yaqin boʻlsa, ular bir qism-toʻplamga birlashtiriladi.…”

Section: Taklif Qilingan Yondashuvunclassified

Bo'sag'aviy funksiyalarni qurishga asoslangan ekstensional tipdagi tanib olish operatorlari

Mirzayev

2024

Международный журнал теоретических и прикладных вопросов цифров

View full text Add to dashboard Cite

Maqolada belgilar fazosining oʻlchami katta ekanligini hisobga olgan holda tanib olish operatorlari modelini qurish masalasi koʻrib chiqilgan. Ushbu masalani yechish uchun lokal qarorlarni qabul qilishga asoslangan original yondashuv taklif qilingan. Taklif etilgan yondashuv asosida lokal boʻsagʻaviy funksiyalarni qurishga asoslangan ekstensional turdagi tanib olish operatorlarining modeli ishlab chiqilgan. Taklif etilayotgan tanib olish operatorlarining parametrik koʻrinishi keltirilgan. Ishlab chiqilgan algoritmlardan foydalanib hal qilinadigan amaliy masalalar koʻlamini kengaytirish va katta oʻlchamli fazoda berilgan obyektlarni tanib olish aniqligini oshirish imkonini beradi.

show abstract