Results 251 to 260 of about 49,054 (309)

A Multi-Modal Few-Shot Learning Framework for Foreign Object Segmentation in GIS Inspection. [PDF]

Sensors (Basel)
Liu J, Lang Y, Tang J, Li Q, Yang S, Dian S.   +5 more
europepmc   +1 more source

Integrating Multi-Source and Multi-Temporal UAV Observations to Improve Wheat Yield Prediction Using Machine Learning. [PDF]

Plants (Basel)
Chen C, Liu J, Deng Y, Guo R, Yao W, Yang T, Zhang W, Liu T, Jin X, Xiong W, Li D.   +10 more
europepmc   +1 more source

Region guided mask R-CNN with Haralick ResNet fusion for accurate coronary artery disease detection in computed tomography angiography images. [PDF]

Sci Rep
Revathi G, Mathew OC.
europepmc   +1 more source

An Optimal Deep Hybrid Framework with Selective Kernel U-Net for Skin Lesion Detection and Classification. [PDF]

Bioengineering (Basel)
Gulmirzaeva G, Hudec R, Akbaraliev B, Samandarov B.   +3 more
europepmc   +1 more source

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Texture feature extraction

Pattern Recognition Letters, 1987
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He
exaly   +2 more sources

Decorrelation Methods of Texture Feature Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980
This paper presents the development and evaluation of a visual texture feature extraction method based on a stochastic field model of texture. Results of recent visual texture discrimination experiments are reviewed in order to establish necessary and sufficient conditions for texture features that are in agreement with human discrimination.
Olivier D. Faugeras, William K. Pratt
exaly   +3 more sources

Feature extraction for texture classification

Pattern Recognition, 1980
Abstract We address the problem of texture classification. Random walks are simulated for plane domains A bounded by absorbing boundaries Γ, and the absorption distributions are estimated. Measurements derived from the above distributions are the features used for texture classification.
Harry Wechsler
exaly   +2 more sources

An optimum feature extraction method for texture classification

Expert Systems With Applications, 2009
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced.
Engin Avci, Abdülkadir Şengür, Davut Hanbay   +2 more
exaly   +2 more sources

Role of correlated noise in textural features extraction

Physica Medica, 2021
Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the ...
Carlos, Huerga, Ana, Morcillo, Luis, Alejo, Alberto, Marín, Alba, Obesso, Daniela, Travaglio, Jose, Bayón, David, Rodriguez, Monica, Coronado   +8 more
openaire   +2 more sources