Abstract
Electrical substation online monitoring in computer vision technology is based on image processing algorithm to perform visual analysis. This paper presents classification of ceramicand glass insulators through Bag of Visual Words and detection of these insulators by Point Feature Matching. The training image datasets are used for categorization by forming a visual vocabulary while a new unlabeled image from testing image dataset is classify using nearest neighbor classification method for features descriptor. For detection we use Speeded up Robust Features for detecting position of insulator present in cluttered scene image. Matching process is done between test and reference image and decision is made based on similar features. We conducted experiment on insulators to verify the superiority of our proposed method. The proposed method can be used in security, surveillance and inspection system
Highlights
Over the years, Image processing and Machine learning techniques gained popularity for the visual analysis of images [1]
Local feature detecting algorithmsuch as Speed Up Robust Feature (SURF) [12]and Scale Invariant Feature Transform (SIFT)[13] are well known descriptors
Datasets In this paper ceramic and Glass insulators are used for classification
Summary
Image processing and Machine learning techniques gained popularity for the visual analysis of images [1]. Local point descriptors and key points are used for imagery data classification and image retrieval [4, 5, 6, 7, 8]. For the classification of ceramic and glass insulators the algorithm used is knowns as (BOVW) Bag of Visual Words.[9, 10]. Image can be analyzed based on their color, texture, shape and edges etc. Jacob Vetha Raj[11] gives brief idea about image retrieval process using color, texture, shape and any information extracted for query and database images. Local feature detecting algorithmsuch as Speed Up Robust Feature (SURF) [12]and Scale Invariant Feature Transform (SIFT)[13] are well known descriptors. BOVW with SURF features is used for classification
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