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A New Texture Spectrum Based on Parallel Encoded Texture Unit and Its Application on Image Classification: A
José Trinidad Guillen Bonilla1, Nancy Elizabeth Franco Rodríguez2, Héctor Guillen Bonilla3
1Departamento de Electro-Fotónica, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd-M. García Barragán 1421, Guadalajara 44430, Jalisco, Mexico.
Sensors (Basel, Switzerland)
|October 28, 2023
Summary
A new Texture Spectrum based on Parallel Encoded Texture Unit (TS_PETU) offers efficient and fast texture classification for industrial quality control. This method excels with binary images and larger observational windows, demonstrating significant industrial application potential.
Area of Science:
- Computer Vision
- Image Processing
- Machine Learning
Background:
- Efficient and fast texture classification is crucial for industrial quality control.
- Existing methods may not meet the speed and efficiency demands of real-time industrial processes.
- The need for robust texture analysis in diverse environments, from controlled industrial settings to natural scenes.
Purpose of the Study:
- To propose a novel texture spectrum, the Texture Spectrum based on the Parallel Encoded Texture Unit (TS_PETU), for efficient and fast texture classification.
- To evaluate the performance of TS_PETU using two distinct image databases: industrial tiles and tree stems.
- To demonstrate the applicability of TS_PETU in multi-class classification tasks for industrial quality control.
Main Methods:
- Development of a new texture unit coding in parallel.
- Introduction of the TS_PETU as a characteristic vector for multi-class classification.
- Utilizing observation windows larger than 3x3 to enhance classification accuracy.
- Testing the method on industrial tile images (Interceramic®®) and natural tree stem images.
Main Results:
- The TS_PETU classifier demonstrated both efficiency and speed, meeting key industrial requirements.
- High classification efficiency was achieved, particularly with larger observational windows.
- The compute time for TS_PETU was reduced through the application of parallel coding concepts.
- Successful classification of two diverse image databases, indicating versatility.
Conclusions:
- The proposed TS_PETU technique satisfies the critical requirements of efficiency and speed for industrial texture classification.
- The method's effectiveness is enhanced by using larger observational windows and parallel coding.
- TS_PETU shows significant promise for industrial applications, especially in quality control, as evidenced by its high efficiency in classifying Interceramic®® tiles.
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