Recently, with the development of technology, computer vision research based on the human visual system has been actively conducted. Saliency maps have been used to highlight areas that are visually interesting within the image, but they can suffer from low performance due to external factors, such as an indistinct background or light source. In this study, existing color, brightness, and contrast feature maps are subjected to multiple shape and orientation filters and then connected to a fully connected layer to determine pixel intensities within the image based on location-based weights. The proposed method demonstrates better performance in separating the background from the area of interest in terms of color and brightness in the presence of external elements and noise. Location-based weight normalization is also effective in removing pixels with high intensity that are outside of the image or in non-interest regions. Our proposed method also demonstrates that multi-filter normalization can be processed faster using parallel processing.
|Number of pages||14|
|Journal||KSII Transactions on Internet and Information Systems|
|Publication status||Published - 2021 Jan|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.2020-0-02219, Development of technology for irregularly shaped waste classification based on deep learning).
© 2021 Korean Society for Internet Information. All rights reserved.
- Fully Connected Layer
- Location-Based Normalization
- Multi Shape
- Saliency Map
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications