Abstract
Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new, simple, and biologically inspired pre processing technique by using the data‐driven mechanism of visual attention. In this part, the responses of Retinal Ganglion Cells (RGCs) are simulated. After obtaining these responses, an efficient threshold is selected. Then, the points of the raw image with the most information are extracted according to it. Then, the new images with these points are created, and finally, by combining these images with entropy coefficients, the most salient object is located. After extracting appropriate features, the classifier categorizes the initial image into one of the predefined object categories. Our system was evaluated on the Caltech‐101 dataset. Experimental results demonstrate the efficacy and effectiveness of this novel method of preprocessing.
Highlights
One of the challenges in the field of artificial intelligence is object recognition. e objective of this process is to classify an object into one of the predefined categories.ere are various challenges in this field, such as cluttered and noisy background or objects under different illumination and contrast environments
Parasol cells are approximately 80% of the total retinal ganglion cells that get information from several rods and cones. ey have a fast transfer rate and can react to low-contrast stimuli. ey have uncomplicated center-surround receptive fields, where the center can be either OFF or ON while the surrounding is in the opposite mode. ree main steps in modelling these cells are as follows: Step 1: initializing basic parameters according to Table 1 [35]
Our proposed method is compared with other state-of-theart algorithms in both saliency detection and object recognition
Summary
One of the challenges in the field of artificial intelligence is object recognition. e objective of this process is to classify an object into one of the predefined categories.ere are various challenges in this field, such as cluttered and noisy background or objects under different illumination and contrast environments. One of the challenges in the field of artificial intelligence is object recognition. Researchers believe that the recognition system is closer to the human visual system will be better. Numerous studies [1,2,3] have shown that inspired by the human visual system, the recognition system can be designed with relatively high accuracy. According to the recent advances in visual neuroscience, the researchers tend to develop biologically plausible algorithms to improve the accuracy of the object recognition system. Object recognition considerably relies on image representation, for which, in this paper, a novel biologically inspired model is presented for this stage. Bag-of-words (BoW) representation [4] has been generally employed because it is robust to object scale and translation changes. Soft k-means [5] and sparse coding [6] procedures are presented to overcome this problem
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
