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.

中文翻译：

---

数据驱动和生物启发的预处理方案，以改善视觉对象识别

图像中的自主对象识别是安全和商业应用中最关键的主题之一。由于视觉神经科学的最新进展，研究人员倾向于扩展生物学上可行的方案，以提高物体识别的准确性。预处理是视觉识别系统的一部分，受到了越来越少的关注。在本文中，我们通过使用数据驱动的视觉注意力机制，提出了一种新的，简单的，受生物学启发的预处理技术。在这一部分中，模拟了视网膜神经节细胞（RGC）的反应。在获得这些响应之后，选择有效的阈值。然后，据此提取信息最多的原始图像的点。然后，创建具有这些点的新图像，最后，通过将这些图像与熵系数组合，可以找到最显着的物体。在提取适当的特征之后，分类器将初始图像分类为预定义的对象类别之一。我们的系统在Caltech-101数据集上进行了评估。实验结果证明了这种新型预处理方法的功效。