Highly accurate environmental perception is essential for autonomous mobile robots, and saliency has been studied as a method for identifying conspicuous regions and areas of interest. Research on saliency began with the principle model proposed by Koch and Ullman, and numerous methods have been developed using 2D images and RGB-D data as inputs. However, these image-based approaches do not adequately capture the 3D spatial features needed for autonomous navigation. Therefore, to meet accuracy requirements, it is necessary to use 3D point clouds as input when applying saliency to autonomous mobile robots. In this study, we constructed a topological structure from 3D point clouds using Growing Neural Gas which is one of unsupervised learning, and developed a saliency map utilizing a center-surround suppression mechanism. This approach allows for the identification of more detailed regions of focus compared to previous studies. Through experiments on both benchmark datasets and real-world 3D point clouds, we demonstrate the effectiveness of the proposed method.

In this paper, we focus on the double bind between textual and visual information and investigate the following points: (1) whether Large Language Models (LLMs) can detect a sense of incongruity, (2) whether images that evoke positive or negative impressions influence the detection of incongruity, and (3) whether the incongruity judgments made by LLMs align with those of human subjects. We examined three LLMs: GTP-4o, Gemini 1.5 Flash, and Claude 3 Haiku. Our results indicate that LLMs tend to detect the sense of incongruity arising from the double bind between text and images. Moreover, despite variations in impression evaluations due to different images, there was a consistent tendency for LLMs to detect incongruity. Finally, among the LLMs studied, GTP-4o’s incongruity judgments were most similar to those of human subjects.

The c-regression model (CRM) is a method that simultaneously performs clustering and regression. CRM is based on linear regression, which has the disadvantage of not obtaining a nonlinear structure. To overcome this drawback, a c-regression model based on Gaussian process regression (GPCRM), which extends CRM to Gaussian process regression, has been proposed. Gaussian process regression is a method for estimating nonlinear regression lines using kernel functions, which are inner products of feature spaces. Although GPCRM can handle nonlinear structures, it has been reported that underfitting can occur depending on the kernel parameters, resulting in large residuals in the regression line. Therefore, this paper proposes Maximum Marginal Likelihood Gaussian Process based c-Regression Models as a method to maximize the marginal likelihood and optimize the kernel parameters. Numerical experiments suggest that the proposed method divides the data by finding a regression line with smaller residuals than the existing method.