Search Results (3)

With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds in capturing intricate structures within complex scenes, combined with the computational burden imposed by large datasets, presents substantial challenges to current registration methods. The proposed method encompasses two innovative feature point pruning techniques and two closely interconnected progressive processes. First, it identifies structural points that effectively represent the features of the scene and performs a rapid initial alignment of point clouds within the two-dimensional plane. Subsequently, it establishes the mapping relationship between the point clouds to be matched utilizing FPFH descriptors, followed by further screening to extract the maximum consensus set composed of points that meet constraints based on the intensity of graph nodes. Then, it integrates the processes of feature point description and similarity measurement to achieve precise point cloud registration. The proposed method effectively extracts matching primitives from large datasets, addressing issues related to false matches and noise in complex data environments. It has demonstrated favorable matching results even in scenarios with low overlap between datasets. On two public datasets and a self-constructed dataset, the method achieves an effective point set screening rate of approximately 1‰. On the WHU-TLS dataset, our method achieves a registration accuracy characterized by a rotation precision of 0.062° and a translation precision of 0.027 m, representing improvements of 70% and 80%, respectively, over current state-of-the-art (SOTA) methods. The results obtained from real registration tasks demonstrate that our approach attains competitive registration accuracy when compared with existing SOTA techniques. Full article

As an aesthetic resource in ancient China, the Zhuangzi’s description of Dao is similar to the American philosopher Emerson’s experience of beauty, and both reveal that the essence of beauty lies in its inherent vitality, spiritual transcendence, and the unity of multidimensional connotations. Emerson defines beauty as the constitution of all things in the world and believes it to be an expression of the universe. The Zhuangzi proposes the thought of tiandi damei 天地大美 (lit. Great Beauty of heaven and earth) as a manifestation of the function of the wordless Dao. Nature, intact from any human interference, becomes the common intermediary for Emerson and the Zhuangzi to elaborate on the connotations of beauty. The Emersonian definition of beauty originates from the philosophical implication of the world in ancient Greek, whereas the meaning of Great Beauty in the Zhuangzi, which embodies the worship of heaven in primitive religion, is very close to Emerson’s definition of beauty. The pattern of mei 美 consisting of da 大 (lit. great, equivalent to Dao) and yang 羊 (lit. auspice) signifies the natural celestial phenomena predicting good or bad luck and can be seen as synonymous with Dao illuminated by Daoism. By describing such natural imagery as forest, time sequence, dawn, and wilderness, Emerson reveals the vastness, harmony, brightness, and tranquility of beauty, which not only delights the spirit but also brings the human soul back to its natural state and improves personality. Emerson’s illumination of beauty conforms to those of Dao unraveled by the Zhuangzi. Despite the difference between the former’s poetic linguistic feature and the latter’s application of allegorical fables, both resort to visualized language to express internal aesthetic perceptions of the physical nature. Using the approaches of word tracing, textual comparison, and logical analysis, this article identifies the consistency in the original meanings of beauty in both Emerson’s essays and the Zhuangzi first and then goes on to analyze the similarities between their descriptions of natural imagery, so as to hint at the commonality in their understanding of natural beauty and verify the significance of literary language in cross-cultural comparative research. Full article

Classification using the rich information provided by time-series and polarimetric Synthetic Aperture Radar (SAR) images has attracted much attention. The key point is to effectively reveal the correlation between different dimensions of information and form a joint feature. In this paper, a multi-dimensional SAR descriptive primitive for each single pixel is firstly constructed, which in the polarimetric scale obtains incoherent information through target decompositions while in the time scale obtains coherent information through stochastic walk. Secondly, for the purpose of feature extraction and dimension reduction, a special feature space mapping for the descriptive primitive of the whole image is proposed based on sparse manifold expression and compressed sensing. Finally, the above feature is inputted into a support vector machine (SVM) classifier. This proposed method can inherently integrate the features of polarimetric SAR times series. Experiment results on three real time-series polarimetric SAR data sets show the effectiveness of our presented approach. The idea of a multi-dimensional descriptive primitive as a convenient tool also opens a new spectrum of potential for further processing of polarimetric SAR image time series. Full article