Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.

Decentralized centroid and formation control for multi-robot systems

Abstract: In this paper, a decentralized control strategy for networked multi-robot systems that allows the tracking of the team centroid and the relative formation is presented. The proposed solution consists of a distributed observer-controller scheme where, based only on local information, each robot estimates the collective state and tracks the two assigned control variables. We provide a formal stability analysis of the observer-controller scheme and we relate convergence properties to the topology of the connectivity graph. Experiments are presented to validate the approach.

Localization Algorithm based on Improved Weighted Centroid in Wireless Sensor Networks

Abstract: Location technology is becoming more and more important in wireless sensor networks. The weighted centroid localization offers a fast and simple algorithm for the location equipment in wireless sensor networks. The algorithm derives from the centroid measurement and calculation device of the adjacent anchor in the average coordinate. After the analysis of the radio propagation loss model, the most appropriate log-distance distribution model is selected to simulate the signal propagation. Based on the centroid algorithm and the weighted centroid algorithm, this paper proposes an ellipse centroid localization algorithm. This algorithm makes use of ellipse’s characteristic to estimate the unknown node’s coordinate. The main idea of ellipse centroid localization algorithm is the precision control factor that can control the algorithm’s location precision. In ellipse centroid localization algorithm, node is extended as anchor in order to strengthen anchor density’s dynamic characteristic. The simulation result shows the ellipse centroid localization algorithm is more effective than the centroid algorithm and the weighted centroid precision algorithm

C2FDA: Coarse-to-Fine Domain Adaptation for Traffic Object Detection

Abstract: Object detection in traffic scenes has attracted considerable attention from both academia and industry recently. Modern detectors achieve excellent performance under a simple constrained environment while performing poorly under the actual complex and open traffic environment. Therefore, the capability of adapting to new and unseen domains is a key factor for the large-scale application and proliferation of detectors in autonomous driving. To this end, this paper proposes a novel category-induced coarse-to-fine domain adaptation approach (C2FDA) for cross-domain object detection, which consists of three pivotal components: (1) Attention-induced coarse-grained alignment module (ACGA), which strengthens the distribution alignment across disparate domains within the foreground features in category-agnostic way by the minimax optimization between the domain classifier and the backbone feature extractor; (2) Attention-induced feature selection module, which assists the model to emphasize the crucial foreground features and enables the ACGA to focus on the relevant and discriminative foreground features, without being affected by the distribution of inconsequential background features; (3) Category-induced fine-grained alignment module (CFGA), which reduces the domain shift in category-aware way by minimizing the distance of centroids with the same category from different domains and maximizing that of centroids with disparate categories. We evaluate the performance of our approach in various source/target domain pairs and comprehensive results demonstrate that C2FDA significantly outperforms the state-of-the-art on multiple domain adaptation scenarios, i.e., the synthetic-to-real adaptation, the weather adaptation, and the cross camera adaptation.

Matching Contours in Images through the use of Curvature, Distance to Centroid and Global Optimization with Order-Preserving Constraint

Abstract: This paper presents a new methodology to establish the best global match of objects’ contours in images. The first step is the extraction of the sets of ordered points that define the objects’ contours. Then, by using the curvature value and its distance to the corresponded centroid for each point, an affinity matrix is built. This matrix contains information of the cost for all possible matches between the two sets of ordered points. Then, to determine the desired one-to-one global matching, an assignment algorithm based on dynamic programming is used. This algorithm establishes the global matching of the minimum global cost that preserves the circular order of the contours’ points. Additionally, a methodology to estimate the similarity transformation that best aligns the matched contours is also presented. This methodology uses the matching information which was previously obtained, in addition to a statistical process to estimate the parameters of the similarity transformation in question. In order to validate the proposed matching methodology, its results are compared to those obtained by the geometric modeling approach proposed by Shapiro and Brady who are well known in this domain.