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.

Center of mass attracts attention

Abstract: Using the spatial cueing technique, this study demonstrates that the center of mass (centroid) of a visual scene has a special ability to attract attention even when there is no object presented at this location. Four boxes formed an imaginary square and were presented to the left or right hemifield. After the cueing in one box, a target appeared in one of the four boxes and, in addition, at centroid. Fastest reaction times were observed at centroid, irrespective of whether this centroid was also occupied by a box. Reaction times at the uncued locations varied according to their relative positions to centroid and fixation. No inhibition of return effect was observed when the cue was at centroid.

Investigation of Centroid Connector Placement for Advanced Traffic Assignment Models with Added Network Detail

Abstract: Advanced traffic assignment models, such as simulation-based dynamic traffic assignment, typically incorporate more detailed network representations than do traditional planning models. In this context, the placement of centroid connectors may have a significant effect on model performance, and attention must be paid to their number and location to avoid unrealistic congestion or low utilization of minor roadways by local traffic. Given that the manual inspection of centroid connector placement may be too time-consuming in large regional networks, this paper proposes two simple automatic centroid connector placement strategies for dynamic traffic assignment applications. The first approach radially distributes the connectors to the nearest nodes and is intended to exemplify some limitations of the most common techniques in practice. The second strategy involves dividing the centroid and subsequent demand into two parts, distributing the demand across one subcentroid linked to nearby nodes and one linked t...

Exact bias correction and covariance estimation for stereo vision

Abstract: We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig. Our analysis is based on the observation that, due to quantization error, a 3D point reconstructed by triangulation essentially represents an entire region in space. The true location of the world point that generated the triangulated point could be anywhere in this region. We argue that the reconstructed point, if it is to represent this region in space without bias, should be located at the centroid of this region, which is not what has been done in the literature. We derive the exact geometry of these regions in space, which we call 3D cells, and we show how they can be viewed as uniform distributions of possible pre-images of the pair of corresponding pixels. By assuming a uniform distribution of points in 3D, as opposed to a uniform distribution of the projections of these 3D points on the images, we arrive at a fast and exact computation of the triangulation bias in each cell. In addition, we derive the exact covariance matrices of the 3D cells. We validate our approach in a variety of simulations ranging from 3D reconstruction to camera localization and relative motion estimation. In all cases, we are able to demonstrate a marked improvement compared to conventional techniques for small disparity values, for which bias is significant and the required corrections are large.

Unsupervised texture segmentation using multiresolution Markov random fields

Abstract: In this thesis, a multiresolution Markov Random Field (MMRF) model for segmenting textured images without supervision is proposed. Stochastic relaxation labelling is adopted to assign the class label with highest probability to the block (site) being visited. Class information is propagated from low spatial resolution to high spatial resolution, via appropriate modifications to the interaction energies defining the field, to minimise class-position uncertainty. The thesis contains novel ideas presented in Chapter 4 and 5, respectively. In Chapter 4, the Multiresolution Fourier Transform (MFT) is used to provide a set of spatially localised texture descriptors, which are based on a two-component model of texture, in which one component is a deformation, representing the structural or deterministic elements and the other is a stochastic one. Experiments show that the algorithm is efficient in alleviating class-position uncertainty via data propagation across resolutions. However, the blocking artifacts of the segmentation results show that it is preferable to combine both class and position information so as to achieve smoother and more accurate boundary estimation. In Chapter 5, based on the same MFT-MMRF framework, a boundary process is proposed to refine the segmentation result of the region process proposed in Chapter 4. At each resolution, all the image blocks on either sides of the preliminary boundary detected in the region process are treated as potential boundary-containing blocks (PBCB's). The orientation and the centroid of the boundary-segment contained in each PBCB are calculated. The sequence of PBCB's are then modelled as a MRF and the interaction energy between each pair of neighbouring blocks is defined as a function of the 'distance' D between the centroids of the two boundary segments. During the stochastic relaxation process boundary/non-boundary labels are assigned to the PBCB's. Once the algorithm converges, the centroids of the identified true boundary blocks are connected to form the refined boundary which is propagated down to the next resolution for further refinement.