We provide a comparison between competitive approaches, the Gambini's algorithm (GA) and a CFAR method (The Co-efficient of Variation Detector), to highlight their strengths and weaknesses. In doing so, we help researchers and practitioners making informed decisions towards the best technique to be applied in any situation of interest. Both methods were able to detect edges in the simulated images used in this work. The GA has the advantage of finding a single pixel as an edge while the CFAR method detects multiple edges, mainly when the edge is a ramp edge. In the other hand, the CFAR method can be directly applied to the images while the GA needs to have as input a region of interest and lines where the transition point between distributions (edges) are detected.

Polarimetric synthetic aperture radar (PolSAR) sensors have reached an essential position in remote sensing. The images they provide have speckle noise, making their processing and analysis challenging tasks. We discuss an edge detection method based on the fusion of evidences obtained in the intensity channels hh, hv, and vv of PolSAR multilook images. The method consists of detecting transition points in the thinnest possible range of data that covers two regions using maximum likelihood under the Wishart distribution. The fusion methods used are: simple average, multiresolution discrete wavelet transform (MR-DWT), principal component analysis (PCA), receiver operating characteristic (ROC) statistics, multiresolution stationary wavelet transform (MR-SWT), and a multiresolution method based on singular value decomposition (MR-SVD). A quantitative analysis suggests that PCA and MR-SVD provide the best results.

A prompt and effective response to natural disasters is essential. Polarimetric Synthetic Aperture Radar (PolSAR) images playa crucial role in helping decisions in these scenarios, especially in adverse weather or the absence of sunlight. In this context, PolSAR images still provide information. In these situations, the process of finding precise boundaries to determine the extent of the disaster is an essential issue as PolSAR image resolutions are usually high (of the order of 10 m). The fusion of information from the intensity channels of PolSAR images for edge detection enhances the precision of edges presented in these images. In this work, we propose three metrics: the accuracy (Mac), the$\mathrm{F}_{1}$-score (Mfe), and the Matthews correlation coefficient normalized (nMcc). These metrics quantify the influence of the information from the intensity channels of PolSAR images on the information fusion process for edge detection.

This paper presents a control structure for a general-purpose image understanding system. It addresses the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is done by an independent subsystem that uses Bayesian networks and utility theory to compute marginal value of information and selects the algorithm with the highest value of information. It is shown that the knowledge base can be acquired using learning techniques and the value-driven approach to the selection of vision algorithms leads to performance gains.

This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is performed by an independent subsystem that uses Bayesian networks and utility theory to compute the marginal value of information provided by alternative operators and selects the ones with the highest value. We have implemented and tested this control structure with several aerial image datasets. The results show that the knowledge base used by the system can be acquired using standard learning techniques and that the value-driven approach to the selection of vision algorithms leads to performance gains. Moreover, the modular system architecture simplifies the addition of both control knowledge and new vision algorithms.

The Art Gallery Problem is the problem of determining the number of observers necessary to cover an art gallery room such that every point is seen by at least one observer. This problem is well known and has a linear solution for the 2 dimensional case, but little is known in the 3-D case. In this paper we present a polynomial time solution for the 3-D version of the Art Gallery problem. Because the problem is NP-hard, the solution presented is an approximation, and we present the bounds to our solution. Our solution uses techniques from computational geometry, graph coloring and set coverage. A complexity analysis is presented for each step and an analysis of the overall quality of the solution is given.