A new transform is presented that utilizes local radial symmetry to highlight points of interest within a scene. Its low-computational complexity and fast runtimes makes this method well-suited for real-time vision applications. The performance of the transform is demonstrated on a wide variety of images and compared with leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transform is seen to offer equal or superior performance to contemporary techniques at a relatively low-computational cost. A real-time implementation of the transform is presented running at over 60 frames per second on a standard Pentium III PC.

Fast radial symmetry for detecting points of interest

To secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches Monitoring of both power line components and vegetation around them is included Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data The review shows that most previous studies have concentrated on the mapping and analysis of network components In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images However, in many studies datasets have been small and numerical quality analyses have been omitted Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

https://cyberleninka.org/article/n/613852.pdf

Remote sensing methods for power line corridor surveys

This paper proposes a systematic methodology of fuzzy logic modeling for complex system modeling. It has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces 4 parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. The fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering. Major bottlenecks are addressed and analytical solutions are suggested. We also address the classification process to extend the derived fuzzy partition to the entire output space. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy fine clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples-a nonlinear function and a gas furnace dynamic procedure-are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches.

Development of a systematic methodology of fuzzy logic modeling

An Empirical-Statistical Agenda for Recognition.- A Formal-Physical Agenda for Recognition.- Shape.- Shape Models and Object Recognition.- Order Structure, Correspondence, and Shape Based Categories.- Quasi-Invariant Parameterisations and Their Applications in Computer Vision.- Shading.- Representations for Recognition Under Variable Illumination.- Shadows, Shading, and Projective Ambiguity.- Grouping.- Grouping in the Normalized Cut Framework.- Geometric Grouping of Repeated Elements within Images.- Constrained Symmetry for Change Detection.- Grouping Based on Coupled Diffusion Maps.- Representation and Recognition.- Integrating Geometric and Photometric Information for Image Retrieval.- Towards the Integration of Geometric and Appearance-Based Object Recognition.- Recognizing Objects Using Color-Annotated Adjacency Graphs.- A Cooperating Strategy for Objects Recognition.- Statistics, Learning and Recognition.- Model Selection for Two View Geometry:A Review.- Finding Objects by Grouping Primitives.- Object Recognition with Gradient-Based Learning.

Shape, Contour and Grouping in Computer Vision

The problem of recovering an input signal from a blurred output, in an input-output system with linear distortion, is ubiquitous in science and technology. When the blurred output is not degraded by statistical noise the problem is entirely deterministic and amounts to a mathematical inversion of a linear system with positive parameters, subject to positivity constraints on the solution. We show that all such linear inverse problems with positivity restrictions (LININPOS problems for short) can be interpreted as statistical estimation problems from incomplete data based on infinitely large'samples', and that maximum likelihood (ML) estimation and the EM algorithm provide a straightforward method of solution for such problems

From image deblurring to optimal investments : maximum likelihood solutions for positive linear inverse problems

Indexing and retrieval methods based on the image content are required to effectively use information from the large repositories of digital images and videos currently available. Both global (colour, texture, motion, etc.) and local (object shape, etc.) features are needed to perform a reliable content based retrieval. We present a method for automatic extraction of global image features, like colour and motion parameters, and their use for data restriction in video database querying. Further retrieval is therefore accomplished, in a restricted set of images, by shape feature (skeleton, local symmetry moments, correlation, etc.) local search. The proposed indexing methodology has been developed and tested inside JACOB, a prototypal system for content-based video database querying.

Content-based indexing of image and video databases by global and shape features

A vision procedure may be considered as the repeated application of image operators until the vision goal is reached. The type of these operators and the spaces on which they are defined and act depends on the specific problem and on what we are searching on the image. Morphological operations, as filtering, edge detection, skeletonizing, and so on, are mainly required at low and medium levels of the vision procedure, where local and global knowledge is used to enhance the image information content, before a final decision about the image is taken.

Mathematical Morphology Based on Fuzzy Operators

Features selection and `possibility theory'

The paper shows a distributed architecture for autonomous robot navigation. The architecture is based on three modules that are implemented on separate and interacting agents: the target recognizer, the obsta90cle evaluator and the planner. An adaptive genetic algorithm has been studied to identify mechanisms for reaching the target and for manipulating the 2-directions of the robot; the distributed architecture has been embedded in the DAISY (Distributed Architecture for Intelligent System). Experiments have been carried out using a LEGO intelligent brick.

http://math.unipa.it/~lenzitti/papers/camp2000.pdf

V. Di Gesu

Papers

Content-based indexing of image and video databases by global and shape features

Mathematical Morphology Based on Fuzzy Operators

Features selection and `possibility theory'

A distributed architecture for autonomous navigation of robots

Some statistical properties of the minimum spanning forest