Topological Active Nets Research Articles

Smart traffic management of vehicles using faster R-CNN based deep learning method

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses adaptive background modeling. It also addresses shadow and illumination issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this framework with respect to other methods.

Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing

In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.

Extended Topological Active Nets

Topological Active Nets are promising parametric deformable models that integrate features of region-based and boundary-based segmentation techniques. Problems associated with the complexity of the model, however, have limited their utility. This paper introduces an extension of the model, defining a new behavior for changing its topology, as well as a novel external force definition and a new local search optimization procedure. In particular, we propose a new automatic pre-processing phase, a new external energy term based on the Extended Vector Field Convolution, node movement constraints to avoid crossing links, and different procedures to perform link cuts and hole detection. Moreover, the new local search procedure also incorporates heuristics to correct the position of eventually misplaced nodes. The proposal has been tested on 18 synthetic images which present different segmentation difficulties along with 3 real medical images. Its performance has been compared with that of the original Topological Active Net optimization approach along with both state-of-the-art parametric and geometric active contours: two snakes (based on Gradient Vector Flow and Vector Field Convolution), and two level sets (Chan and Vese, and Geodesic Active Contour). Our new method outperforms all the others for the given image sets, in terms of segmentation accuracy measured by using four standard segmentation metrics.

Image Segmentation Using Extended Topological Active Nets Optimized by Scatter Search

Image segmentation is the critical task of partitioning an image into multiple objects. Deformable Models are effective tools aimed at performing image segmentation. Among them, Topological Active Nets (TANs), and their extension, ETANs, are models integrating features of region-based and boundary-based segmentation techniques. Since the deformation of the meshes composing these models to fit the objects to be segmented is controlled by an energy functional, the segmentation task is tackled as a numerical optimization problem. Despite their good performance, the existing ETAN optimization method (based on a local search) can lead to result inaccuracies, that is, local optima in the sense of optimization. This paper introduces a novel optimization approach by embedding ETANs in a global search memetic framework, Scatter Search, thus considering multiple alternatives in the segmentation process using a very small solution population. With the aim of improving the accuracy of the segmentation results in a reasonable processing time, we introduce a global search-suitable internal energy term, a diversity function, a frequency memory population generator and two proper solution combination operators. In particular, these operators are effective in coalescing multiple meshes, a task previous global search methods for TAN optimization failed to accomplish. The proposal has been tested on a mix of 20 synthetic and real medical images with different segmentation difficulties. Its performance has been compared with two ETAN optimization approaches (the original local search and a new multi-start local search) as well as with the state-of-the-art memetic proposal for classical TAN optimization based on differential evolution. Our new method significantly outperformed the other three for the given set of images in terms of four standard segmentation metrics.

Multiobjective differential evolution in the optimization of topological active models

We performed a combination of differential evolution and a multiobjective approach for the optimization of topological active nets, a deformable model that integrates features of region-based and boundary-based segmentation techniques. As the deformation of the model is determined by the minimization of different energy components, the multiobjective methodology provides a natural solution to the minimization of the different energy components or objectives. This approach also provides a solution to the need of an experimental tuning of the weight of the energy parameters, which is also needed for each kind of image.We used a well established evolutionary multiobjective optimization algorithm, SPEA2, adapted to our application. The incorporation of the differential evolution approach in the SPEA2 algorithm allows a faster search of the Pareto Front, incorporating the main advantages of both algorithms and minimizing the designer decisions. The combined approach permits the correct segmentation of real images in the 2D and 3D domains.

Topological Active Volume 3D segmentation model optimized with genetic approaches

The Topological Active Volumes is an active model focused on 3D segmentation tasks. It is based on the 2D Topological Active Nets model and provides information about the surfaces and the inside of the detected objects in the scene. This paper proposes new optimization approaches based on Genetic Algorithms that improve the results of the 3D segmentations and overcome some drawbacks of the model related to parameter tuning or noise conditions. The hybridization of the genetic algorithm with a greedy local search allows the treatment of topological changes in the model, with the possibility of an automatic subdivision of the Topological Active Volume. This combination integrates the advantages of the global and local search procedures in the segmentation process.

Evolutionary multiobjective optimization of Topological Active Nets

In this work we used the evolutionary multiobjective optimization methodology for the optimization of Topological Active Nets. This is a deformable model that integrates features of region-based and boundary-based segmentation techniques. The model deformation is controlled by energy functions that must be minimized. When the minimization task is performed by means of a greedy local search or a global search method, an experimental tuning of the energy parameters is needed to obtain a correct segmentation. This tuning must be done for each kind of image. Evolutionary multiobjective optimization gives a solution to this problem by considering the optimization of several objectives in parallel. We used the SPEA2 algorithm, adapted to our application, to the search of the Pareto optimal individuals. We tested the improvements and problems between the uses of the multiobjective optimization technique versus the use of a genetic algorithm and a greedy local search in our application of the optimization of the Topological Active Nets deformable model. We used several representative examples with images from different medical domains.

Localisation of the optic disc by means of GA-optimised Topological Active Nets

In this paper we propose a new approach to the optic disc localisation process in digital retinal images by means of Topological Active Nets (TAN). This is a deformable model used for image segmentation that integrates features of region-based and edge-based segmentation techniques, being able to fit the edges of the objects and model their inner topology. In this paper the active nets incorporate new energy terms for the optic disc localisation and their optimisation is performed with a genetic algorithm, with adapted or new ad hoc genetic operators. There is no need of any pre-processing of the images, which allows a quasi automatic localisation of the optic disc. This process also provides a simultaneous segmentation of the disc. We present representative results of optic disc localisations showing the advantages of the approach, with images focusing on the optic disc or on the macula, and with images with different levels of noise and lesion areas.

Genetic approaches for topological active nets optimization

The topological active nets (TANs) model is a deformable model used for image segmentation. It integrates features of region-based and edge-based segmentation techniques so it is able to fit the contours of the objects and model their inner topology. Also, topological changes in its structure allow the detection of concave and convex contours, holes, and several objects in the scene. Since the model deformation is based on the minimization of an energy functional, the adjustment depends on the minimization algorithm. This paper presents two evolutionary approaches to the energy minimization problem in the TAN model. The first proposal is a genetic algorithm with ad hoc operators whereas the second approach is a hybrid model that combines genetic and greedy algorithms. Both evolutionary approaches improve the accuracy of the segmentation even though only the hybrid model allows topological changes in the model structure.