Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

We consider the convex quadratic linearly constrained problem with bounded variables and with huge and dense Hessian matrix that arises in many applications such as the training problem of bias support vector machines. We propose a decomposition algorithmic scheme suitable to parallel implementations and we prove global convergence under suitable conditions. Focusing on support vector machines training, we outline how these assumptions can be satisfied in practice and we suggest various specific implementations. Extensions of the theoretical results to general linearly constrained problem are provided. We included numerical results on support vector machines with the aim of showing the viability and the effectiveness of the proposed scheme

Direction Selective Contour Detection for Salient Objects

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy even in case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points are extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods

Struktúrális információ az érzékelők mérési terében = Structural information in the space of sensor networks

A projekt során különböző körülmények között végeztünk méréseket, és ennek megfelelő feladatokban értünk el eredményeket: 1. Több kamera használatával: mozgáskövetés, mozgásjelleg/viselkedés felismerés, helyszín geometria viszonyainak bemérése. 2. Mélységi detekcióra alkalmas eszközökkel: LIDAR és TOF kamera képeiből illetve pontfelhőjéből detektáltunk mozgásjellemzőket, 3D alakzatokat. 3. Légi és orvosi képeken illetve képsorozatokon: változások követése, jellegzetes struktúrák detektálása. A projekt során jelentős elméleti eredmények születettek: 1. A vizsgált helyszín jellemző struktúráinak illetve változásainak felismerésére, 2. Új képleírók kidolgozása gyenge felbontású alakzatok felismeréséhez és finom felbontású aktív kontúr előállítására, 3. Videókép sorozatokon a szokatlan mozgássorok illetve speciális viselkedések felismerése, követése, 4. Mélységi információk szűrése 2D (gráfok, dekonvolúció), illetve 3D (LIDAR, TOF) adatokon. Az eredményeket a téma szakkonferenciáin, illetve a szakma jelentős folyóirataiban publikáltuk. | We have built up several measurement environments for the project’ purposes, and we have achieved results evaluating the experiments in these setups: 1. Multicamera system: motion tracking, recognition of the behavior of the objects, the structural geometry given by the scene events, 2. Devices for depth measurements: images and point-clouds of LIDAR and Time-of-Flight cameras for motion tracking and shape detection, 3. Aerial and medical images/image series: detection of changes, finding featuring structures. During the project the following important theoretical results have been published in the most important conferences and journals: 1. Change detection and structure recognition of the given scene, 2. Improved feature point set for low resolution pattern recognition and enhanced active contour detection, 3. Unusual motion flow pattern and crowd behavior detection on video sequences, 4. Depth information filters in 2D (graphs, deconvolution) and in 3D (LIDAR, TOF)

Orientation-selective building detection in aerial images

This paper introduces a novel aerial building detection method based on region orientation as a new feature, which is used in various steps throughout the presented framework. As building objects are expected to be connected with each other on a regional level, exploiting the main orientation obtained from the local gradient analysis provides further information for detection purposes. The orientation information is applied for an improved edge map design, which is integrated with classical features like shadow and color. Moreover, an orthogonality check is introduced for finding building candidates, and their final shapes defined by the Chan-Vese active contour algorithm are refined based on the orientation information, resulting in smooth and accurate building outlines. The proposed framework is evaluated on multiple data sets, including aerial and high resolution optical satellite images, and compared to six state-of-the-art methods in both object and pixel level evaluation, proving the algorithm's efficiency. © 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

Harris function based active contour external force for image segmentation

Deformable active contour (snake) models are efficient tools for object boundary detection. Existing alterations of the traditional gradient vector flow (GVF) model have reduced sensitivity to noise, parameters and initial location, but high curvatures and noisy, weakly contrasted boundaries cause difficulties for them. This paper introduces two Harris based parametric snake models, Harris based gradient vector flow (HGVF) and Harris based vector field convolution (HVFC), which use the curvature-sensitive Harris matrix to achieve a balanced, twin-functionality (corner and edge) feature map. To avoid initial location sensitivity, starting contour is defined as the convex hull of the most attractive points of the map. In the experimental part we compared our methods to the traditional external energy-inspired state-of-the-art GVF and VFC; the recently published parametric decoupled active contour (DAC) and the non-parametric Chan–Vese (ACWE) techniques. Results show that our methods outperform the classical approaches, when tested on images with high curvature, noisy boundaries