39 research outputs found
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
Feature-Based Target Detection and Classification in Passive ISAR Range-Crossrange Images
We present a method for passive ISAR image analysis for target detection, feature extraction and shape-based classification without a priori target shape information. Results show that classification is possible with limited target samples
Struktúrális információ az érzékelők mérési terében = Structural information in the space of sensor networks
A projekt során kĂĽlönbözĹ‘ körĂĽlmĂ©nyek között vĂ©geztĂĽnk mĂ©rĂ©seket, Ă©s ennek megfelelĹ‘ feladatokban Ă©rtĂĽnk el eredmĂ©nyeket: 1. Több kamera használatával: mozgáskövetĂ©s, mozgásjelleg/viselkedĂ©s felismerĂ©s, helyszĂn geometria viszonyainak bemĂ©rĂ©se. 2. MĂ©lysĂ©gi detekciĂłra alkalmas eszközökkel: LIDAR Ă©s TOF kamera kĂ©peibĹ‘l illetve pontfelhĹ‘jĂ©bĹ‘l detektáltunk mozgásjellemzĹ‘ket, 3D alakzatokat. 3. LĂ©gi Ă©s orvosi kĂ©peken illetve kĂ©psorozatokon: változások követĂ©se, jellegzetes struktĂşrák detektálása. A projekt során jelentĹ‘s elmĂ©leti eredmĂ©nyek szĂĽletettek: 1. A vizsgált helyszĂn jellemzĹ‘ struktĂşráinak illetve változásainak felismerĂ©sĂ©re, 2. Ăšj kĂ©pleĂrĂłk kidolgozása gyenge felbontásĂş alakzatok felismerĂ©sĂ©hez Ă©s finom felbontásĂş aktĂv kontĂşr előállĂtására, 3. VideĂłkĂ©p sorozatokon a szokatlan mozgássorok illetve speciális viselkedĂ©sek felismerĂ©se, követĂ©se, 4. MĂ©lysĂ©gi informáciĂłk szűrĂ©se 2D (gráfok, dekonvolĂşciĂł), illetve 3D (LIDAR, TOF) adatokon. Az eredmĂ©nyeket a tĂ©ma szakkonferenciáin, illetve a szakma jelentĹ‘s folyĂłirataiban 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)
Multidirectional Building Detection in Aerial Images Without Shape Templates
Abstract. The aim of this paper is to exploit orientation information of an urban area for extracting building contours without shape templates. Unlike using shape templates, these given contours describe more variability and reveal the fine details of the building outlines, resulting in a more accurate detection process, which is beneficial for many tasks, like map updating and city planning. According to our assumption, orientation of the closely located buildings is coherent, it is related to the road network, therefore adaptation of this information can lead to more efficient building detection results. The introduced method first extracts feature points for representing the urban area. Orientation information in the feature point neighborhoods is analyzed to define main orientations. Based on orientation information, the urban area is classified into different directional clusters. The edges of the classified building groups are then emphasized with shearlet based edge detection method, which is able to detect edges only in the main directions, resulting in an efficient connectivity map. In the last step, with the fusion of the feature points and connectivity map, building contours are detected with a non-parametric active contour method.
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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