295 research outputs found
Shape detection of structural changes in long time-span aerial image samples by new saliency methods
Bayesian foreground and shadow detection in uncertain frame rate surveillance videos
In in this paper we propose a new model regarding
foreground and shadow detection in video sequences. The model works without detailed a-priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: (1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects. (2)We give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts. (3) We show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov Random Field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets
Segmentation of remote sensing images using similarity measure based fusion-MRF model
Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote sensing image analysis tasks, including comparison and retrieval in repositories containing multitemporal remote image samples for the same area in very different quality and details. We propose a multilayer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering on a fused-image series by using cross-layer similarity measure, followed by multilayer Markov random field segmentation. The resulted label map is applied for the automatic training of single layers. After the segmentation of each single layer separately, changes are detected between single label maps. The significant benefit of the proposed method has been numerically validated on remotely sensed image series with ground-truth data
Adaptive Image Decomposition Into Cartoon and Texture Parts Optimized by the Orthogonality Criterion
In this paper a new decomposition method is introduced
that splits the image into geometric (or cartoon) and
texture parts. Following a total variation based preprocesssing,
the core of the proposed method is an anisotropic diffusion
with an orthogonality based parameter estimation and stopping
condition. The quality criterion is defined by the theoretical
assumption that the cartoon and the texture components of an
image should be orthogonal to each other. The presented method
has been compared to other decomposition algorithms through
visual and numerical evaluation to prove its superiority
Video törtĂ©nĂ©sek felismerĂ©se automatikusan detektált adatbázis asszociáciĂłk segĂtsĂ©gĂ©vel = Classification of video events through automatically detected categories of video database
Elvileg is Ăşj algoritmusokat dolgoztunk ki videĂł rĂ©szletek elemzĂ©sĂ©hez. Egy eljárásunk szerint kĂ©pek relatĂv fĂłkusztĂ©rkĂ©pĂ©t határozzuk meg elĹ‘zetes modell vagy bármilyen paramĂ©ter ismerete nĂ©lkĂĽl. Ehhez egy elvileg is Ăşj hiba-mĂ©rtĂ©ket vezettĂĽnk be, ami a vak-dekonvolĂşciĂłs iteráciĂłk során a hibának az ortogonalitási feltĂ©teltĹ‘l valĂł eltĂ©rĂ©sĂ©t mutatja. Kidolgoztuk a mozgĂł Ă©s állĂł objektumokat elkĂĽlönĂtĂ©sĂ©t mozgĂł kamera esetĂ©re, azt is becsĂĽlve, hogy a teljes megfigyelt terĂĽletbĹ‘l mit lát Ă©ppen a kamera. Automatikus kĂ©pillesztĹ‘ eljárásokat fejlesztettĂĽnk ki, amelyek általános megoldást biztosĂtanak szĂ©les bázistávolságĂş sztereĂł-kĂ©pek illesztĂ©sĂ©re tetszĹ‘leges körĂĽlmĂ©nyek esetĂ©n. A mozgás-gyanĂşs pontok statisztikai összehasonlĂtásával az esemĂ©nytĂ©r szerkezetĂ©nek geometriai adataira tehetĂĽnk becslĂ©st elĹ‘feltevĂ©s Ă©s modell nĂ©lkĂĽl. Ăšj mĂłdszert adtunk az egynĂ©zetű kĂ©pen levĹ‘ tĂĽkör, illetve az árnyĂ©kot adĂł fĂ©nyforrás vetĂtĂ©si pontjának meghatározására. Az eljárás során statisztikai korreláciĂłtĂ©rkĂ©pekre illesztett parametrikus hipotĂ©zismodelleket optimalizálunk a vetĂtĂ©si hiba segĂtsĂ©gĂ©vel. A kapott eredmĂ©nyek jĂłl jellemzik a szĂnhely esemĂ©nyterĂ©nek elrendezĂ©sĂ©t. A videĂłkĂ©p mozgĂł alakzatainak eredmĂ©nyes detektáláshoz egyĂ©rtelmű eredmĂ©nyt adĂł kutatást folytattunk az optimális szĂnmetrika kialakĂtására. MĂłdszereket dolgoztunk ki videĂłfelvĂ©telek vizuális szempontbĂłl fontos terĂĽleteinek Ă©s esemĂ©nyeinek automatikus elkĂĽlönĂtĂ©sĂ©re Ă©s alkalmazásukra a visszakeresĂ©sben. | We have introduced theoretically new algorithms for analysing video shots and events. We present an automatic focus area estimation method, working with a single image without a priori information about the image, the camera, or the scene. It produces relative focus maps by localized blind deconvolution and a new residual error-based classification. Evaluation and comparison is performed and applicability is shown through image indexing. We have developed segmentation method for arbitrary foreground objects in case of indefinitely moving cameras. A new motion-based method is presented for automatic registration of images in multicamera systems, to permit synthesis of wide-baseline composite views. Our approach does not need any a priori information about the scene, the appearance of objects in the scene, or their motion. We introduce an entropy-based preselection of motion histories and an iterative Bayesian assignment of corresponding image areas. Correlated point-histories and data-set optimization lead to the matching of the different views. An automatic method is presented using motion statistics to determine vanishing-point position for the geometrical modelling of reflective surfaces or cast shadows, even in cases of heavy noise effects. We proposed an optimal colour space for modelling cast shadow problems in video sequences, applied in an MRF framework
Markovian framework for foreground-background-shadow separation of real world video scenes
In this paper we give a new model for foreground-background-shadow separation. Our method extracts the faithful silhouettes of foreground objects even if they have partly background like colors and shadows are observable on the image. It does not need any a priori information about the shapes of the objects, it assumes only they are not point-wise. The method exploits temporal statistics to characterize the background and shadow, and spatial statistics for the foreground. A Markov Random Field model is used to enhance the accuracy of the separation. We validated our method on outdoor and indoor video sequences captured by the surveillance system of the university campus, and we also tested it on well-known benchmark videos
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