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

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

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 történések felismerése automatikusan detektált adatbázis asszociációk segítségével = Classification of video events through automatically detected categories of video database

Elvileg is új algoritmusokat dolgoztunk ki videó részletek elemzéséhez. Egy eljárásunk szerint képek relatív fókusztérképét határozzuk meg előzetes modell vagy bármilyen paraméter ismerete nélkül. Ehhez egy elvileg is új hiba-mértéket vezettünk be, ami a vak-dekonvolúciós iterációk során a hibának az ortogonalitási feltételtől való eltérését mutatja. Kidolgoztuk a mozgó és álló objektumokat elkülönítését mozgó kamera esetére, azt is becsülve, hogy a teljes megfigyelt területből mit lát éppen a kamera. Automatikus képillesztő eljárásokat fejlesztettünk ki, amelyek általános megoldást biztosítanak széles bázistávolságú sztereó-képek illesztésére tetszőleges körülmények esetén. A mozgás-gyanús pontok statisztikai összehasonlításával az eseménytér szerkezetének geometriai adataira tehetünk becslést előfeltevés és modell nélkül. Új módszert adtunk az egynézetű képen levő tükör, illetve az árnyékot adó fényforrás vetítési pontjának meghatározására. Az eljárás során statisztikai korrelációtérképekre illesztett parametrikus hipotézismodelleket optimalizálunk a vetítési hiba segítségével. A kapott eredmények jól jellemzik a színhely eseményterének elrendezését. A videókép mozgó alakzatainak eredményes detektáláshoz egyértelmű eredményt adó kutatást folytattunk az optimális színmetrika kialakítására. Módszereket dolgoztunk ki videófelvételek vizuális szempontból fontos területeinek és eseményeinek automatikus elkülönítésére és alkalmazásukra a visszakeresésben. | 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

Color models of shadow detection in video scenes

In this paper we address the problem of appropriate modelling of shadows in color images. While previous works compared the different approaches regarding their model structure, a comparative study of color models has still missed. This paper attacks a continuous need for defining the appropriate color space for this main surveillance problem. We introduce a statistical and parametric shadow model-framework, which can work with different color spaces, and perform a detailed comparision with it. We show experimental results regarding the following questions: (1) What is the gain of using color images instead of grayscale ones? (2) What is the gain of using uncorrelated spaces instead of the standard RGB? (3) Chrominance (illumination invariant), luminance, or ”mixed” spaces are more effective? (4) In which scenes are the differences significant? We qualified the metrics both in color based clustering of the individual pixels and in the case of Bayesian foreground-background-shadow segmentation. Experimental results on real-life videos show that CIE L*u*v* color space is the most efficient