An object-based coding scheme is proposed for the coding of a stereoscopic image sequence, using motion and disparity information. A hierarchical block-based motion estimation approach is used for initialization, while disparity estimation is performed using a pixel-based hierarchical dynamic programming algorithm. A split and merge segmentation procedure based on 3-D motion modeling is then used to determine regions with similar motion parameters. The segmentation part of the algorithm is interleaved with the estimation part in order to optimize the coding performance of the procedure. Furthermore, a technique is examined for propagating the segmentation information with time. A 3-D motion compensated prediction technique is used for both intensity and depth image sequence coding. Error images and depth maps are encoded using DCT and Huuman methods. Alternately , an ecient wireframe depth modeling technique may b e u s e d t o c o n vey depth information to the receiver. Motion and wireframe model parameters are then quantized and transmitted to the decoder, along with the segmentation information. As a straightforward application, the use of the depth map information for the generation of intermediate views at the receiver is also discussed. The performance of the proposed compression methods is evaluated experimentally and is compared to other stereoscopic image sequence coding schemes.

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Building detection from 2D high-resolution satellite images is a computer vision, photogrammetry and remote sensing task that has arisen in the last decades with the advances in sensors technology and can be utilised in several applications that require the creation of urban maps or the study of urban changes. However, the variety of irrelevant objects that appear in an urban environment and resemble buildings and the significant variations in the shape and generally the appearance of buildings render building detection a quite demanding task. As a result, automated methods that can robustly detect buildings in satellite images are necessary. To this end, we propose a building detection method that consists of two modules. The first module is a feature detector that extracts Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) from image regions. Using a novel approach, a Support Vector Machine (SVM) classifier is trained with the introduction of a special denoising distance measure for the computation of distances between HOG-LBP descriptors before their classification to the building or non-building class. The second module consists of a set of region refinement processes that employs the output of the HOG-LBP detector in the form of detected rectangular image regions. Image segmentation is performed and a novel building recognition methodology is proposed to accurately identify building regions, while simultaneously discard false detections of the first module of the proposed method. We demonstrate that the proposed methodology can robustly detect buildings from satellite images and outperforms state-of-the-art building detection methods.

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