Assessment of a photogrammetric approach for urban DSM extraction from tri-stereoscopic satellite imagery

Built-up environments are extremely complex for 3D surface modelling purposes. The main distortions that hamper 3D reconstruction from 2D imagery are image dissimilarities, concealed areas, shadows, height discontinuities and discrepancies between smooth terrain and man-made features. A methodology is proposed to improve automatic photogrammetric extraction of an urban surface model from high resolution satellite imagery with the emphasis on strategies to reduce the effects of the cited distortions and to make image matching more robust. Instead of a standard stereoscopic approach, a digital surface model is derived from tri-stereoscopic satellite imagery. This is based on an extensive multi-image matching strategy that fully benefits from the geometric and radiometric information contained in the three images. The bundled triplet consists of an IKONOS along-track pair and an additional near-nadir IKONOS image. For the tri-stereoscopic study a densely built-up area, extending from the centre of Istanbul to the urban fringe, is selected. The accuracy of the model extracted from the IKONOS triplet, as well as the model extracted from only the along-track stereopair, are assessed by comparison with 3D check points and 3D building vector data

An Improved Multi-Level Edge-Based Stereo Correspondence Technique for Snake Based Object Segmentation

Disparity maps generated by stereo correspondence are very useful for stereo object segmentation because based on disparity background clutter can be effectively removed from the image. This enables conventional methods such as snake-based to efficiently detect the object of interest contour. In this research I propose two main enhancements on Alattar’s method first I increased the number of edge levels, and utilized the color information in the matching process. Besides a few minor modifications, these enhancements achieve a more accurate disparity map which eventually helps achieve higher segmentation accuracy by the snake. Experiments were performed in various indoor and outdoor image conditions to evaluate the matching performance of the proposed method compared to the previous work

LEVEL-BASED CORRESPONDENCE APPROACH TO COMPUTATIONAL STEREO

One fundamental problem in computational stereo reconstruction is correspondence. Correspondence is the method of detecting the real world object reflections in two camera views. This research focuses on correspondence, proposing an algorithm to improve such detection for low quality cameras (webcams) while trying to achieve real-time image processing. Correspondence plays an important role in computational stereo reconstruction and it has a vast spectrum of applicability. This method is useful in other areas such as structure from motion reconstruction, object detection, tracking in robot vision and virtual reality. Due to its importance, a correspondence method needs to be accurate enough to meet the requirement of such fields but it should be less costly and easy to use and configure, to be accessible by everyone. By comparing current local correspondence method and discussing their weakness and strength, this research tries to enhance an algorithm to improve previous works to achieve fast detection, less costly and acceptable accuracy to meet the requirement of reconstruction. In this research, the correspondence is divided into four stages. Two stages of preprocessing which are noise reduction and edge detection have been compared with respect to different methods available. In the next stage, the feature detection process is introduced and discussed focusing on possible solutions to reduce errors created by system or problem occurring in the scene such as occlusion. Lastly, in the final stage it elaborates different methods of displaying reconstructed result. Different sets of data are processed based on the steps involved in correspondence and the results are discussed and compared in detail. The finding shows how this system can achieve high speed and acceptable outcome despite of poor quality input. As a conclusion, some possible improvements are proposed based on ultimate outcome

Coarse-to-Fine Lifted MAP Inference in Computer Vision

There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.Comment: Published in IJCAI 201

Low-level Vision by Consensus in a Spatial Hierarchy of Regions

We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs. The framework uses a dense, overlapping set of image regions at multiple scales and a "local model," such as a slanted-plane model for stereo disparity, that is expected to be valid piecewise across the visual field. Estimation is cast as optimization over a dichotomous mixture of variables, simultaneously determining which regions are inliers with respect to the local model (binary variables) and the correct co-ordinates in the local model space for each inlying region (continuous variables). When the regions are organized into a multi-scale hierarchy, optimization can occur in an efficient and parallel architecture, where distributed computational units iteratively perform calculations and share information through sparse connections between parents and children. The framework performs well on a standard benchmark for binocular stereo, and it produces a distributional scene representation that is appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page: http://www.ttic.edu/chakrabarti/consensus