L. Grammatikopoulos

Generation of Orthoimages and Perspective Views with Automatic Visibility Checking and Texture Blending

Conventional orthorectification software cannot handle surface occlusions and image visibility. The approach presented here synthesizes related work in photogrammetry and computer graphics/vision to automatically produce orthographic and perspective views based on fully 3D surface data (supplied by laser scanning). Surface occlusions in the direction of projection are detected to create the depth map of the new image. This information allows identifying, by visibility checking through back-projection of surface triangles, all source images which are entitled to contribute color to each pixel of the novel image. Weighted texture blending allows regulating the local radiometric contribution of each source image involved, while outlying color values are automatically discarded with a basic statistical test. Experimental results from a close-range project indicate that this fusion of laser scanning with multiview photogrammetry could indeed combine geometric accuracy with high visual quality and speed. A discussion of intended improvements of the algorithm is also included.

Monocular camera fall detection system exploiting 3d measures: a semi-supervised learning approach

Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. The system presented in this article addresses the fall detection problem through visual cues. The proposed methodology utilize a fast, real-time background subtraction algorithm based on motion information in the scene and capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object and, at the same time, it exploits 3D spaces measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.

Stereo matching based on census transformation of image gradients

Although multiple-view matching provides certain significant advantages regarding accuracy, occlusion handling and radiometric fidelity, stereo-matching remains indispensable for a variety of applications; these involve cases when image acquisition requires fixed geometry and limited number of images or speed. Such instances include robotics, autonomous navigation, reconstruction from a limited number of aerial/satellite images, industrial inspection and augmented reality through smart-phones. As a consequence, stereo-matching is a continuously evolving research field with growing variety of applicable scenarios. In this work a novel multi-purpose cost for stereo-matching is proposed, based on census transformation on image gradients and evaluated within a local matching scheme. It is demonstrated that when the census transformation is applied on gradients the invariance of the cost function to changes in illumination (non-linear) is significantly strengthened. The calculated cost values are aggregated through adaptive support regions, based both on cross-skeletons and basic rectangular windows. The matching algorithm is tuned for the parameters in each case. The described matching cost has been evaluated on the Middlebury stereo-vision 2006 datasets, which include changes in illumination and exposure. The tests verify that the census transformation on image gradients indeed results in a more robust cost function, regardless of aggregation strategy.

3D city models completion by fusing lidar and image data

A fundamental step in the generation of visually detailed 3D city models is the acquisition of high fidelity 3D data. Typical approaches employ DSM representations usually derived from Lidar (Light Detection and Ranging) airborne scanning or image based procedures. In this contribution, we focus on the fusion of data from both these methods in order to enhance or complete them. Particularly, we combine an existing Lidar and orthomosaic dataset (used as reference), with a new aerial image acquisition (including both vertical and oblique imagery) of higher resolution, which was carried out in the area of Kallithea, in Athens, Greece. In a preliminary step, a digital orthophoto and a DSM is generated from the aerial images in an arbitrary reference system, by employing a Structure from Motion and dense stereo matching framework. The image-to-Lidar registration is performed by 2D feature (SIFT and SURF) extraction and matching among the two orthophotos. The established point correspondences are assigned with 3D coordinates through interpolation on the reference Lidar surface, are then backprojected onto the aerial images, and finally matched with 2D image features located in the vicinity of the backprojected 3D points. Consequently, these points serve as Ground Control Points with appropriate weights for final orientation and calibration of the images through a bundle adjustment solution. By these means, the aerial imagery which is optimally aligned to the reference dataset can be used for the generation of an enhanced and more accurately textured 3D city model.

Precise 3D Reconstruction of Cultural Objects Using Combined Multi-component Image Matching and Active Contours Segmentation

Cultural and creative industries constitute a large range of economic activities. Towards this expansion we need to state the inclusion of ICT technologies, as such of 3D reconstruction methods. However, precise 3D reconstruction under a computationally affordable manner is a research challenge. One way to precisely reconstruct a cultural object is through the use of photogrammetry with the main goal of finding the correspondences between two or more images to reconstruct 3D surfaces. A cultural object is often surrounded by visual background data that should be excluded to improve 3D reconstruction accuracy. Background conditions dynamically change, especially if the object is captured under outdoor conditions, where many occlusions occur and the shadows effects are not negligible. In this paper, we propose a combine image segmentation and matching method to yield an affordable 3D reconstruction of cultural objects. Image segmentation is performed on the use of active contours while image matching through novel multi-cost criteria optimization functions. Experimental results on real-life ancient column capitals indicate the efficiency of the proposed scheme both in terms of performance efficiency and cost.