Scalable virtual viewpoint image synthesis for multiple camera environments

One of the main aims of emerging audio-visual (AV) applications is to provide interactive navigation within a captured event or scene. This paper presents a view synthesis algorithm that provides a scalable and flexible approach to virtual viewpoint synthesis in multiple camera environments. The multi-view synthesis (MVS) process consists of four different phases that are described in detail: surface identification, surface selection, surface boundary blending and surface reconstruction. MVS view synthesis identifies and selects only the best quality surface areas from the set of available reference images, thereby reducing perceptual errors in virtual view reconstruction. The approach is camera setup independent and scalable as virtual views can be created given 1 to N of the available video inputs. Thus, MVS provides interactive AV applications with a means to handle scenarios where camera inputs increase or decrease over time

Multiple image view synthesis for free viewpoint video applications

Interactive audio-visual (AV) applications such as free viewpoint video (FVV) aim to enable unrestricted spatio-temporal navigation within multiple camera environments. Current virtual viewpoint view synthesis solutions for FVV are either purely image-based implying large information redundancy; or involve reconstructing complex 3D models of the scene. In this paper we present a new multiple image view synthesis algorithm that only requires camera parameters and disparity maps. The multi-view synthesis (MVS) approach can be used in any multi-camera environment and is scalable as virtual views can be created given 1 to N of the available video inputs, providing a means to gracefully handle scenarios where camera inputs decrease or increase over time. The algorithm identifies and selects only the best quality surface areas from available reference images, thereby reducing perceptual errors in virtual view reconstruction. Experimental results are presented and verified using both objective (PSNR) and subjective comparisons

Numerical integration and other techniques for computer aided network design programming Final technical report, 1 Jan. 1970 - 1 Jan. 1971

Matrix method and stiffly stable algorithms in numerical integration for computer aided network design programmin

Bone cross-sectional geometry in male runners, gymnasts, swimmers and non-athletic controls: a hip-structural analysis study.

Loading of the skeleton is important for the development of a functionally and mechanically appropriate bone structure, and can be achieved through impact exercise. Proximal femur cross-sectional geometry was assessed in the male athletes (n = 55) representing gymnastics, endurance running and swimming, and non-athletic controls (n = 22). Dual energy X-ray absorptiometry (iDXA, GE Healthcare, UK) measurements of the total body (for body composition) and the left proximal femur were obtained. Advanced hip structural analysis (AHA) was utilised to determine the areal bone mineral density (aBMD), hip axis length (HAL), cross-sectional area (CSA), cross-sectional moment of inertia (CSMI) and the femoral strength index (FSI). Gymnasts and runners had greater age, height and weight adjusted aBMD than in swimmers and controls (p < 0.05). Gymnasts and runners had greater resistance to axial loads (CSA) and the runners had increased resistance against bending forces (CSMI) compared to swimmers and controls (p < 0.01). Controls had a lower FSI compared to gymnasts and runners (1.4 vs. 1.8 and 2.1, respectively, p < 0.005). Lean mass correlated with aBMD, CSA and FSI (r = 0.365-0.457, p < 0.01), particularly in controls (r = 0.657-0.759, p < 0.005). Skeletal loading through the gymnastics and running appears to confer a superior bone geometrical advantage in the young adult men. The importance of lean body mass appears to be of particular significance for non-athletes. Further characterisation of the bone structural advantages associated with different sports would be of value to inform the strategies directed at maximising bone strength and thus, preventing fracture

3D image analysis for pedestrian detection

A method for solving the dense disparity stereo correspondence problem is presented in this paper. This technique is designed specifically for pedestrian detection type applications. A new Ground Control Points (GCPs) scheme is introduced, using groundplane homography information to determine regions in which good GCPs are likely to occur. The method also introduces a dynamic disparity limit constraint to further improve GCP selection and dense disparity generation. The technique is applied to a real world pedestrian detection scenario with a background modeling system based on disparity and edges

Multispectral object segmentation and retrieval in surveillance video

This paper describes a system for object segmentation and feature extraction for surveillance video. Segmentation is performed by a dynamic vision system that fuses information from thermal infrared video with standard CCTV video in order to detect and track objects. Separate background modelling in each modality and dynamic mutual information based thresholding are used to provide initial foreground candidates for tracking. The belief in the validity of these candidates is ascertained using knowledge of foreground pixels and temporal linking of candidates. The transferable belief model is used to combine these sources of information and segment objects. Extracted objects are subsequently tracked using adaptive thermo-visual appearance models. In order to facilitate search and classification of objects in large archives, retrieval features from both modalities are extracted for tracked objects. Overall system performance is demonstrated in a simple retrieval scenari

Comparison of fusion methods for thermo-visual surveillance tracking

In this paper, we evaluate the appearance tracking performance of multiple fusion schemes that combine information from standard CCTV and thermal infrared spectrum video for the tracking of surveillance objects, such as people, faces, bicycles and vehicles. We show results on numerous real world multimodal surveillance sequences, tracking challenging objects whose appearance changes rapidly. Based on these results we can determine the most promising fusion scheme

Detection thresholding using mutual information

In this paper, we introduce a novel non-parametric thresholding method that we term Mutual-Information Thresholding. In our approach, we choose the two detection thresholds for two input signals such that the mutual information between the thresholded signals is maximised. Two efficient algorithms implementing our idea are presented: one using dynamic programming to fully explore the quantised search space and the other method using the Simplex algorithm to perform gradient ascent to significantly speed up the search, under the assumption of surface convexity. We demonstrate the effectiveness of our approach in foreground detection (using multi-modal data) and as a component in a person detection system