18409496
Building optimal 2D statistical shape models
Statistical shape models are used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need, during training, to establish a dense correspondence across a training set of segmented shapes. We show that model construction can be treated as an optimisation problem, automating the process and guaranteeing the effectiveness of the resulting models. This is achieved by optimising an objective function with respect to the correspondence. We use an information theoretic objective function that directly promotes desirable features of the model. This is coupled with an effective method of manipulating correspondence, based on reparameterising each training shape, to build optimal statistical shape models. The method is evaluated on several training sets of shapes, showing that it constructs better models than alternative approaches. q 2003 Elsevier B.V. All rights reserved.
11355264
Automatic Model Selection by Modelling the Distribution of Residuals
Many problems in computer vision involve a choice of the most suitable model for a set of data. Typically one wishes to choose a model which best represents the data in a way that generalises to unseen data without overfitting. We propose an algorithm in which the quality of a model match can be determined by calculating how well the distribution of model residuals matches a distribution estimated from the noise on the data. The distribution of residuals has two components - the measurement noise, and the noise caused by the uncertainty in the model parameters. If the model is too complex to be supported by the data, then there will be large uncertainty in the parameters. We demonstrate that the algorithm can be used to select appropriate model complexity in a variety of problems, including polynomial fitting, and selecting the number of modes to match a shape model to noisy data.
117576
Transforming pixel signatures into an improved metric space
Abstract We address the problem of using scale-orientation pixel signatures to characterise local structure in X-ray mammograms, though the method we report is of general application. When signatures are treated as vectors for statistical analysis, the Euclidean metric is not well behaved. We have previously described a Best Partial Match (BPM) metric that measures signature similarity more naturally, but at high computational cost. We present a method for transforming signatures into a new space in which Euclidean distance approximates BPM distance, allowing BPM distance to be estimated at low computational cost. The new space is constructed using multi-dimensional scaling. The nonlinear transformation between the old and new spaces is learned using support vector regression. We present experimental results for mammographic data.
3881938
A Comparison of Face Verification Algorithms using Appearance Models
Statistical models of shape and appearance have been successfully used in face modeling, tracking and synthesis. In this paper we describe experiments using appearance models for face verification. We compare a variety of different algorithms on a standard face database (XM2VTS). We demonstrate that simple methods of correction for head pose and face expression can significantly improve results.
14993996
3D Statistical Shape Models Using Direct Optimisation of Description Length
We describe an automatic method for building optimal 3D statistical shape models from sets of training shapes. Although shape models show considerable promise as a basis for segmenting and interpreting images, a major drawback of the approach is the need to establish a dense correspondence across a training set of example shapes. It is important to establish the correct correspondence, otherwise poor models can result. In 2D, this can be achieved using manual 'landmarks', but in 3D this becomes impractical. We show it is possible to establish correspondences automatically, by casting the correspondence problem as one of finding the 'optimal' parameterisation of each shape in the training set. We describe an explicit representation of surface parameterisation, that ensures the resulting correspondences are legal, and show how this representation can be manipulated to minimise the description length of the training set using the model. This results in compact models with good generalisation properties. Results are reported for two sets of biomedical shapes, showing significant improvement in model properties compared to those obtained using a uniform surface parameterisation.
626048
Corresponding Articular Cartilage Thickness Measurements in the Knee Joint by Modelling the Underlying Bone
We present a method for corresponding and combining cartilage thickness readings from a population of patients using the underlying bone structure as a reference. Knee joint femoral bone and cartilage surfaces are constructed from a set of parallel slice segmentations of MR scans. Correspondence points across a population of bone surfaces are defined and refined by minimising an objective function based on the Minimum Description Length of the resulting statistical shape model. The optimised bone model defines a set of corresponding locations from which 3D measurements of the cartilage thickness can be taken and combined for a population of patients. Results are presented for a small group of patients demonstrating the feasibility and potential of the approach as a means of detecting sub-millimetre cartilage thickness changes due to disease progression.
17626745
Design of an Adaptive Architecture for Energy Efficient Wireless Image Communication
With the projected significant growth in mobile internet and multimedia services, there is a strong demand for next-generation wireless appliances capable of image communication. However, wireless image communication faces significant bottlenecks including high energy and bandwidth consumption. Past studies have shown that the bottlenecks to wireless image communication can be overcome by developing adaptive image compression algorithms and dynamically adapting them to current channel conditions and service requirements [1,2].In this paper, we present the design of an adaptive hardware/software architecture that enables adaptive wireless image communication. Through intelligent co-design of the proposed architecture and algorithms, we achieve an architecture which enables not only power and performance efficient implementation, but also fast and efficient run-time adaptation of image compression parameters. To achieve efficient image compression and run-time adaptation, we characterized the adaptation needs of an adaptive image compression algorithm in terms of parameters, and implemented an adaptive hardware/software architecture capable of executing JPEC image compression with different parameters. We present experimental results demonstrating that the proposed architecture enables low overhead adaptation to current wireless conditions and requirements while implementing a low cost (energy and performance) implementation of adaptive image compression algorithms.
2497525
Structure and Motion from Line Segments in Multiple Images
This paper presents a new method for recovering the three dimensional structure of a scene composed of straight line segments using the image data obtained from a moving camera. The recovery algorithm is formulated in terms of an objective function which measures the total squared distance in the image plane between the observed edge segments and the projections (perspective) of the reconstructed lines. This objective function is minimized with respect to the line parameters and the camera positions to obtain an estimate for the structure of the scene. The effectiveness of this approach is demonstrated quantitatively through extensive simulations and qualitatively with actual image sequences. The implementation is being made publicly available. >
33669607
Patient-Specific Operative Planning for Aorto-Femoral Reconstruction Procedures
Traditionally, a surgeon will select a procedure for a particular patient based on past experience for patients with a similar state of disease. The experience gained from this patient will be selectively used when treating the next patient with similar symptoms. In this work, a surgical planning system was developed enabling a vascular surgeon to create and test alternative operative plans prior to surgery for a given patient. One dimensional and three dimensional hemodynamic (i.e. blood flow) simulations were performed for rest and exercise for operative plans for two aorto-femoral bypass patients and compared with actual postoperative data. The information that can be obtained from one-dimensional (volume flow distribution and pressure losses) and three-dimensional (wall shear stress) hemodynamic simulation may be clinically relevant to vascular surgeons planning interventions.
15360768
A hardware/software reconfigurable architecture for adaptive wireless image communication
With the projected significant growth in mobile internet and multimedia services, there is a strong demand for next-generation appliances capable of wireless image communication. One of the major bottlenecks in enabling wireless image communication is the high energy requirement, which may surpass the current and future capabilities of battery technologies. Past studies have shown that the bottlenecks can be overcome by developing adaptive multimedia compression algorithms which can adapt to dynamic channel conditions and service requirements. We present an application-specific hardware/software reconfigurable architecture to support adaptive image compression algorithms. We present a design methodology which considers co-design between adaptive algorithms and architectural design leading to a reconfigurable architecture for image compression algorithms. Codesign of the proposed architecture aims not only at performance and power efficient implementation, but also towards fast and efficient run-time adaptation of an adaptive image compression algorithm.
2609415
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs. Our modeling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetricmodeling method which facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach can model large architectural environments with far fewer photographs than current image-based modeling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach’s ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs. CR Descriptors: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding Modeling and recovery of physical attributes; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Color, shading, shadowing, and texture I.4.8 [Image Processing]: Scene Analysis Stereo; J.6 [Computer-Aided Engineering]: Computer-aided design (CAD).