Structural matching by discrete relaxation

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

MissForest - nonparametric missing value imputation for mixed-type data

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a nonparametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple data sets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in data sets including different types of variables. In our comparative study missForest outperforms other methods of imputation especially in data settings where complex interactions and nonlinear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.Comment: Submitted to Oxford Journal's Bioinformatics on 3rd of May 201

Intensity based image registration of satellite images using evolutionary techniques

Image registration is the fundamental image processing technique to determine geometrical transformation that gives the most accurate match between reference and floating images. Its main aim is to align two images. Satellite images to be fused for numerous applications must be registered before use. The main challenges in satellite image registration are finding out the optimum transformation parameters. Here in this work the non-alignment parameters are considered to be rigid and affine transformation. An intensity based satellite image registration technique is being used to register the floating image to the native co-ordinate system where the normalized mutual information (NMI) is taken as the similarity metric for optimizing and updating transform parameters. Because of no assumptions are made regarding the nature of the relationship between the image intensities in both modalities NMI is very general and powerful and can be applied automatically without prior segmentation on a large variety of data and as well works better for overlapped images as compared to mutual information(MI). In order to get maximum accuracy of registration the NMI is optimized using Genetic algorithm, particle swarm optimization and hybrid GA-PSO. The random initialization and computational complexity makes GA oppressive, whereas weak local search ability with a premature convergence is the main drawback of PSO. Hybrid GA-PSO makes a trade-off between the local and global search in order to achieve a better balance between convergence speed and computational complexity. The above registration algorithm is being validated with several satellite data sets. The hybrid GA-PSO outperforms in terms of optimized NMI value and percentage of mis-registration error

Removing Parallax-Induced False Changes in Change Detection

Accurate change detection (CD) results in urban environments is of interest to a diverse set of applications including military surveillance, environmental monitoring, and urban development. This work presents a hyperspectral CD (HSCD) framework. The framework uncovers the need for HSCD methods that resolve false change caused by image parallax. A Generalized Likelihood Ratio Test (GLRT) statistic for HSCD is developed that accommodates unknown mis-registration between imagery described by a prior probability density function for the spatial mis-registration. The potential of the derived method to incorporate more complex signal proccessing functions is demonstrated by the incorporation of a parallax error mitigation component. Results demonstrate that parallax mitigation reduces false alarms

Dense Vision in Image-guided Surgery

Image-guided surgery needs an efficient and effective camera tracking system in order to perform augmented reality for overlaying preoperative models or label cancerous tissues on the 2D video images of the surgical scene. Tracking in endoscopic/laparoscopic scenes however is an extremely difficult task primarily due to tissue deformation, instrument invasion into the surgical scene and the presence of specular highlights. State of the art feature-based SLAM systems such as PTAM fail in tracking such scenes since the number of good features to track is very limited. When the scene is smoky and when there are instrument motions, it will cause feature-based tracking to fail immediately. The work of this thesis provides a systematic approach to this problem using dense vision. We initially attempted to register a 3D preoperative model with multiple 2D endoscopic/laparoscopic images using a dense method but this approach did not perform well. We subsequently proposed stereo reconstruction to directly obtain the 3D structure of the scene. By using the dense reconstructed model together with robust estimation, we demonstrate that dense stereo tracking can be incredibly robust even within extremely challenging endoscopic/laparoscopic scenes. Several validation experiments have been conducted in this thesis. The proposed stereo reconstruction algorithm has turned out to be the state of the art method for several publicly available ground truth datasets. Furthermore, the proposed robust dense stereo tracking algorithm has been proved highly accurate in synthetic environment (< 0.1 mm RMSE) and qualitatively extremely robust when being applied to real scenes in RALP prostatectomy surgery. This is an important step toward achieving accurate image-guided laparoscopic surgery.Open Acces