Generating a 3d hand model from frontal color and range scans

Realistic 3D modeling of human hand anatomy has a number of important applications, including real-time tracking, pose estimation, and human-computer interaction. However the use of RGB-D sensors to accurately capture the full 3D shape of a hand is limited by self-occlusions, relatively smaller size of the hand and the requirement to capture multiple images. In this paper, we propose a method for generating a detailed, realistic hand model from a single frontal range scan and registered color image. In essence, our method converts this 2.5D data into a fully 3D model. The proposed approach extracts joint locations from the color image using a fingertip and interfinger region detector with a Naive Bayes probabilistic model. Direct correspondence between these joint locations in the range scan and a synthetic hand model are used to perform rigid registration, followed by a thin-plate-spline deformation that non-rigidly registers a synthetic model. This reconstructed model maintains similar geometric properties as the range scan, but also includes the back side of the hand. Experimental results demonstrate the promise of the method to produce detailed and realistic 3D hand geometry

Pathotype-specific QTL for stem rust resistance in Lolium perenne

A genetic map populated with RAD and SSR markers was created from F1 progeny of a stem rust-susceptible and stem rust-resistant parent of perennial ryegrass (Lolium perenne). The map supplements a previous map of this population by having markers in common with several other Lolium spp. maps including EST-SSR anchor markers from a consensus map published by other researchers. A QTL analysis was conducted with disease severity and infection type data obtained by controlled inoculation of the population with each of two previously characterized pathotypes of Puccinia graminis subsp. graminicola that differ in virulence to different host plant genotypes in the F1 population. Each pathotype activated a specific QTL on one linkage group (LG): qLpPg1 on LG7 for pathotype 101, or qLpPg2 on LG1 for pathotype 106. Both pathotypes also activated a third QTL in common, qLpPg3 on LG6. Anchor markers, present on a consensus map, were located in proximity to each of the three QTL. These QTL had been detected also in previous experiments in which a genetically heterogeneous inoculum of the stem rust pathogen activated all three QTL together. The results of this and a previous study are consistent with the involvement of the pathotype-specific QTL in pathogen recognition and the pathotype-nonspecific QTL in a generalized resistance response. By aligning the markers common to other published reports, it appears that two and possibly all three of the stem rust QTL reported here are in the same general genomic regions containing some of the L. perenne QTL reported to be activated in response to the crown rust pathogen (P. coronata).Keywords: Ryegrass population, Fescue festuca pratensis, Disease resistance, Puccinia coronata resistance, SSR markers, Multiflorum lam., Crown rust, Self incompatibility, F-SP Lolii, Linkage mapKeywords: Ryegrass population, Fescue festuca pratensis, Disease resistance, Puccinia coronata resistance, SSR markers, Multiflorum lam., Crown rust, Self incompatibility, F-SP Lolii, Linkage ma

DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data

With recent advancements in machine learning, the research community has made tremendous advances towards the classification of neurological disorders from time-series functional MRI signals. However, existing classification techniques rely on hand-crafted features and classical machine learning models. In this paper, we propose an end-to-end model that utilizes the representation learning capability of deep learning to classify a neurological disorder from fMRI data. The proposed DeepFMRI model is comprised of three networks, namely (1) a feature extractor, (2) a similarity network, and (3) a classification network. The model takes fMRI raw time-series signals as input and outputs the predicted labels; and is trained end-to-end using back-propagation. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative model outperforms previous state-of-the-art

FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI

Investigation of functional brain connectivity patterns using functional MRI has received significant interest in the neuroimaging domain. Brain functional connectivity alterations have widely been exploited for diagnosis and prediction of various brain disorders. Over the last several years, the research community has made tremendous advancements in constructing brain functional connectivity from timeseries functional MRI signals using computational methods. However, even modern machine learning techniques rely on conventional correlation and distance measures as a basic step towards the calculation of the functional connectivity. Such measures might not be able to capture the latent characteristics of raw time-series signals. To overcome this shortcoming, we propose a novel convolutional neural network based model, FCNet, that extracts functional connectivity directly from raw fMRI time-series signals. The FCNet consists of a convolutional neural network that extracts features from time-series signals and a fully connected network that computes the similarity between the extracted features in a Siamese architecture. The functional connectivity computed using FCNet is combined with phenotypic information and used to classify individuals as healthy controls or neurological disorder subjects. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative framework can improve classification accuracy, which indicates that the features learnt from FCNet have superior discriminative power

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI

Background: Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behaviour analysis. New method: In this paper, we propose an end-to-end deep learning architecture to diagnose ADHD. Our aim is to (1) automatically classify a subject as ADHD or healthy control, and (2) demonstrate the importance of functional connectivity to increase classification accuracy and provide interpretable results. The proposed method, called DeepFMRI, is comprised of three sequential networks, namely (1) a feature extractor, (2) a functional connectivity network, and (3) a classification network. The model takes fMRI pre-processed time-series signals as input and outputs a diagnosis, and is trained end-to-end using back-propagation. Results: Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative method outperforms previous state-of-the-art. Different imaging sites contributed the data to the ADHD-200 dataset. For the New York University imaging site, our proposed method was able to achieve classification accuracy of 73.1% (specificity 91.6%, sensitivity 65.5%). Comparison with Existing Methods: In this work, we propose a novel end-to-end deep learning method incorporating functional connectivity for the classification of ADHD. To the best of our knowledge, this has not been explored by existing studies. Conclusions: The results suggest that the proposed end-to-end deep learning architecture achieves better performance as compared to the other state-of-the-art methods. The findings suggest that the frontal lobe contains the most discriminative power towards the classification of ADHD

Inverse Consistency Error in the Registration of Prone and Supine Images in CT Colonography

Robust registration between prone and supine data acquisitions for CT colonography is pivotal for medical interpretation but a challenging problem. One measure when evaluating non-rigid registration algorithms over the whole of the deformation field is the inverse consistency error, which suggests improved registration quality when the inverse deformation is consistent with the forward deformation. We show that using computed landmark displacements to initialise an intensity based registration reduces the inverse consistency error when using a state-of-the-art non-rigid b-spline registration method. This method aligns prone and supine 2D images derived from CT colonography acquisitions in a cylindrical domain. Furthermore, we demonstrate that using the same initialisation also improves registration accuracy for a set of manually identified reference points in cases exhibiting local luminal collapse

A First Order Predicate Logic Formulation of the 3D Reconstruction Problem and its Solution Space

This paper defines the 3D reconstruction problem as the process of reconstructing a 3D scene from numerous 2D visual images of that scene. It is well known that this problem is ill-posed, and numerous constraints and assumptions are used in 3D reconstruction algorithms in order to reduce the solution space. Unfortunately, most constraints only work in a certain range of situations and often constraints are built into the most fundamental methods (e.g. Area Based Matching assumes that all the pixels in the window belong to the same object). This paper presents a novel formulation of the 3D reconstruction problem, using a voxel framework and first order logic equations, which does not contain any additional constraints or assumptions. Solving this formulation for a set of input images gives all the possible solutions for that set, rather than picking a solution that is deemed most likely. Using this formulation, this paper studies the problem of uniqueness in 3D reconstruction and how the solution space changes for different configurations of input images. It is found that it is not possible to guarantee a unique solution, no matter how many images are taken of the scene, their orientation or even how much color variation is in the scene itself. Results of using the formulation to reconstruct a few small voxel spaces are also presented. They show that the number of solutions is extremely large for even very small voxel spaces (5 x 5 voxel space gives 10 to 10(7) solutions). This shows the need for constraints to reduce the solution space to a reasonable size. Finally, it is noted that because of the discrete nature of the formulation, the solution space size can be easily calculated, making the formulation a useful tool to numerically evaluate the usefulness of any constraints that are added

Deep Learning for Single-Molecule Science

Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in Machine Learning (ML), so-called Deep Learning (DL) offers an interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional Machine Learning strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the 'internal workings' of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a Convolutional Neural Network, may be used for base calling in DNA sequencing applications. We compare it with a Support Vector Machine as a more conventional ML method, and and discuss some of the strengths and weaknesses of the approach. In particular, a 'deep' neural network has many features of a 'black box', which has important implications on how we look at and interpret data

A Survey of Methods for Volumetric Scene Reconstruction from Photographs

Scene reconstruction, the task of generating a 3D model of a scene given multiple 2D photographs taken of the scene, is an old and difficult problem in computer vision. Since its introduction, scene reconstruction has found application in many fields, including robotics, virtual reality, and entertainment. Volumetric models are a natural choice for scene reconstruction. Three broad classes of volumetric reconstruction techniques have been developed based on geometric intersections, color consistency, and pair-wise matching. Some of these techniques have spawned a number of variations and undergone considerable refinement. This paper is a survey of techniques for volumetric scene reconstruction