MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We describe an auto-calibrated 3D perspective-corrected spherical display that uses multiple rear projected pico-projectors. The display system is auto-calibrated via 3D reconstruction of each projected pixel on the display using a single inexpensive camera. With the automatic calibration, the multiple-projector system supports a seamless blended imagery on the spherical screen. Furthermore, we incorporate head tracking with the display to present 3D content with motion parallax by rendering perspective-corrected images based on the viewpoint. To show the effectiveness of this design, we implemented a view-dependent application that allows walk-around visualization from all angles for a single head-tracked user. We also implemented a view-independent application that supports a wall-papered rendering for multi-user viewing. Thus, both view-dependent 3D VR content and spherical 2D content, such as a globe, can be easily experienced with this display.

We describe a novel automatic calibration method using a single camera for a multiple-projector spherical Fish Tank Virtual Reality (FTVR) display. Modeling the projector as an inverse camera, we estimate the intrinsic and extrinsic projector parameters automatically using a set of projected images on the spherical screen. A calibrated camera is placed beneath to observe partially visible projected patterns. Using the correspondence between the observed pattern and the projected pattern, we reconstruct the shape of the spherical display and finally recover the 3D position of each projected pixel on the display. Additionally we present a practical calibration evaluation method that estimates on-surface accuracy using the single camera. We use point mismatch as a metric to describe misalignment and line mismatch to describe distortion. We demonstrate our automatic approach can achieve an on-surface point mismatch less than 1mm and line mismatch less than 1 on a 30cm diameter spherical screen. Taken together, our calibration approach and evaluation method are automatic and accurate for a desktop spherical FTVR and can be applied to other multiple-projector displays with curved screens.

In this poster we present Portable-Spheree, an interactive spherical rear-projected 3D-content-display that provides perspective-corrected views according to the user's head position, to provide parallax, shading and occlusion depth cues. Portable-Spheree is an evolution of the Spheree and it is developed in a smaller form factor, using more projectors and a dark-translucent screen with increased contrast. We present some preliminary results of this new configuration as well as applications with spatial interaction that might benefit from this new form factor.

We present an investigation of two different visualizations of video history: Video Timeline and Video Tiles. Video Timeline extends the commonly employed list-based visualization for navigation history by applying size to indicate heuristics and occupying the full screen with a two-sided timeline. Video Tiles visualizes history items in a grid-based layout by following pre-defined templates based on items' heuristics and ordering, utilizing screen space more effectively at the expense of a clearer temporal location. The visualizations are compared against the state-of-the-art method (a filmstrip-based visualization), with ten participants tasked with sharing their previously-seen affective intervals. Our study shows that our visualizations are perceived as intuitive and both outperform and are strongly preferred to the current method. Based on these results, Video Timeline and Video Tiles provide an effective addition to video viewers to help manage the growing quantity of video. They provide users with insight into their navigation patterns, allowing them to quickly find previously-seen intervals, leading to efficient clip sharing, simpler authoring and video summarization.

Human pose detection is the foundation for many applications, particularly those using gestures as part of a natural user interface. We introduce a novel task-based control method for human pose detection, encoding specialist knowledge in a descriptive abstraction for application by non-experts, such as developers, artists and students. The abstraction hides the details of a set of algorithms which specialise either in different estimations of pose (e.g. articulated, body part) or under different conditions (e.g. occlusion, clutter). Users describe the conditions of their problem, which is used to select the most suitable algorithm (and automatically set up the parameters). The task-based control is evaluated with images described using the abstraction. Expected outcomes are compared to results and demonstrate that describing the conditions is sufficient to allow the abstraction to produce the required result.

Research into computer vision techniques has far outpaced the development of interfaces (such as APIs) to support the techniques' accessibility, especially to developers who are not experts in the field. We present a new description-based interface designed to be mainstream-developer-friendly while retaining sufficient power and flexibility to solve a wide variety of computer vision problems. The interface presents vision at the task level (hiding algorithmic detail) and uses a task-based description derived from definitions of vision problems. We show that after interpretation, the description can be used to invoke an appropriate method to provide the developer's requested result. Our implementation interprets the description and invokes various vision methods with automatically derived parameters, which we demonstrate on a range of tasks.

This paper introduces a system that automatically classifies image pairs based on the type of registration required to align them. The system uses support vector machines to classify between panoramas, high-dynamic-range images, focal stacks, super-resolution, and unrelated image pairs. A feature vector was developed to describe the images, and 1100 pairs were used to train and test the system with 5-fold cross validation. The system is able to classify the desired registration application using a 1: Many classifier with an accuracy of 91.18%. Similarly 1:1 classifiers were developed for each class with classification rates as follows: Panorama image pairs are classified at 93.15%, high-dynamic-range pairs at 97.56%, focal stack pairs at 95.68%, super-resolution pairs at 99.25%, and finally unrelated image pairs at 95.79%. An investigation into feature importance outlines the utility of each feature individually. In addition, the invariance of the classification system towards the size of the image used to calculate the feature vector was explored. The classification of our system remains level at ~91% until the image size is scaled to 10% (150 × 100 pixels), suggesting that our feature vector is image size invariant within this range.

We are addressing two aspects of vision-based system development that are not fully exploited in current frameworks: abstraction over low-level details and high-level module reusability. Through an evaluation of existing frameworks, we relate these shortcomings to the lack of systematic classification of sub-tasks in vision-based system development. In this paper we present our work-in-progress which addresses these two issues by classifying vision into decoupled sub-tasks, hence defining a clear scope for a vision-based system development framework and its sub-components. Firstly, we decompose the task of vision system development into data management and processing. We then proceed to further decompose data management into three components: data access, conversion and transportation. We present the Vision Utility (VU) framework which provides abstraction over the vision system data management and verify this approach through an example vision system.

We introduce a work-in-progress camera access scheme we call the unified camera framework. Attempts have been made in the past to provide simple access to cameras, however these are generally OS specific or lacking in functionality. We present a novel interface which works across operating systems, and provides access to native images through a descriptor. A unified configuration model is presented to allow manipulation of camera parameters to the level each camera supports. Validation of the ideas presented is given in the form of a proof-of-concept implementation called the all seeing eye.

This paper presents a novel volumetric reconstruction technique that combines shape-from-silhouette with stereo photo-consistency in a global optimisation that enforces feature constraints across multiple views. Human shape reconstruction is considered where extended regions of uniform appearance, complex self-occlusions and sparse feature cues represent a challenging problem for conventional reconstruction techniques. A unified approach is introduced to first reconstruct the occluding contours and left-right consistent edge contours in a scene and then incorporate these contour constraints in a global surface optimisation using graph-cuts. The proposed technique maximises photo-consistency on the surface, while satisfying silhouette constraints to provide shape in the presence of uniform surface appearance and edge feature constraints to align key image features across views. 1