BACKGROUND: Various chromatin modifications, identified in large-scale epigenomic analyses, are associated with distinct phenotypes of different cells and disease phases. To improve our understanding of these variations, many computational methods have been developed to discover novel sites and cell-specific chromatin modifications. Despite the availability of existing methods, there is still room for further improvement when they are applied to resolve the histone code hypothesis. Hence, we aim to investigate the development of a computational method to provide new insights into de novo combinatorial pattern discovery of chromatin modifications to characterize epigenetic variations in distinct phenotypes of different cells. RESULTS: We report a new computational approach, ChARM (Combinatorial Chromatin Modification Patterns using Association Rule Mining), that can be employed for the discovery of de novo combinatorial patterns of differential chromatin modifications. We used ChARM to analyse chromatin modification data from the livers of normal (non-cancerous) mice and hepatitis B virus X (HBx)-transgenic mice with hepatocellular carcinoma, and discovered 2,409 association rules representing combinatorial chromatin modification patterns. Among these, the combination of three histone modifications, a loss of H3K4Me3 and gains of H3K27Me3 and H3K36Me3, was the most striking pattern associated with the cancer. This pattern was enriched in functional elements of the mouse genome such as promoters, coding exons and 5'UTR with high CpG content, and CpG islands. It also showed strong correlations with polymerase activity at promoters and DNA methylation levels at gene bodies. We found that 30 % of the genes associated with the pattern were differentially expressed in the HBx compared to the normal, and 78.9 % of these genes were down-regulated. The significant canonical pathways (Wnt/ß-catenin, cAMP, Ras, and Notch signalling) that were enriched in the pattern could account for the pathogenesis of HBx. CONCLUSIONS: ChARM, an unsupervised method for discovering combinatorial chromatin modification patterns, can identify histone modifications that occur globally. ChARM provides a scalable framework that can easily be applied to find various levels of combination patterns, which should reflect a range of globally common to locally rare chromatin modifications.

Image deblurring to remove blur caused by camera shake has been intensively studied. Nevertheless, most methods are brittle and computationally expensive. In this paper we analyze multi-image approaches, which capture and combine multiple frames in order to make deblurring more robust and tractable. In particular, we compare the performance of two approaches: align-and-average and multi-image deconvolution. Our deconvolution is non-blind, using a blur model obtained from real camera motion as measured by a gyroscope. We show that in most situations such deconvolution outperforms align-and-average. We also show, perhaps surprisingly, that deconvolution does not benefit from increasing exposure time beyond a certain threshold. To demonstrate the effectiveness and efficiency of our method, we apply it to still-resolution imagery of natural scenes captured using a mobile camera with flexible camera control and an attached gyroscope.

Computational photography techniques overcome limitations of traditional image sensors such as dynamic range and noise. Many computational imaging techniques have been proposed that process image stacks acquired using different exposure, aperture or gain settings, but far less attention has been paid to determining the parameters of the stack automatically. In this paper, we propose a novel computational imaging system that automatically and efficiently computes the optimal number of shots and corresponding exposure times and gains, taking into account characteristics of the scene and sensor. Our technique seamlessly integrates the use of multiple capture for both High Dynamic Range (HDR) imaging and denoising. The acquired images are then aligned, warped and merged in the raw Bayer domain according to a statistical noise model of the sensor to produce an optimal, potentially HDR and denoised image. The result is a fully automatic camera that constantly monitors the scene in front of it and decides how many images are required to capture it, without requiring the user to explicitly switch between different capture modalities.

The FCam API is an open-source camera control library, enabling precise control over a camera's imaging pipeline. Intended for researchers and students in the field of computational photography, it allows easy implementation of novel algorithms and applications. Currently implemented on the Nokia N900 smartphone, and a custom-built "Frankencamera", it has been used in teaching at universities around the world, and is freely available for download for the N900. This paper describes the architecture underlying the API, the design of the API itself, several applications built on top of it, and some examples of its use in education.

Many natural scenes have a dynamic range that is larger than the dynamic range of a camera's image sensor. A popular approach to producing an image without under- and over-exposed areas is to capture several input images with varying exposure settings, and later merge them into a single high-quality result using offline image processing software. In this paper, we describe a system for creating images of high-dynamic-range (HDR) scenes that operates entirely on a mobile camera. Our system consists of an automatic HDR metering algorithm that determines which exposures to capture, a video-rate viewfinder preview algorithm that allows the user to verify the dynamic range that will be recorded, and a light-weight image merging algorithm that computes a high-quality result directly on the camera. By using our system, a photographer can capture, view, and share images of HDR scenes directly on the camera, without using offline image processing software.

Many natural scenes have a dynamic range that is larger than the dynamic range of a camera's image sensor. A popular approach to producing an image without under- and over-exposed areas is to capture several input images with varying exposure settings, and later merge them into a single high-quality result using offline image processing software. We present a system for creating images of high-dynamic-range (HDR) scenes that operates entirely on a mobile camera. Our system consists of an automatic HDR metering algorithm that determines which exposures to capture, a video-rate viewfinder preview algorithm that allows the user to verify the dynamic range that will be recorded, and a light-weight image merging algorithm that computes a high-quality result directly on the camera. By using our system, a photographer can capture, view, and share images of HDR scenes directly on camera, without using offline image processing software

Although there has been much interest in computational photography within the research and photography communities, progress has been hampered by the lack of a portable, programmable camera with sufficient image quality and computing power. To address this problem, we have designed and implemented an open architecture and API for such cameras: the Frankencamera. It consists of a base hardware specification, a software stack based on Linux, and an API for C++. Our architecture permits control and synchronization of the sensor and image processing pipeline at the microsecond time scale, as well as the ability to incorporate and synchronize external hardware like lenses and flashes. This paper specifies our architecture and API, and it describes two reference implementations we have built. Using these implementations we demonstrate six computational photography applications: HDR viewfinding and capture, low-light viewfinding and capture, automated acquisition of extended dynamic range panoramas, foveal imaging, IMU-based hand shake detection, and rephotography. Our goal is to standardize the architecture and distribute Frankencameras to researchers and students, as a step towards creating a community of photographer-programmers who develop algorithms, applications, and hardware for computational cameras.