Dilip Krishnan

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Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge(More)
Detecting boundaries between semantically meaningful objects in visual scenes is an important component of many vision algorithms. In this paper, we propose a novel method for detecting such boundaries based on a simple underlying principle: pixels belonging to the same object exhibit higher statistical dependencies than pixels belonging to different(More)
Photographs taken through a window are often compromised by dirt or rain present on the window surface. Common cases of this include pictures taken from inside a vehicle, or outdoor security cameras mounted inside a protective enclosure. At capture time, defocus can be used to remove the artifacts, but this relies on achieving a shallow depth-of-field and(More)
Camera flashes produce intrusive bursts of light that disturb or dazzle. We present a prototype camera and flash that uses infra-red and ultra-violet light mostly outside the visible range to capture pictures in low-light conditions. This "dark" flash is at least two orders of magnitude dimmer than conventional flashes for a comparable exposure. Building on(More)
The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their appeal, such models often fail to generalize from synthetic(More)
We present a new multi-level preconditioning scheme for discrete Poisson equations that arise in various computer graphics applications such as colorization, edge-preserving decomposition for two-dimensional images, and geodesic distances and diffusion on three-dimensional meshes. Our approach interleaves the selection of fine-and coarse-level variables(More)
We propose a framework that infers mid-level visual properties of an image by learning about ordinal relationships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal measurements are globalized to create a dense output map of continuous(More)
This paper proposes a new spatial-temporal Markov random field (MRF) model for the detection of missing data (also referred to as blotches) in image sequences. The blotches in noise-corrupted image sequences exhibit a temporal discontinuity characteristic that is commonly used for the detection of blotches. However, badly motion-compensated pixels also(More)