Sorry, you need to enable JavaScript to visit this website.

Current object segmentation algorithms are based on the hypothesis that one has access to a very large amount of data. In this paper, we aim to segment objects using only tiny datasets. To this extent, we propose a new automatic part-based object segmentation algorithm for non-deformable and semi-deformable objects in natural backgrounds. We have developed a novel shape descriptor which models the local boundaries of an object's part. This shape descriptor is used in a bag-of-words approach for object detection.

In this paper, we present a novel image representation approach for classifying emphysema in computed tomography (CT) images of the lung. Our proposed method extends rotation invariant uniform local binary pattern (RIULBP) and local ternary pattern (LTP), which are extensively used in a variety of computer vision applications, into rotation invariant uniform local ternary pattern (RIULTP) with a human perception principle: Weber’s law.

We consider the task of removing the effect of camera shake during a long exposure. Technically, this is a blind deconvolution problem in which both the image and the motion blur have to be jointly inferred. Several algorithms have been proposed till date for removing camera shake that work with one or more images. However, most of these algorithms are computationally expensive and hence cannot be used in real-time.

In this paper, we propose a novel object proposal generation scheme by formulating a graph-based salient edge classification framework that utilizes the edge context. In the proposed method, we construct a Bayesian probabilistic edge map to assign a saliency value to the edgelets by exploiting low level edge features. A Conditional Random Field is then learned to effectively combine these features for edge classification with object/non-object label. We propose an objectness score for the generated windows by analyzing the salient edge density inside the bounding box.

Capturing more information, e.g. geometry and material, using optical cameras can greatly help the perception and understanding of complex scenes. This paper proposes a novel method to capture the spectral and light field information simultaneously. By using a delicately designed chromatic aberration enlarged camera, the spectral-varying slices at different depths of the scene can be easily captured.

In this paper, we propose a principal color component extraction
method that is simply performed by linear color composition (transformation)
of R, G, B colors, but its composite coefficients are calculated
so as to obtain a noisy-texture-less principal component of
RGB color images. Our method is related to principal component
analysis (PCA) and edge preserving smoothing by total variation
(TV) minimization. The resultant image becomes a principal color
component image with the minimum total variation. We show this

Mobile cameras have come a long way since their evolution and have replaced digital still cameras. However, their lowlight photography performance needs significant improvement. Dual camera systems consisting of a monochrome sensor and a Bayer sensor offer us a way to improve the low-light photography. The existing dual camera systems use post-processing methods after Image Signal Processor (ISP) for image fusion which are computationally intensive and use two ISPs. We propose a novel architecture in which the image fusion can be done in Bayer domain prior to the ISP.