Dilip K. Prasad

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Color constancy is a well-studied topic in color vision. Methods are generally categorized as (1) low-level statistical methods, (2) gamut-based methods, and (3) learning-based methods. In this work, we distinguish methods depending on whether they work directly from color values (i.e., color domain) or from values obtained from the image's spatial(More)
In this paper, we propose a novel ellipse detection method for real images. The proposed method uses the information of edge curvature and their convexity in relation to other edge contours as clues for identifying edge contours that can be grouped together. A search region is computed for every edge contour that contains other edge contours eligible for(More)
This paper focuses on a training-based method to reconstruct a scene’s spectral reflectance from a single RGB image captured by a camera with known spectral response. In particular, we explore a new strategy to use training images to model the mapping between cameraspecific RGB values and scene reflectance spectra. Our method is based on a radial basis(More)
Many geometric methods have been used extensively for detection of ellipses in images. Though the geometric methods have rigorous mathematical framework, the effect of quantization appears in various forms and introduces errors in the implementation of such models. This unexplored aspect of geometric methods is studied in this paper. We identify the various(More)
A novel ellipse fitting method which is selective for digital and noisy elliptic curves is proposed in this paper. The method aims at fitting an ellipse only when the data points are highly likely belong to an ellipse. This is achieved using the geometric distances of the ellipse from the data points. The proposed method models the non-linear problem of(More)
Object detection and recognition are important problems in computer vision. Since these problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real-time, and dynamic object detection/recognition methods are still unavailable. We propose a new object detection/recognition method, which improves over the existing methods in(More)
Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods(More)
Many ellipse detection algorithms produce multiple elliptic hypotheses corresponding to a single elliptic object. Thus, it is needed to identify similar ellipses that possibly belong to the same object and cluster them as a single object. This will reduce the computational and memory requirement for further higher level processing of ellipse detection(More)
Approximating digital curves using polygonal approximations is required in many image processing applications [Kolesnikov & Fränti, 2003, 2005; Lavallee & Szeliski, 1995; Leung, 1990; Mokhtarian & Mackworth, 1986; Prasad, et al., 2011; Prasad & Leung, 2010a, 2010b; Prasad & Leung, 2010; Prasad & Leung, 2012; Prasad, et al., 2011a]. Such representation is(More)