Maneesh Kumar Singh

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Two novel methods are proposed for robust segmentation of pulmonary nodules in CT images. The proposed solutions locate and segment a nodule in a semi-automatic fashion with a marker indicating the target. The solutions are motivated for handling the difficulty to segment juxtapleural, or wall-attached, nodules by using only local information without a(More)
To identify Binteresting[ images, human observers view 10 images/sec, while electroencephalography (EEG) signals from the observers own brains are automatically decoded. ABSTRACT | Our society's information technology advancements have resulted in the increasingly problematic issue of information overloadVi.e., we have more access to information than we can(More)
We consider the problem of segmentation of images that can be modelled as piecewise continuous signals having unknown , non-stationary statistics. We propose a solution to this problem which first uses a regression framework to estimate the image PDF, and then mean-shift to find the modes of this PDF. The segmentation follows from mode identification(More)
Predicate logic based reasoning approaches provide a means of formally specifying domain knowledge and manipulating symbolic information to explicitly reason about different concepts of interest. Extension of traditional binary predicate logics with the bilattice formalism permits the handling of uncertainty in reasoning, thereby facilitating their(More)
Models of spatial variation in images are central to a large number of low-level computer vision problems including segmentation, registration, and 3D structure detection. Often, images are represented using parametric models to characterize (noise-free) image variation, and, additive noise. However, the noise model may be unknown and para-metric models may(More)
Challenges to accurate registration come from three factors —presence of background clutter, occlusion of the pattern being registered and changes in feature values across images. To address these concerns, we propose a robust probabilistic estimation approach pred-icated on representations of the object model and the target image using a kernel density(More)
Robustness to illumination variations is a key requirement for the problem of change detection which in turn is a fundamental building block for many visual surveillance applications. The use of ordinal measures is a powerful way of filtering out illumination dependency in representing appearance , and several such measures have been proposed in the past(More)
We present a change detection method resistant to global and local illumination variations for use in visual surveillance scenarios. Approaches designed thus far for ro-bustness to illumination change are generally based either on color normalization, texture (e.g. edges, rank order statistics, etc.), or illumination compensation. Normaliza-tion based(More)
This paper proposes a new variational bound optimization framework for incorporating spatial prior information to the mean shift-based data-driven mode analysis, offering flexible control of the mean shift convergence. Two forms of Gaussian spatial priors are considered. Attractive prior pulls the convergence toward a desired location. Repulsive prior(More)