Kiran Nanjunda Iyer

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Conventional Image Retargeting methods aim to preserve the salient regions in an image using As Similar as Possible (ASAP) energy formulation or As Rigid as Possible (ARAP) energy formulation. ASAP energy formulation preserves the shape of the salient object while the scale of salient object can get distorted in the retargeted image. On the contrary, ARAP(More)
We propose a method for interactive object segmentation using a single touch provided on the foreground object. The `extent' of the foreground object is estimated by a random walk technique designed on the salient edge representation of the image. The final image segmentation is performed under graph-cut framework. The accuracy of proposed method is(More)
Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to(More)
Deep hierarchical models for feature learning have emerged as an effective technique for object representation and classification in recent years. Though the features learnt using deep models have shown lot of promise towards achieving invariance to data transformations, this primarily comes at the expense of using much larger training data and model size.(More)
This paper proposes a novel approach for real-time video summarization on mobile using Dictionary Learning, Global Camera Motion analysis and Colorfulness. A dictionary is represented as a distinct set of events that are described as spatio-temporal features. Uniqueness measure is predicted based on the correlation scores of the dictionary elements whereas(More)
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