Browsing by Author "Purkait, P."

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A novel technique for sketch to photo synthesis
(2010) Purkait, P.; Chanda, B.; Kulkarni, S.
We propose a novel pseudo photo generation system from a sketch image. All the training images and the input test sketch are warped around its mean shape to get shape free images. Based on local geometry preserving manifold learning method called locally linear embedding (LLE), a shape free pseudo-photo is synthesized by block matching. Then the photo of actual shape is constructed with the help of an Active Shape Model(ASM) and a neural network built on shape control point coordinate pair for training sketch-photo samples. We experimented over 300 sketch-photo image pairs and output synthesized photo for every test image is encouraging. Â© 2010 ACM.
Face image retrieval based on probe sketch using SIFT feature descriptors
(2012) Rakesh, S.; Atal, K.; Arora, A.; Purkait, P.; Chanda, B.
This paper presents a feature-based method for matching facial sketch images to face photographs. Earlier approaches calculated descriptors over the whole image and used some transformation and matched them by some classifiers. We present an idea, where descriptors are calculated at selected discrete points (eyes, nose, ears...). This allows us to compare only prominent features. We use SIFT (Scale Invariant Feature Transform) to extract feature descriptors at the annotated points in the sketches and experiment with various methods to retrieve photos. Experimental results demonstrate appreciable matching performances using the presented feature-based methods at a low computational cost. � 2012 Springer-Verlag.
Face image retrieval based on probe sketch using SIFT feature descriptors
(2012) Rakesh, S.; Atal, K.; Arora, A.; Purkait, P.; Chanda, B.
This paper presents a feature-based method for matching facial sketch images to face photographs. Earlier approaches calculated descriptors over the whole image and used some transformation and matched them by some classifiers. We present an idea, where descriptors are calculated at selected discrete points (eyes, nose, ears...). This allows us to compare only prominent features. We use SIFT (Scale Invariant Feature Transform) to extract feature descriptors at the annotated points in the sketches and experiment with various methods to retrieve photos. Experimental results demonstrate appreciable matching performances using the presented feature-based methods at a low computational cost. Â© 2012 Springer-Verlag.
A novel technique for sketch to photo synthesis
(2010) Purkait, P.; Chanda, B.; Kulkarni, S.
We propose a novel pseudo photo generation system from a sketch image. All the training images and the input test sketch are warped around its mean shape to get shape free images. Based on local geometry preserving manifold learning method called locally linear embedding (LLE), a shape free pseudo-photo is synthesized by block matching. Then the photo of actual shape is constructed with the help of an Active Shape Model(ASM) and a neural network built on shape control point coordinate pair for training sketch-photo samples. We experimented over 300 sketch-photo image pairs and output synthesized photo for every test image is encouraging. � 2010 ACM.