International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Computer science & Authentication. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

A Bag of Words approach for discriminating between retinal images containing exudates or drusen

Abstract: Population screening for sight threatening diseases based on fundus imaging is in place or being considered worldwide. Most existing programs are focussed on a specific disease and are based on manual reading of images, though automated image analysis based solutions are being developed. Exudates and drusen are bright lesions which indicate very different diseases, but can appear to be similar. Discriminating between them is of interest to increase screening performance. In this paper, we present a Bag of Words approach which can be used to design a system that can play the dual role of content based retrieval (of images with exudates or drusen) system and a decision support system to address the problem of bright lesion discrimination. The approach consists of a novel partitioning of an image into patches from which color, texture, edge and granulometry based features are extracted to build a dictionary. A bag of Words approach is then employed to help retrieve images matching a query image as well as derive a decision on the type of bright lesion in the given (query) image. This approach has been implemented and tested on a combination of public and local dataset of 415 images. The area under the curve for image classification is 0.90 and retrieved precision is 0.76.

FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks.

Abstract: Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a degraded image requires denoising and inpainting. We propose to address these problems with an end-to-end trainable Convolutional Neural Network based architecture called FPD-M-net, by posing the fingerprint denoising and inpainting problem as a segmentation (foreground) task. Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background. Our method outperforms the baseline method and achieves an overall 3rd rank in the Chalearn LAP Inpainting Competition Track 3—Fingerprint Denoising and Inpainting, ECCV 2018.

Video retrieval by mimicking poses

Abstract: We describe a method for real time video retrieval where the task is to match the 2D human pose of a query. A user can form a query by (i) interactively controlling a stickman on a web based GUI, (ii) uploading an image of the desired pose, or (iii) using the Kinect and acting out the query himself. The method is scalable and is applied to a dataset of 18 films totaling more than three million frames. The real time performance is achieved by searching for approximate nearest neighbors to the query using a random forest of K-D trees. Apart from the query modalities, we introduce two other areas of novelty. First, we show that pose retrieval can proceed using a low dimensional representation. Second, we show that the precision of the results can be improved substantially by combining the outputs of independent human pose estimation algorithms. The performance of the system is assessed quantitatively over a range of pose queries.

A behavioral study of emotions in south indian classical music andits implications in music recommendation systems

Abstract: In order to model a culture-specific content-based music recommendatio/n system, a total of 750 subjective emotional responses to tunes composed in popular raagas of South Indian classical (Carnatic) music are empirically investigated to find out the long speculated relation between raagas (indian music scales) and rasas (emotion clusters). We discuss the results from analysis of this survey, which show that raagas are quite useful as a first step in a different direction towards content-based music recommendation. Along the way, we discriminate Carnatic and North-Indian classical (Hindustani) music traditions. We also convey the definition of rasa, which is different from being a single emotional state. We use a classification based on a novel approach in conceptualization of emotions based on navarasa, which is a emotion classification given by Bharata, that suits behavioral studies with Indian arts. Pitch-class profiles which were previously shown to give high accuracies in Hindustani raaga recognition are tested in a preliminary experiment to automatically recognize Carnatic raagas. The results are discussed and additional challenges in dealing with melodies in Carnatic tradition are highlighted.