Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

In this paper, the problem of depth estimation from single monocular image is considered. The depth cues such as motion, stereo correspondences are not present in single image which makes the task more challenging. We propose a machine learning based approach for extracting depth information from single image. The deep learning is used for extracting features, then, initial depths are generated using manifold learning in which neighborhood preserving embedding algorithm is used. Then, fixed point supervised learning is applied for sequential labeling to obtain more consistent and accurate depth maps. The features used are initial depths obtained from manifold learning and various image based features including texture, color and edges which provide useful information about depth. A fixed point contraction mapping function is generated using which depth map is predicted for new structured input image. The transfer learning approach is also used for improvement in learning in a new task through the transfer of knowledge from a related task that has already been learned. The predicted depth maps are reliable, accurate and very close to ground truth depths which is validated using objective measures: RMSE, PSNR, SSIM and subjective measure: MOS score. © 2016 ACM.

World University root node

ACM International Conference Proceeding Series

Depth estimation from single image using machine learning techniques

Performance of most of the recognition engines for document images is effected by quality of the image being processed and the selection of parameter values for the pre-processing algorithm. Usually the choice of such parameters is done empirically. In this paper, we propose a novel framework for automatic selection of optimal parameters for pre-processing algorithm by estimating the quality of the document image. Recognition accuracy can be used as a metric for document quality assessment. We learn filters that capture the script properties and degradation to predict recognition accuracy. An EM based framework has been formulated to iteratively learn optimal parameters for document image pre-processing. In the E-step, we estimate the expected accuracy using the current set of parameters and filters. In the M-step we compute parameters to maximize the expected recognition accuracy found in E-step. The experiments validate the efficacy of the proposed methodology for document image pre-processing applications. © 2016 IEEE.

Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016

Automatic Selection of Parameters for Document Image Enhancement Using Image Quality Assessment

In this paper we present a technique that estimates the Structure from Motion (SFM) in a recursive fashion. Traditionally successful SFM algorithms take the set of images and estimate the scene geometry and camera positions either using incremental algorithms or the global algorithms and do the refinement process [2] to reduce the reprojection error. In this work it is assumed that we don’t have complete image set at the start of the reconstruction process, unlike most of the traditional approaches present in the literature. It is assumed that the set of images come in at the regular intervals and we recursively perform the SFM on the incoming set of images and update the previously reconstructed structure with the structure estimated from the current set of images. The proposed system has been tested on two datasets which consist of 12 images and 60 images respectively and reconstructions obtained show the validity of our proposed technique. © Springer International Publishing AG 2017.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Recursive structure from motion

Speckle reduction is desired to improve the quality of ultrasound images. However, a uniform speckle reduction from the entire image results in loss of important information, especially in cardiac ultrasound images. In this paper, a tissue selective total variation regularization approach is proposed for the enhancement of cardiac ultrasound images. It measures the pixel probability of belonging to blood regions and uses it in the total variation framework. As a result, the unwanted speckle from the blood chamber regions is removed and the useful speckle in the tissue regions is preserved. This helps to improve the visible contrast of the images and enhances the structural details. The proposed approach is evaluated using synthetic as well as real images. A better performance is observed as compared to the state-of-the-art filters in terms of speckle region’s signal to noise ratio, structural similarity measure index, figure of merit, and mean square error. © Springer International Publishing AG 2017.

Cardiac ultrasound image enhancement using tissue selective total variation regularization

Information retrieval research has shown significant improvement and provided techniques that retrieve documents in image or text form. However, retrieval of multi-modal documents has been given very less attention. We aim to build a system for retrieval of documents with embedded information graphics (Info-graphics). Info-graphics are images of bar charts and line graphs appearing with textual components in magazines, newspapers, and journals. In this paper, we present multi-modal document image retrieval framework by learning an optimal fusion of information from text and info-graphics regions. The evaluation of the proposed concept is demonstrated on documents collected from various sources such as magazines and journals. © Springer International Publishing AG 2017.

Info-graphics retrieval: A multi-kernel distance based hashing scheme

“What is the state-of-the-art in sketch recognition and what are some important future research directions in matching sketches with digital face images?” This opinion paper focuses on answering these questions through proposing three important steps that need to move the field forward: (i) create a large, real world forensic sketch database, (ii) develop fundamental understanding of human cognition of processing sketches, and (iii) develop improved algorithms for matching sketches with mugshot photos. © 2016 Elsevier B.V.

Image and Vision Computing

Sketch Recognition: What Lies Ahead?

Face detection is a challenging problem having a wide range of security and surveillance-based applications. Skin color can be an important differentiator and used to augment the performance of automatic face detection. However, obtaining skin color consistency across illumination, different camera settings, and diverse ethnicity is a challenging task. Static skin color models that rely on image preprocessing are able to bring only limited consistency. Their performance in terms of accuracy and computation time degrades severely in real-world videos. In this paper, we study the dynamics of different color models on a database of five videos containing more than 93,000 manually annotated face images. Further, we propose an adaptive skin color model to reduce the false accept cases of Adaboost face detector. Since the face color distribution model is regularly updated using previous Adaboost responses, we find the system to be more effective to real-world environmental covariates. Importantly, the adaptive nature of the skin classifier does not significantly affect the computation time. © Springer India 2016.

Advances in Intelligent Systems and Computing

Adaptive skin color model to improve video face detection

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. However, similar to other biometric modalities, iris recognition systems are also vulnerable to presentation attacks (commonly called spoofing) that attempt to conceal or impersonate identity. Examples of typical iris spoofing attacks are printed iris images, textured contact lenses, and synthetic creation of iris images. It is critical to note that majority of the algorithms proposed in the literature are trained to handle a specific type of spoofing attack. These algorithms usually perform very well on that particular attack. However, in real-world applications, an attacker may perform different spoofing attacks. In such a case, the problem becomes more challenging due to inherent variations in different attacks. In this paper, we focus on a medley of iris spoofing attacks and present a unified framework for detecting such attacks. We propose a novel structural and textural feature based iris spoofing detection framework (DESIST). Multi-order dense Zernike moments are calculated across the iris image which encode variations in structure of the iris image. Local Binary Pattern with Variance (LBPV) is utilized for representing textural changes in a spoofed iris image. The highest classification accuracy of 82.20% is observed by the proposed framework for detecting normal and spoofed iris images on a combined iris spoofing database. © 2016 IEEE.

2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016

Detecting medley of iris spoofing attacks using DESIST

Biometric-based authentication for smart handheld devices promises to provide a reliable and alternate security mechanism compared to traditional methods such as pins, patterns, and passwords. Although fingerprints are a viable source for authentication, they generally require installation of an additional hardware such as optical and swipe sensors on mobile devices, and are only available in expensive, high-end smartphones. Alternatively, fingerphoto images captured using the smartphone camera for authentication is one of the promising biometric approaches. However, using fingerphotos for authentication brings along a major challenge of fingerphoto spoofing. This research is aimed at understanding the effect of spoofing on fingerphotos. There are three major contributions of this research: (i) create a large spoofed fingerphoto database and make it publicly available for research, (ii) to establish the effect of print attack and photo attack in fingerphoto spoofing, and (iii) understand the performance of existing spoofing detection algorithms on fingerphoto spoofing. © 2016 IEEE.

Fingerphoto spoofing in mobile devices: A preliminary study

In thermal face detection, researchers have generally assumed manual face detection or have designed algorithms that focus on indoor environment. However, facial properties are dependent on body temperature, surrounding environment, and any accessories or occlusion present on the face. For instance, the presence of scarfs, glasses, or any disguise accessories will alter the emitted heat pattern, thereby making it challenging to detect the face in thermal images. Similarly, daytime outdoor image acquisition has certain effects on the heat pattern compared to nighttime (or indoor controlled) image acquisition settings that affect automatic face detection performance. In this research, we provide a thorough understanding of challenges in thermal face detection along with an experimental evaluation of traditional approaches. Further, we adapt the AdaBoost face detector to yield improved performance on face detection in thermal images in both indoor and outdoor environments. We also propose a region of interest selection approach designed specifically for aiding occluded/disguised thermal face detection. Experiments are performed on the Notre Dame thermal face database as well as the IIITD databases that include variations such as disguise, age, and environmental (day/night) factors. The results suggest that while thermal face detection in semi-controlled environments is relatively easy, occlusion and disguise are challenges that require further attention. © Springer International Publishing Switzerland 2016.