Browsing by Author "Gupta, Neetika"

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An Experimental Study of Markerless Image Registration Methods on Varying Quality of Images for Augmented Reality Applications
(ACM Digital Library, 2017-10) Rohil, Mukesh Kumar; Gupta, Neetika
Robustness of Augmented Reality (AR) applications depends heavily on image registration procedures. The registration process in AR either make use of manually placed markers in the scene or use natural features extracted from the scene. These markers or detected features estimate the correct position of virtual objects that are to be integrated with the view of real environment. Use of markers for this purpose have only limited applicability. Therefore, for incorporating AR in a wide variety of applications, there is a need for detecting affine invariant and stable natural features from an image. This paper presents a comparative study of six feature detectors, which could be used for the 3D registration process in AR. These feature detectors are applied on 48 images (eight image-sets with six images in each set) varying in terms of change of viewpoint, scale, blur, illumination and compression ratio. Detectors chosen for comparison are Harris-Affine, Hessian-Affine, MSER, SIFT, ASIFT and SURF. The novelty of the work done is the usage of image quality metrics and Pearson Coefficient to study the traits of number of detected keypoints in an image and number of matches between two images with respect to image quality. RANSAC algorithm is used for determining correct number of correspondences between two images by eliminating outliers. Experimental results show that in most cases, performance of feature detectors could be correlated with the quality of images used for experimentation. The comparative evaluation of feature detectors is also done with respect to computational complexity to reason detector's applicability or inapplicability in an AR system.
Exploring Possible Applications of Augmented Reality in Education
(IEEE, 2017) Rohil, Mukesh Kumar; Gupta, Neetika
In recent years, use of Augmented Reality (AR) has been explored in a wide range of applications in various fields. Among these, one of the most inquired is the field of education. Many of these studies have shown that the use of AR in any learning environment promotes critical thinking, better understanding and motivates the learner for further studies. This is due to the real-time experience that AR brings with it to the learners. This paper reports some of the recent studies that list positive and negative impact of AR in an educational setting and how beneficial the employment of this technology is. The paper also gives an overview of some of the possible and promising applications of AR in the fields of science, social science, mathematics and language. Furthermore, the paper discusses trends and the vision towards future opportunities for possible research in augmented reality for education.
Image feature detection using an improved implementation of maximally stable extremal regions for augmented reality applications
(Taylor & Francis, 2017-05) Rohil, Mukesh Kumar; Gupta, Neetika
Augmented Reality for all practical purposes requires extensive computation, accurate view alignment and real-time performance. To address some of these limitations, an improved method of feature detection is proposed using Maximally Stable Extremal Regions. The approach used for feature detection extracts the regions of interest using a true flood fill approach for building and maintaining the component tree. This approach has true worst-case linear complexity (Linear-MSER). In the present work, Linear-MSER is implemented at multiple scales of an image in order to increase the affine invariance properties of the detector (MSLinear-MSER). The two detectors, Linear-MSER and MSLinear-MSER, are then combined separately with Scale Invariant Feature Transform and Speeded-Up Robust Feature descriptors for performance comparison. Performance evaluation is done under varying imaging conditions like changes in viewpoint, scale, blur, illumination and JPEG compression. Results show that, MSLinear-MSER+SIFT performs best in terms of time-complexity and number of keypoint matches when executed at six octaves and five levels. This observation is true for all image-sets taken into consideration, containing images that are affine transformed in one way or other. To exhibit the efficiency of MSLinear-MSER+SIFT, a prototype of an AR system is also developed and discussed in this article using this approach.
An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis
(Springer, 2019-08) Rohil, Mukesh Kumar; Gupta, Neetika
No-reference image quality assessment (NR-IQA) uses only the test image for its quality assessment, and as video is essentially comprised of image frames with additional temporal dimension, video quality assessment (VQA) requires a thorough understanding of image quality assessment metrics and models. Therefore, in order to identify features that deteriorate video quality, a fundamental analysis of spatial and temporal artifacts with respect to individual video frames needs to be performed. Existing IQA and VQA metrics are primarily for capturing few distortions and hence may not be good for all types of images and videos. In this paper, we propose an NR-IQA model by combining existing three methods (namely NIQE, BRISQUE and BLIINDS-II) using multi-linear regression. We also present a holistic no-reference video quality assessment (NR-VQA) model by exploring quantification of certain distortions like ringing, frame difference, blocking, clipping and contrast in video frames. For the proposed NR-IQA model, the results represent improved performance as compared to the state-of-the-art methods and it requires very low fraction of samples for training to provide a consistent accuracy over different training-to-testing ratios. The performance of NR-VQA model is examined using a simple neural network model to attain high value of goodness of fit.
Robust Image Registration Methods for Varying Image Quality and Imaging Conditions in Augmented Reality Systems
(BITS, Pilani, 2019) Gupta, Neetika