&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

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

Compression of multimedia content is an important processing step and backbone of real life applications in terms of optimum resource utilization in transmission and storage. It is an established field of research with very little scope for further improvement in achieved compression through customary coding-based compression techniques. Consequently, non-customary compression methods have become an important area for future research. Based on the principle ‘Any information that can be restored can be compressed’, we propose a novel spectrum-based image compression technique to further reduce the data footprint with satisfactory quality metric for images. We first blur the image with a point spread function (PSF) determined using frequency content of the given image. Blurring increases the DC component in the image, which in turn gets further compressed compared with original image by DCT-based JPEG compression. To recover the image, we perform deconvolution using the known blur PSF. Results obtained show further improvement of 20 - 30 % in achieved compression with respect to original JPEG compressed image with satisfactory quality of recovered image. © 2020, Indian Academy of Sciences.

Department of Electrical Engineering

World University root node

Sadhana - Academy Proceedings in Engineering Sciences

A novel non-customary method of image compression based on image spectrum

Conventionally, the point spread function (PSF) is understood as a characteristic function of any optical system. It captures the information about the amount of blur present along all the directions for a point in the scene. However, the dependence of blur on the PSF is in the form of convolution for any object other than a point source present in the scene and hence their relationship is less explicit. The authors propose a blur parameter locus curve (BPLC) as a system representation which has a one to one relationship with blur. BPLC simply is a chart of blur amounts in all directions of a given PSF with respect to the selected measurement function. They further characterise the PSF by decomposing the variation of BPLC across all directions based on the study performed for different possible forms of the blur kernels. Such decomposition provides powerful tools for various analysis. As PSF can be anisotropic, the computation of BPLC becomes an essential intermediate step to obtain the scale map as at the same scale, blur is different in different directions. Furthermore, they demonstrate the use of BPLC to obtain other system characteristics function such as PSF. © The Institution of Engineering and Technology 2019.

Fulltext

IET Image Processing

Blur parameter locus curve and its applications

The occurrence of motion blur along with defocus blur is a common phenomena in natural images. Usually, these blurs are spatially varying in nature for any general image and estimation of one type of blur is affected by presence of other. In this paper, we propose a novel method to estimate the concurrent defocus and motion blurs in a single image. Unlike the recent methods, which perform well only on simulated conditions or in presence of single type of blur, proposed method works well for real images as well as for compressed images. In this paper, we consider only commonly associated motion and defocus blurs for analysis. Decoupling of motion and defocus blur provides a fundamental tool that can be used for various analysis and applications. © 1991-2012 IEEE.

IEEE Transactions on Circuits and Systems for Video Technology

Simultaneous estimation of defocus and motion blurs from single image using equivalent gaussian representation

Defocus based Depth estimation has been widely applied for constructing 3D setup from 2D image(s), reconstructing 3D scenes and image refocusing. Using defocus enables us to infer depth information from a single image using visual clues which can be captured by a monocular camera. In this paper, we propose an application of Depth from Defocus to a novel, portable keyboard design. Our estimation technique is based on the concept that depth of the finger with respect to our camera and its defocus blur value is correlated, and a map can be obtained to detect the finger position accurately. We have utilised the near-focus region for our design, assuming that the closer an object is to our camera, more will be its defocus blur. The proposed keyboard can be integrated with smartphones, tablets and Personal Computers, and only requires printing on plain paper or projection on a flat surface. The detection approach involves tracking the finger’s position as the user types, measuring its defocus value when a key is pressed, and mapping the measured defocus together with a precalibrated relation between the defocus amount and the keyboard pattern. This is utilised to infer the finger’s depth, which, along with the azimuth position of the stroke, identifies the pressed key. Our minimalistic design only requires a monocular camera, and there is no need for any external hardware. This makes the proposed approach a cost-effective and feasible solution for a portable keyboard. © 2020, Springer Nature Switzerland AG.

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

A Defocus Based Novel Keyboard Design

Dehazing is an important process as it can significantly improve the performance of computer vision applications in outdoor environments. The two main requirements that an online dehazing system demands are low processing time and high visual range. The authors present a novel dehazing algorithm based on colour uniformity principle (CUP) which meets the desired requirements of a realtime implementation. Estimation of atmospheric scattering parameter and transmission map forms the key step in dehazing problem. At first, the authors use CUP to generate the transmission map and refine it further by Fast Guided Filter. They estimate the atmospheric scattering parameter with the help of the estimated transmission map. Experimental results show that the quality of dehazed output, produced in real-time using the proposed method, is comparable with the results achieved by the state of the art techniques. The proposed dehazing method produces reliable dehazed output in varying haze conditions, unlike current methods. © The Institution of Engineering and Technology 2019.

Realtime dehazing using colour uniformity principle

Compression is an essential processing step for multimedia storage, distribution and transmission in terms of optimum resource utilization. Non-customary compression methods have become an important area for research due saturation of traditional coding based compression techniques. In this paper, we propose a novel spectrum based compression technique to further reduce the data footprint with satisfactory quality metric for the cases of video. Proposed method is based on principle that 'Any information that can be restored, can be compressed'. We blur the individual frames with a given Point Spread Function (PSF) to increase the DC component and compress using standard MPEG coder. To recover the video, we perform frame by frame deconvolution using the known blur PSF. Results obtained show further improvement of 20 to 30% in achieved compression with respect to original MPEG compressed video with satisfactory quality of recovered input. © 2019 IEEE.

GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings

A novel blurring based method for video compression

Focal Plane Ambiguity (FPA) is a fundamental limitation of the Depth from Defocus (DFD) technique and refers to ambiguity of two possible distances corresponding to a single defocus blur value. Since, mix-sided scenes exist frequently in images and image-sequences, the assumption of a one sided focused scene often does not hold true. This leads to errors in the estimated defocus map. However, the inherent ordering of defocus blurs at the edges due to chromatic aberration in the R, G and B color planes can be used to correct this ambiguity. But, in highly defocused regions the ordering of defocus blurs becomes unreliable as the detection of edges becomes erroneous. In this paper, we propose a novel region based method using Color Uniformity Principle (CUP) for detecting the ordering of defocus blurs in R, G and B color planes to resolve the FPA. © 2018 IEEE.

International Conference on Digital Signal Processing, DSP

Resolving Focal Plane Ambiguity using Chromatic Aberration and Color Uniformity Principle

Depth map estimation from Defocus is a computer vision technique which has wide applications such as constructing the 3D setup from 2D image(s), image refocusing and reconstructing 3D scenes. In this paper, we propose an application of Depth from Defocus to a novel keyboard design for detecting keystrokes. The proposed keyboard can be integrated with devices such as mobile, PC and tablets and can be generated by either printing on plain paper or by projection on a flat surface. The proposed design utilizes measured defocus together with a precalibrated relation between the defocus amount and the keyboard pattern to infer the depth, which, along with the azimuth position of the stroke identifies the key. As the proposed design does not require any other hardware besides a monocular camera, this makes the proposed approach a cost effective and feasible solution for a portable keyboard. © 2019 IEEE.

2019 IEEE Conference on Information and Communication Technology, CICT 2019

A defocus based novel keyboard design

Significant recent developments in 3D display technology have focused on techniques for converting 2D media into 3D. Depth map is an integral part of 2D-to-3D conversion. Combining multiple depth cues results in a more accurate depth map as it compensates for the errors caused by one depth cue as well as its absence by other depth cues. In this paper, we present a novel framework to generate a more accurate depth map for video using defocus and motion cues. The moving objects present in the scene are the source of errors in both defocus and motion-based depth map estimation. The proposed method rectifies these errors in the depth map by integrating defocus blur and motion cues. In addition, it also corrects the errors in other parts of depth map caused by inaccurate estimation of defocus blur and motion. Since the proposed integration approach relies on the characteristics of point spread functions of defocus and motion blur along with their relations to camera parameters, it is more accurate and reliable. © 2018 IEEE.

Depth Map Estimation Using Defocus and Motion Cues

Compression is an important aspect of image processing with respect to transmission and storage. It is an established field of research with very little scope for improvement through customary coding based compression techniques. Consequently, non-customary ways of compression have become an important area for future research. Any information that can be restored can be compressed. Using this principle, we propose a novel spectrum based image compression technique. We first blur the image with Point Spread Function (PSF) determined using spectrum of given image. Blurring increases the DC component in the image which in turns gets further compressed by DCT based JPEG compression compared to original image. To recover the image, we perform deconvolution using the known blur PSF. © 2017 IEEE.

2017 9th International Conference on Advances in Pattern Recognition, ICAPR 2017

A Novel Method for Image Compression Using Spectrum

Smart video surveillance of indoor and outdoor scenes is a challenging task for modern surveillance systems. Different imaging conditions like bad illumination, adverse weather, etc., makes the surveillance process difficult. Recently, researchers have proposed smart surveillance systems with additional features for more accurate monitoring of events, but not much attention is paid to improve the system such that the monitoring process consumes as minimum resources as possible. In this paper, we propose a novel surveillance system that enhances visibility in adverse weather conditions and summarizes the captured videos automatically to reduce storage space. As the summarization process is based on the events in a scene, video interpretation becomes fast and easy. We propose perceptual features that can be used for more meaningful and robust summarization of the video than the existing summarization algorithms. We test the system for both indoor and outdoor scenes and show that the system works well even with multiple moving objects and complex motions. © 2017 IEEE.

Design of smart video surveillance system for indoor and outdoor scenes

Depth map estimation forms an integral part of many applications such as 2D-to-3D creation. There exists various methods in literature for depth map estimation using different cues and structure. Usually, depth information is decoded from these cues at the edges and matting is applied to spread it over neighboring regions. Defocus is one such cue due to its natural existence and does not require any precondition compared to other cues. However, there can exist regions in images with no edges. These regions are referred to hole regions and are the main source of error in estimated depth map. In this paper, we propose a method to correct some of these errors to obtain an accurate depth map. The proposed method uses color uniformity principle to detect hole regions present in depth map. We also provide a framework to identify falsely detected holes in order to increase effectiveness of our method. © 2017 IEEE.

Hole correction in estimated depth map from single image using color uniformity principle

As imaging is a process of 2D projection of a 3D scene, the depth information is lost at the time of image capture from conventional camera. This depth information can be inferred back from a set of visual cues present in the image. In this work, we present a model that combines two monocular depth cues namely Texture and Defocus. Depth is related to the spatial extent of the defocus blur by assuming that more an object is blurred, the farther it is from the camera. At first, we estimate the amount of defocus blur present at edge pixels of an image. This is referred as the Sparse Defocus map. Using the sparse defocus map we generate the full defocus map. However such defocus maps always contain hole regions and ambiguity in depth. To handle this problem an additional depth cue, in our case texture has been integrated to generate better defocus map. This integration mainly focuses on modifying the erroneous regions in defocus map by using the texture energy present at that region. The sparse defocus map is corrected using texture based rules. The hole regions, where there are no significant edges and texture are detected and corrected in sparse defocus map. We have used region wise propagation for better defocus map generation. The accuracy of full defocus map is increased with the region wise propagation. © 2015 IEEE.

2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015

Depth estimation from single image using Defocus and Texture cues

Focal plane ambiguity in depth map creation from defocus blur has remained an challenging problem. In this paper, we present a method to resolve this issue with the help of Chromatic Aberration(CA). CA is a distortion referred to focal length variation of the lens with wavelength of light. When light, a mixture of various monochromatic components, passes through a lens, multiple focal planes are generated due to CA. There also exists an inherent ordering in the defocus blur of the object for different RGB components depending on whether the object lies in the near or far focus region. The ordering reverses as soon as focal planes are crossed. By using this difference in ordering of the defocus amount for different RGB components of an object, we can deduce whether the object is present in front of or behind the image plane, hence using this information, a more reliable depth map can be obtained. © 2015 IEEE.

2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

Resolving focal plane ambiguity in depth map creation from defocus blur using chromatic aberration

Light is a mixture of multiple spectral components. An image is a response of the scene with respect to these spectra. Principal components are more compact representation of the data compared to any other representations. Hence accuracy of the estimated defocus parameter is higher in principal component representation than any other customary used representations. In this paper, we present comparison between principal component representation and customary gray scale representation for depth map creation. The presented results shows that the depth maps obtained using principal component are smoother than depth maps obtained using gray scale representation. Besides that, the noise estimation using principal components is much more accurate than using Wiener strategy. © 2015 IEEE.