Prem Kalra

Space-Time Super-Resolution Using Graph-Cut Optimization

We address the problem of super-resolution-obtaining high-resolution images and videos from multiple low-resolution inputs. The increased resolution can be in spatial or temporal dimensions, or even in both. We present a unified framework which uses a generative model of the imaging process and can address spatial super-resolution, space-time super-resolution, image deconvolution, single-image expansion, removal of noise, and image restoration. We model a high-resolution image or video as a Markov random field and use maximum a posteriori estimate as the final solution using graph-cut optimization technique. We derive insights into what super-resolution magnification factors are possible and the conditions necessary for super-resolution. We demonstrate spatial super-resolution reconstruction results with magnifications higher than predicted limits of magnification. We also formulate a scheme for selective super-resolution reconstruction of videos to obtain simultaneous increase of resolutions in both spatial and temporal directions. We show that it is possible to achieve space-time magnification factors beyond what has been suggested in the literature by selectively applying super-resolution constraints. We present results on both synthetic and real input sequences.

Super resolution of images of 3D scenecs

We address the problem of super resolved generation of novel views of a 3D scene with the reference images obtained from cameras in general positions; a problem which has not been tackled before in the context of super resolution and is also of importance to the field of image based rendering. We formulate the problem as one of estimation of the color at each pixel in the high resolution novel view without explicit and accurate depth recovery. We employ a reconstruction based approach using MRF-MAP formalism and solve using graph cut optimization. We also give an effective method to handle occlusion. We present compelling results on real images.

Super resolution using graph-cut

This paper addresses the problem of super resolution – obtaining a single high-resolution image given a set of low resolution images which are related by small displacements. We employ a reconstruction based approach using MRF-MAP formalism, and use approximate optimization using graph cuts to carry out the reconstruction. We also use the same formalism to investigate high resolution expansions from single images by deconvolution assuming that the point spread function is known. We present a method for the estimation of the point spread function for a given camera. Our results demonstrate that it is possible to obtain super-resolution preserving high frequency details well beyond the predicted limits of magnification.

A System for View‐Dependent Animation

In this paper, we present a novel system for facilitating the creation of stylized view‐dependent 3D animation. Our system harnesses the skill and intuition of a traditionally trained animator by providing a convivial sketch based 2D to 3D interface. A base mesh model of the character can be modified to match closely to an input sketch, with minimal user interaction. To do this, we recover the best camera from the intended view direction in the sketch using robust computer vision techniques. This aligns the mesh model with the sketch. We then deform the 3D character in two stages ‐ first we reconstruct the best matching skeletal pose from the sketch and then we deform the mesh geometry. We introduce techniques to incorporate deformations in the view‐dependent setting. This allows us to set up view‐dependent models for animation.

Time based Activity Inference using Latent Dirichlet Allocation

In this paper we address the problem of time based activity inference in unsupervised manner for an area under surveillance. We use a Latent Dirichlet Allocation based model that captures the activities and how they change over time. We use agglomerative clustering on optical flow vectors to code direction and spatial information. In this model each activity is associated with not only a mixture distribution over these cluster occurrences but also on the distribution over timestamps of their occurrences. Our method thus helps in determining the prominence and the correlation of activities over a period of time.

Generic cuts: an efficient algorithm for optimal inference in higher order MRF-MAP

We propose a new algorithm called Generic Cuts for computing optimal solutions to 2 label MRF-MAP problems with higher order clique potentials satisfying submodularity. The algorithm runs in time O(2kn3) in the worst case (k is clique order and n is the number of pixels). A special gadget is introduced to model flows in a high order clique and a technique for building a flow graph is specified. Based on the primal dual structure of the optimization problem the notions of capacity of an edge and cut are generalized to define a flow problem. We show that in this flow graph max flow is equal to min cut which also is the optimal solution to the problem when potentials are submodular. This is in contrast to all prevalent techniques of optimizing Boolean energy functions involving higher order potentials including those based on reductions to quadratic potential functions which provide only approximate solutions even for submodular functions. We show experimentally that our implementation of the Generic Cuts algorithm is more than an order of magnitude faster than all algorithms including reduction based whose outputs on submodular potentials are near optimal.

Off-line hand written input based identity determination using multi kernel feature combination

The paper presents a novel framework for the application of multiple features for handwritten data based identity recognition. Different types of features characterise different facets of the handwriting. We have designed a scheme for multiple feature based identity establishment using multi-kernel learning. A new formulation for multi-kernel learning using genetic algorithm has been presented. The efficacy of the framework using individual and combination of features is demonstrated for Devanagari script input.

An efficient graph cut algorithm for computer vision problems

Graph cuts has emerged as a preferred method to solve a class of energy minimization problems in computer vision. It has been shown that graph cut algorithms designed keeping the structure of vision based flow graphs in mind are more efficient than known strongly polynomial time max-flow algorithms based on preflow push or shortest augmenting path paradigms [1]. We present here a new algorithm for graph cuts which not only exploits the structural properties inherent in image based grid graphs but also combines the basic paradigms of max-flow theory in a novel way. The algorithm has a strongly polynomial time bound. It has been bench-marked using samples from Middlebury [2] and UWO [3] database. It runs faster on all 2D samples and is at least two to three times faster on 70% of 2D and 3D samples in comparison to the algorithm reported in [1].

Quantitative analysis of variable extent of anterior clinoidectomy with intradural and extradural approaches: 3-dimensional analysis and cadaver dissection.

BACKGROUND: Drilling of the anterior clinoid process (ACP) is an integral component of surgical approaches for central and paracentral skull base lesions. The technique to drill ACP has evolved from pure intradural to extradural and combined techniques. OBJECTIVE: To describe the computerized morphometric evaluation of exposure of optic nerve and internal carotid artery with proposed tailored intradural (IDAC) and complete extradural (EDAC) anterior clinoidectomy. METHODS: We describe a morphometric subdivision of ACP into 4 quadrangles and 1 triangle on the basis of fixed bony landmarks. Computerized volumetric analysis with 3-dimensional laser scanning of dry-drilled bones for respective tailored IDAC and EDAC was performed. Both approaches were compared for the area and length of the optic nerve and internal carotid artery. Five cadaver heads were dissected on alternate sides with intradural and extradural techniques to evaluate exposure, surgical freedom, and angulation of approach. RESULTS: Complete anterior clinoidectomy provides a 2.5-times larger area and 2.7-times larger volume of ACP. Complete clinoidectomy deroofed the optic nerve to an equal extent as by proposed the partial tailored clinoidectomy approach. Tailored IDAC exposes only the distal dural ring, whereas complete EDAC exposes both the proximal and distal dural rings with complete exposure of the carotid cave. CONCLUSION: Quantitative comparative evaluation provides details of exposure and surgical ease with both techniques. We promote hybrid/EDAC technique for vascular pathologies because of better anatomic orientation. Extradural clinoidectomy is the preferred technique for midline cranial neoplasia. An awareness of different variations of clinoidectomy can prevent dependency on any particular approach and facilitate flexibility. ABBREVIATIONS: ACP, anterior clinoid process EDAC, extradural anterior clinoidectomy ICA, internal carotid artery IDAC, intradural anterior clinoidectomy MOB, meningo-orbital band ON, optic nerve SOF, superior orbital fissure

Free-access open-source e-learning in comprehensive neurosurgery skills training

BACKGROUNDnSince the end of last century, technology has taken a front seat in dispersion of medical education. Advancements of technology in neurosurgery and traditional training methods are now being challenged by legal and ethical concerns of patient safety, resident work-hour restriction and cost of operating-room time. To supplement the existing neurosurgery education pattern, various e-learning platforms are introduced as structured, interactive learning system.nnnMATERIALS AND METHODSnThis study focuses on the concept, formulation, development and impact of web based learning platforms dedicated to neurosurgery discipline to disseminate education, supplement surgical knowledge and improve skills of neurosurgeons. Neurosurgery Education and Training School (NETS), e-learning platform has integration of web-based technologies like Content Management System for organizing the education material and Learning Management System for updating neurosurgeons. NETS discussion forum networks neurosurgeons, neuroscientists and neuro-technologists across the globe facilitating collaborative translational research.nnnRESULTSnMulti-authored neurosurgical e-learning material supplements the deficiencies of regular time-bound education. Interactive open-source, global, free-access e-learning platform of NETS has around 1) 425 visitors/month from 73 countries; ratio of new visitors to returning visitors 42.3; 57.7 (2); 64,380 views from 190 subscribers for surgical videos, 3-D animation, graphics based training modules (3); average 402 views per post.nnnCONCLUSIONnThe e-Learning platforms provide updated educational content that make them quick, surf, find and extract resources. e-Learning tools like web-based education, social interactive platform and question-answer forum will save unnecessary expenditure of time and travel of neurosurgeons seeking knowledge. The need for free access platforms is more pronounced for the neurosurgeons and patients in developing nations.