Interactive visualization and tuning of multi-dimensional clusters for indexing

Dasari Pavan Kumar (homepage)

The automation of activities in all areas, including business, engineering, science, and government, produces an ever-increasing stream of data. Especially, the amount of multimedia content produced and made available on the Internet, both in professional and personal collections is growing rapidly. Equally increasing are the needs in terms of efficient and effective ways to manage it. And why is that so? Because, people believe that data collected contains valuable information. But, extracting any such information/patterns is however an extremely difficult task. This has led to a great amount of research into content based retrieval and visual recognition. The most recent retrieval systems available extract low-level image features and conceptualize them into clusters. A conventional sequential scan on those image features would approximately take about a few hours to search in a set of hundreds of images. Hence, clustering and indexing forms the very crux of the solution. The state of the art uses the 128-dimensional SIFT as low level descriptors. Indexing even a moderate collection involves several millions of such vectors. The search performance depends on the quality of indexing and there is often a need to interactively tune the process for better accuracy. In this thesis, we propose a visualization-based framework and a tool which adheres to the it to tune the indexing process for images and videos. We use a feature selection approach to improve the clustering of SIFT vectors. Users can visualize the quality of clusters and interactively control the importance of individual or groups of feature dimensions easily. The results of the process can be visualized quickly and the process can be repeated. The user can use a filter or a wrapper model in our tool. We use input sampling, GPU-based processing, and visual tools to analyze correlations to provide interactivity. We present results of tuning the indexing for a few standard datasets. A few tuning iterations resulted in an improvement of over 5% in the final classification performance, which is significant. (more...)

Related Publications

• Dasari Pavan Kumar and P. J. Narayanan - Interactive Visualization and Tuning of SIFT Indexing in Proceedings of the Vision, Modelling and Visualization Workshop 2010, Siegen, Germany, 97-105, Eurographics Association., 2010.

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Detection of diabetic retinopathy lesions in color retinal images

Keerthi Ram (homepage)

Advances in medical device technology have resulted in a plethora of devices that sense, record, transform and process digital data. Images are a key form of diagnostic data, and many devices have been designed and deployed that capture high-resolution in-vivo images in different parts of the spectrum. Computers have enabled complex forms of reconstruction of cross-sectional/ 3D structure (and temporal data) non-invasively, by combining views from multiple projections. Images thus present valuable diagnostic information that may be used to make well-informed decisions.

Computer aided diagnosis is a contemporary exploration to apply computers to process digital data with the aim of assisting medical practitioners in interpreting diagnostic information. This thesis takes up a specific disease: diabetic retinopathy, which has visual characteristics manifesting in different stages. Image analysis and pattern recognition have been used to design systems with the objective of detecting and quantifying the extent. The quantitative information can be used by the practitioner to stage the disease and plan treatment, track drug efficacy, or make decisions on course of treatment or prognosis.

The generic task of image understanding is known to be computationally ill-posed. However adding domain constraints and restricting the size of the problem make it possible to attempt solutions that are useful. Two basic tasks in image understanding : detection and segmentation, are used. A system is designed to detect a type of vascular lesion called microaneurysm, which appear in the retinal vasculature at the advent of diabetic retinopathy. As the disease progresses it manifests visually as exudative lesions, which are to be segmented, and a system has been developed for the same.

The developed systems are tested with image datasets that are in the public domain, as well as a real dataset (from a local speciality hospital) collected during the course of the research, to compare performance indicators against the prior art and elicit better understanding of the factors and challenges involved in creating a system that is ready for clinical use. (more...)

Related Publications

• Keerthi Ram, Gopal Datt Joshi and Jayanthi Sivaswamy - A Successive Clutter-Rejection based Approach for Early Detection of Diabetic Retinopathy IEEE Transactions on Biomedial Engineering, 58(3), pp. 664-673, Mar,2011. [PDF]

• Keerthi Ram and Jayanthi Sivaswamy - Multi-space clustering for segmentation of exudates in retinal color photographs Proceedings of 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC 09), 2-6 September, 2009, Minneapolis, USA. [PDF]

• Keerthi Ram, Yogesh Babu and Jayanthi Sivaswamy - Curvature Orientation Histograms for Detection and Matching of Vascular Landmarks in Retinal Images Proceedings of SPIE Medical Imaging(SPIE 09), 7-12 February, 2009, Florida, USA. [PDF]

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Object-centric Video Navigation and Manipulation

Rajvi Shah (homepage)

The past decade has seen tremendous advancements in consumer electronics and web technology. The proliferation of digital cameras and popularity of content sharing sites have caused a rapid growth in creation and distribution of images and videos by home-users. Enhancement and manipulation of captured images is fairly popular among common users due to availability of numerous easy-to-use photo editing utilities like Instagram, Picasa, Photo Gallery, etc. In comparison, video manipulation is still less popular among common users due to the lack of easy-to-use yet powerful video editing platforms. Basic video editing platforms for home-users are simple and intuitive, but these tools provide limited functionality such as split and merge videos, add captions or audio etc. Professional video editing platforms are rich in functionality, but these tools demand high technical expertise for use. A novice user usually gets discouraged by complex interactions and cumbersome processing. Moreover, the traditional video editing interfaces model and represent videos as a collection of frames against a timeline. In a user's perception, a video has more meaningful semantics such as objects, actions, events, interactions, etc. The gap between perception and representation makes object-centric manipulation of videos an unnatural and laborious task. In this thesis, we attempt to bridge the gap between the power and usability of video manipulation interfaces by using computer vision techniques. We propose a representation based on three high-level video semantics, scene mosaic, object motion, and camera motion to enable simple and meaningful interaction for object-centric navigation and manipulation of long shot videos. We build an extended field of view mosaic of the video scene and represent object motion in this scene mosaic using 3D space-time trajectories. We define novel object and camera manipulation operations using object trajectories as basic interaction elements. The use of object trajectories as basic video semantics replaces complex interface elements by interactive curve manipulation operations. The object operations allow the users to perform various temporal manipulations on the video objects by interactively manipulating the object trajectories. For example, users can delay or advance the video objects by dragging the trajectories along the timeline or replicate objects by creating multiple copies of the object trajectories. The camera operations model the camera as a movable and scalable aperture and allow the users to simulate camera pan, tilt, and zoom by creating new aperture trajectories. Object and camera operations, in combination allow users to perform a number of high-level video manipulations in a simple 'click and drag' fashion.

Related Publications

• Rajvi Shah and P.J. Narayanan - Trajectory based Video Object Manipulation Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2011),11-15 July, 2011, Barcelona, Spain. [PDF]
  

• Rajvi Shah and P. J. Narayanan - Scene Background and Object Trajectories based Interactive Video Manipulation, in IEEE Transactions on Circuit Systems and Video Technology, Under Review.

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Improving image quality of synthetically zoomed tomographic images.

Neha Dixit (homepage)

 3-D medical images such as Computed Tomography(CT), Positron Emission Tomography(PET) and Magnetic Resonance Imaging(MRI) are commonly used for diagnosis. Tomographic reconstruction is the underlying technique for all these images. It is often of interest to increase the resolution of these images. Some of the possible solutions for increasing the resolution are: increasing the number of detectors, changing the detector material/geometry, increasing the excitation energy level or scanning time. These solutions are limited in terms of practical implementation. Hence, finding solutions at the processing level (post data acquisition) is of interest. This thesis focuses on the problem of preserving details and quality of tomographic images after upsampling. We have explored two different approaches for generating higher resolution images. These are based on re-examining the lattice geometry for reconstruction. We offer two novel solutions, one which uses a hexagonal instead of the conventional square lattice and a second one based super resolution strategy which combines samples drawn from two lattices.

In the first solution, we reconstruct the image onto a hexagonal lattice. These samples are then interpolated to find the samples on the desired higher resolution square lattice. In the super resolution (SR)- based solution we reconstruct the images onto 2 low resolution lattices. The lattices are rotated versions of each other which provide a 'different view' of an object/scene. For generating low resolution images both square and hexagonal sampling have been considered.

The two solutions have been implemented and tested using a variety of test objects. These objects which are called phantoms were chosen to be of 2 types: analytical and real. Three analytically generated phantoms used were Concentric rings, dots and lines. Two real PET phantoms were Nema and Hoffman Brain phantoms. The results were benchmarked against upsampled images generated by: Direct upsampling and an existing super resolution technique which uses Union of Shifted Lattice (USL) samples. The evaluation results show a qualitative and quantitative improvement over results obtained by direct upsampling. The SR-based results are comparable to that obtained with USL in quality. However, the main strength of the proposed SR-based solution is the computational savings and a reduced number of required images for a given upsampling factor. (more...)

Related Publications

• N. Dixit and J. Sivaswamy - A Novel Approach to Generate Up-sampled Tomographic Images using Combination of Rotated Hexagonal Lattices Proceedings of Sixteenth National Confernece on Communications (NCC'10),29-31 Jan. 2010,Chennai, India. [PDF]

• Neha Dixit, N.V. Kartheek and Jayanthi Sivaswamy - Synthetic Zooming of Tomogrphics Images by Combination of Lattices Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference, 25-31 October, 2009. [PDF]

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