A Hierarchical System Design for Detection of Glaucoma From Color Fundus Images

Madhulika Jain (homepage)

Glaucoma is an eye disorder which is prevalent in the aging population and causes irreversible loss of vision. Hence, computer aided solutions are of interest for screening purposes. Glaucoma is indicated by structural changes in the optic disc (OD), loss of nerve ?bres and atrophy of the peripapillary region of the OD in retina. In retinal images, most of these appear in the form of subtle variation in appearance. Hence, automated assessment of glaucoma from colour fundus images is a challenging problem. Prevalent approaches aim at detecting the primary indicator, namely, the optic cup deformation relative to the disc and use the ratio of the two diameters in the vertical direction, to classify images as normal or glaucomatous.

We explore the use of global motion pattern-based features to detect glaucoma from images and propose an image representation that serves to accentuate subtle indicators of the disease. These global image features are then used to identify normal cases effectively. The proposed method is demonstrated on a large image dataset consisting of 1845 images annotated by 3 medical experts. The global approach is extended to detect atrophy and two hierarchical system designs are proposed. In the ?rst design, only global analysis is used, while in the second both global and local analysis are employed.

In the ?rst design, the ?rst stage is based on features capturing information mainly of primary indicators while the second stage is based on features extracted for detecting atrophy (secondary visual indicator).

The second design attempts to combine the strengths of global and local analysis of the OD region. Global features are used to remove as many normal cases as possible in the ?rst stage and local features are used to perform a ?ner classi?cation in the second stage. This system has been tested on 1040 images with ground truth collected from 3 glaucoma experts. The results show the hybrid approach offers a good solution for glaucoma screening from retinal images (more...)

Related Publications

• K Sai Deepak, Madhulika Jain, Gopal Datt Joshi, Jayanthi Sivaswamy - Motion pattern-based image features for glaucoma detection from retinal images Proceedings of the 8th Indian Conference on Vision, Graphics and Image Processing, 16-19 Dec. 2012, Bombay, India. [PDF]
  

• Madhulika Jain, Arunava Chakravarty, Gopal Datt Joshi and Jayanthi Sivaswamy - A Hierarchical and Multifactorial System Design for detection of Glaucoma from Color Fundus Images, in Medical Image Computing and Computer Assisted Intervention 2014 (Not Accepted). [PDF]

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Representation of Ballistic Strokes of Handwriting for Recognition and Verification.

Prabhu Teja S (homepage)

The primary computing device interfaces are moving away from the traditional keyboard and mouse inputs towards touch and stylus based interactions. To improve their effectiveness, the interfaces for such devices should be made robust, efficient, and intuitive. One of the most natural ways of communication for humans has been through handwriting. Handwriting based interfaces are more practical than keyboards, especially for scripts of Indic and Han family, which have a large number of symbols. Pen based computing serves four functionalities: a. pointing input b. handwriting recognition c. direct manipulation d. gesture recognition. The second and fourth problems fall under the broad umbrella of pattern recognition problems. In this thesis we focus on efficient representations for handwriting.

In the the first part of this thesis we propose a representation for online handwriting based on ballistic strokes. We first propose a technique to segment online handwriting into its constituent ballistic strokes based on the curvature profile. We argue that the proposed method of segmentation is more robust to noise, compared to the traditional speed profile minima based segmentation. We, then, propose to represent the segmented strokes as the arc of a circle. This representation if validated by the Sigma-lognormal theory of handwriting generation. These features are encoded using a bag-of-words representation, which we name bag-of-strokes. This representation is shown to achieve state- of-art accuracies on various datasets.

In the second part, we extend this representation to the problem of signature verification. To define a verification system, a similarity metric is to be defined. We propose a metric learning algorithm based on the Support Vector Machine (SVM) hyperplanes learned to separate the training data. This results in a very simple metric learning strategy that capable of being modified to increase the number of users registered by the verification system. We experiment with this technique on the publicly available SVC-2004 database and show that this method results in accuracies applicable to practical scenarios. (more...)

Related Publications

• Prabhu Teja S. and Anoop M Namboodiri - A Ballistic Stroke Representation of Online Handwriting for Recognition Proceedings of the 12th International Conference on Document Analysis and Recognition, 25-28 Aug. 2013, Washington DC, USA. [PDF]

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Optimizing Average Precision using Weakly Supervised Data.

Aseem Behl (homepage)

Many tasks in computer vision, such as action classification and object detection, require us to rank a set of samples according to their relevance to a particular visual category. The performance of such tasks is often measured in terms of the average precision (AP). Yet it is common practice to employ the support vector machine (SVM) classifier, which optimizes a surrogate 0-1 loss. The popularity of SVM can be attributed to its empirical performance. Specifically, in fully supervised settings, svm tends to provide similar accuracy to AP-SVM, which directly optimizes an AP-based loss. However, we hypothesize that in the significantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based learning frameworks on three challenging problems: action classification, character recognition and object detection. (more...)

Related Publications

• Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar - Optimizing Average Precision using Weakly Supervised Data IEEE Transations on Pattern Analysis and Machine Intelligence (TPAMI 2015). [PDF]

• Puneet K. Dokania, Aseem Behl, C.V. Jawahar, M. Pawan Kumar -  Learning to Rank using High-Order Information Proceedings of European Conference on Computer Vision,06-12 Sep 2014, Zurich. [PDF]
  

• Aseem Behl, C.V. Jawahar and M. Pawan Kumar - Optimizing Average Precision using Weakly Supervised Data Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, Columbus, Ohio, USA. [PDF]

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Two GPU Algorithms for Raytracing

Srinath Ravichandran (homepage)

Raytracing has been primarily used for generating photo-realistic imagery. Once considered as an offline algorithm, raytracing has primarily become realtime or near realtime with the advancement in computer hardware power. GPU computing has enabled the general purpose usage of graphics cards which were primarily designed for for graphics rendering purposes. Raytracing could be considered as a very embarrassingly parallel process in which each pixel's contribution to the final image is computed independent of one another. The tremendous power of GPUs has been been utilized for vastly improving the performance of almost all the functions in a raytracing pipeline.

The raytracing pipeline for rendering images fundamentally consists of three phases. The first is an acceleration structure construction phase which involves creating a data structure. It provides very fast results for intersection queries for the rays traced within the scene and all the objects contained within the scene. The second is the traversal phase in which each ray is traced within the scene employing the aforementioned data structure. The final optional phase is shading in which each hit point is shaded based on a shading algorithm. The shading algorithm determines the color of the pixel of the image for which a particular ray was traversed and the shading performed for all the rays for all the pixels computes the final rendered image. In this thesis we examine two problems associated with the GPU raytracing pipeline and provide two new approaches for them.

In the first work, we develop a novel parallel algorithm for performing raytracing without constructing any explicit acceleration structure on the GPU. This decouples the long standing notion of creating and storing a separate acceleration structure followed by tracing rays through the structure. Our algorithm creates an implicit hierarchical acceleration structure and traverses the implicit structure at each phase of the algorithm in tandem. Parallel construction algorithms for acceleration structures are difficult to implement on the GPU and also have a large memory footprint to store the resulting structure. Compared to CPUs the amount of memory available to GPUs is limited and hence methods that work on very small memory footprints are of utmost importance. Our algorithm is conceptually very simple, utilizing efficient parallel GPU primitives such as sort and reduce. Further our algorithm has small memory requirements compared to methods that construct acceleration structures thereby making it a suitable candidate for the GPU. Since our method employs a traverse-while-construct method, it is particularly very useful for animated scenes in which the acceleration structure has to be created for each frame in a traditional raytracing pipeline. We implement our algorithm on a CUDA enabled machine and show that our algorithm can perform much better than the serial CPU version of the algorithm.

Our second work is targeted towards the problem in the shading phase of the raytracing pipeline. Traditional renderers employed in the production rendering are primary unidirectional path tracers which traces rays from the camera into the scene. However the path tracing algorithm has difficulty in rendering scenes with complicated lighting scenarios which provide very good aesthetic value to certain kinds of scenes. Bidirectional path tracing on the other hand traces rays from both the camera and the lights within the scene and hence able to render scenes with complicated lighting scenarios much more effectively than path tracing. Current production renderers are primarily CPU based and only a few have started to employ GPUs for the entire pipeline. Limited memory compared to CPUs was the original factor for not employing GPUs. However with current generation GPUs having much more memory than earlier generations, GPUs have been slowly adopted for path tracing based pipelines. However bidirectional path tracers that are GPU based are yet to be fully employed in a full production rendering scenario. Our work is aimed providing a solution that tries to bridge the gap that enables bidirectional path tracing to be used in the production rendering scenario which involves complex materials. We specifically provide a new connection mechanism employed in the light vertex cache - bidirectional path tracing algorithm (LVC-BDPT) for improving shading efficiency as well as a sort based pipeline for improving the runtime performance of the algorithm on the GPU. We show that we perform much better than the unoptimized version of the algorithm as well as providing much better quality for the same amount computations performed.

Related Publications

• Srinath Ravichandran and P. J. Narayanan - Parallel Divide and Conquer Ray Tracing In SIGGRAPH ASIA 2013 Technical Briefs, 19-22nd Nov. 2013, Hong Kong. [PDF]

• Srinath Ravichandran and P. J. Narayanan - Coherent and Importance Sampled LVC-BDPT (Submitted to Siggraph Asia 2015 - Technical Briefs, Under Review).

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