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A novel approach for computer aided diagnosis of schizophrenia using auditory oddball functional MRI 14

A novel approach for computer aided diagnosis of schizophren...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Juneja, Akanksha Rana, Bharti Agrawal, R.K. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)1595930361

Schizophrenia is a serious mental illness that requires timely and accurate diagnosis. Functional magnetic resonance imaging (fMRI) helps in identifying variations in activation patterns of schizophrenia patients and healthy subjects. But, manual diagnosis using fMRI is cumbersome and prone to subjective errors. This has drawn the attention of pattern recognition and computer vision research community towards developing a reliable and efficient decision model for computer aided diagnosis (CAD) of schizophrenia. However, high dimensionality and limited availability of fMRI samples leads to curse-of-dimensionality which may deteriorate the performance of a decision model. In this research work, a combination of feature extraction and feature selection techniques is employed to obtain a reduced set of relevant features for differentiating schizophrenia patients from healthy subjects. A general linear model approach is used for feature extraction on pre-processed fMRI data. Further t-test based feature selection is employed to determine a subset of discriminative features which are used for learning a decision model using support vector machine. Experiments are carried out on two balanced and well-age matched datasets (acquired on 1.5 Tesla and 3 Tesla scanners) of auditory odd-ball task derived from a publicly available multisite FBIRN dataset. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches. Experimental results demonstrate that the proposed model outperforms the two existing approaches in terms of sensitivity, specificity and classification accuracy. With the proposed approach, the classification accuracy of 80.9% and 88.0% is achieved for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be used as biomarkers for CAD of schizophrenia using fMRI. Copyright 2014 ACM.

关键词： computer aided diagnosis

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Deep transfer learning with ontology for image classification

Deep transfer learning with ontology for image classificatio...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Umang Gupta Santanu Chaudhury Dept. of Electrical Engineering Indian Institute of Technology Delhi

Purely, data-driven large scale image classification has been achieved using various feature descriptors like SIFT, HOG etc. Major milestone in this regards is Convolutional Neural Networks (CNN) based methods which learn optimal feature descriptors as filters. Little attention has been given to the use of domain knowledge. Ontology plays an important role in learning to categorize images into abstract classes where there may not be a clear visual connect between category and image, for example identifying image mood - happy, sad and neutral. Our algorithm combines CNN and ontology priors to infer abstract patterns in indian Monument images. We use a transfer learning based approach in which, knowledge of domain is transferred to CNN while training (top down transfer) and inference is made using CNN prediction and ontology tree/priors (bottom up transfer ). We classify images to categories like Tomb, Fort and Mosque. We demonstrate that our method improves remarkably over logistic classifier and other transfer learning approach. We conclude with a remark on possible applications of the model and note about scaling this to bigger ontology.

关键词： Ontologies Training Feature extraction Linear programming Logistics Shape Neural networks

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SIFT based view invariant watermarking for DIBR 3D image

SIFT based view invariant watermarking for DIBR 3D image

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shuvendu Rana Vishavdeep Mattu Arijit Sur Department of CSE Indian Institute of Technology Guwahati India

In this paper, a view invariant watermarking scheme is proposed for depth image based rendering (DIBR) 3D image representation. To make the scheme invariant against view synthesis process, the watermark is inserted in the image locations selected by the SIFT (Scale-invariant feature transform) feature points. A novel SIFT feature based coefficient selection scheme has been implemented for watermark embedding by exploiting the shift invariance and directional property of the DIBR based view synthesis process. A coefficient partitioning based blind watermarking method has been included to make the scheme more efficient. A comprehensive set of experiments have been carried out to justify the robustness of the proposed scheme against the view synthesis attack.

关键词： Watermarking Feature extraction Three-dimensional displays Discrete cosine transforms image representation Robustness Visualization

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Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition 14

Advancing Fingerprint Recognition Quality Assessment: Introd...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Jaiswal, Prateek Koner, Arka Namboodiri, Anoop M. CVIT IIIT Hyderabad India India

ISBN: (纸本)9798400716256

In the field of biometric security, the quality assessment of fingerprint images is paramount for boosting the accuracy of fingerprint recognition systems. These systems are fundamental for the secure and efficient authentication and identification of individuals. Our research presents FRBQ (Fingerprint Recognition-Based Quality), an innovative quality metric designed to navigate the limitations of the NFIQ2 model. FRBQ exploits deep learning algorithms in a weakly supervised setting and utilizes matching scores from DeepPrint, a Fixed-Length Fingerprint Representation Model. Each score is paired with labels indicating the robustness of fingerprint image matches. However, in a fully referenced setting, these labels can be subjective, lacking a clear definition of what "image quality" inherently means. This weakly labeled approach strives to capture diverse perspectives on image quality, potentially making it a more encompassing metric. In comparison to NFIQ2, our research showcases the superior performance of the FRBQ model. It not only correlates better with recognition scores but also effectively evaluates challenging images that NFIQ2 struggles with. Validated by the esteemed FVC 2004 dataset, FRBQ proves its efficacy in fingerprint image quality assessment. This study underscores the transformative potential of AI in biometrics, emphasizing its capability to capture details that traditional methods might overlook. Our work stresses the critical role of precise quality assessment in the evolution of fingerprint recognition systems. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image quality

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A Deep Learning Architecture Based Dimensionality Reduction and Online Signature Verification 4th

A Deep Learning Architecture Based Dimensionality Reduction ...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Vorugunti, Chandra Sekhar Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India

ISBN: (纸本)9789811393600

In this paper, we propose a novel hybrid deep learning based autoencoder-CNN-Softmax architecture aims at obtaining reduced dimension feature set from raw feature set. The reduced feature set forms an input to CNN layers to learn deep global features. These global features are used to train the SoftMax layer for online signature classification. Ability to reduce the noisy features and to discover the hidden corelated features makes the proposed architecture light weight and efficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint mobile devices. We demonstrate the superiority of our model for feature correlation learning and signature classification by conducting experiments on standard datasets MCYT, SUSIG. The experimentation confirms that the proposed model achieves better accuracy (lower error rates) with a lesser number of features compared to the current state-of-the-art models. The proposed models yield state-of-the-art performance of 0.4% EER on MCYT-100 dataset and 3.47% with SUSIG dataset. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Classification (of information)

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Local Ternary Patterns and Maximum Bipartite Matching for Face Recognition

Local Ternary Patterns and Maximum Bipartite Matching for Fa...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Renuka Patnaik Raj Gupta Anurag Mittal Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India

Local Binary Pattern (LBP) has been the successful feature descriptor used for face recognition. The basic idea in this method is to convert from an intensity space to an order space where the order of neighboring pixels is used to create a monotonic change illumination-invariant code for each point in the image. A drawback for this method, however, is that, in homogenous regions, the order of the pixel with respect to its neighbors is quite noisy. In this paper, we propose to use a third value which indicates if two pixels are similar in value. We also propose to match these patterns using maximum bipartite matching rather than histogram matching. Significant performance boost was found when compared to LBP and other standard methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). We present our results on Extended Yale and FERET datasets.

关键词： Face recognition image edge detection Face Lighting Histograms Pattern matching

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Spatio-temporal feature based VLAD for efficient video retrieval

Spatio-temporal feature based VLAD for efficient video retri...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Mopuri K Reddy Sahil Arora R. Venkatesh Babu Video Analytics Lab SERC Indian Institute of Science India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Compact representation of visual content has emerged as an important topic in the context of large scale image/video retrieval. The recently proposed Vector of Locally Aggregated Descriptors (VLAD) has shown to outperform other existing techniques for retrieval. In this paper, we propose two spatio-temporal features for constructing VLAD vectors for videos in the context of large scale video retrieval. Given a particular query video, our aim is to retrieve similar videos from the database. Experiments are conducted on UCF50 and HMDB51 datasets, which pose challenges in the form of camera motion, view-point variation, large intra-class variation, etc. The paper proposes the following two spatio-temporal features for constructing VLADs i) Local Histogram of Oriented Optical Flow (LHOOF), and ii) Space-Time Invariant Points (STIP). The performance of these proposed features are compared with SIFT based spatial feature. The mean average precision (MAP) indicates the better retrieval performance of the proposed spatio-temporal feature over spatial feature.

关键词： Feature extraction Vectors computer vision Histograms Optical imaging Databases conferences

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Fast area of contact computation for collision detection of a deformable object using FEM

Fast area of contact computation for collision detection of ...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Prateek Shrivastava Sukhendu Das Indian Institute of Technology Chennai India

In case of detection and analysis of deformation in collision scenarios, using a method based on area of contact instead of a point of contact generates numerically stable impulse forces. Area of contact improves the stability of control algorithms, but it is often associated with high computational cost. In this paper, we alleviate this problem by proposing a novel algorithm for collision detection of a deformable mesh against rigid structures. We reuse the data structures maintained for elastic force computations in the FEM, for the purpose of collision detection. Parallel constructs on GPU using reduced model make the simulations interactive even for meshes with thousands of elements. Since we don't maintain any additional complex structure for keeping track of the deformable body at each iteration, we significantly reduce the usage of GPU memory bandwidth. Efficiency of our method is illustrated by reporting high culling efficiency on various tests.

关键词： Materials Finite element analysis Force graphics processing units Computational modeling Complexity theory Deformable models

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Attention guided complementary feature integration for latent image recovery from noisy/blurry pairs 21

Attention guided complementary feature integration for laten...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Rosh, K.S. Green Lomte, Sachin Krishnan, Nikhil Prasad, B.H. Pawan Samsung Research Institute Karnataka Bangalore India

ISBN: (纸本)9781450391276

Low-light imaging using a hand-held camera is a challenging task due to excessive noise in the scene. Most of the existing methods try to address this problem either by denoising an image captured using high ISO or by deblurring an image captured using long exposure time. However these methods use a single image to estimate the latent scene and hence fail to leverage the complimentary information available in the scene. In this paper, we propose a method to estimate the latent image using a pair of images captured using high ISO and high exposure time respectively, to leverage the complimentary information present in the two captures. We propose a novel deep learning based method to efficiently extract and integrate the information present in the two images. Contrary to other methods, we use separate filters to extract the complimentary information from the two images. We also progressively integrate the extracted features using a novel attention-guided mechanism. Further, we address the spatially varying nature and localization of motion blur in real life captures by using spatial attention layers. The proposed method achieves state-of-the-art performance against single as well as other noisy/blurry approaches to the problem. We also show that the network learns spatial attention maps with strong correlation to the blur in the scene, and thus the proposed method is more interpretable and easier to analyze. © 2021 ACM.

关键词： Deep learning

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Assessment of computational visual attention models on medical images 12

Assessment of computational visual attention models on medic...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Jampani, Varun Ujjwal Sivaswamy, Jayanthi Vaidya, Vivek CVIT IIIT-Hyderabad Hyderabad India Medical Image Analysis Lab. GE Global Research Bangalore India

ISBN: (纸本)9781450316606

Several computational visual saliency models have been proposed in the context of viewing natural scenes. We aim to investigate the relevance of computational saliency models in medical images in the context of abnormality detection. We report on two studies aimed at understanding the role of visual saliency in medical images. Diffuse lesions in Chest X-Ray images, which are characteristic of Pneumoconiosis and high contrast lesions such as 'Hard Exudates' in retinal images were chosen for the study. These approximately correspond to conjunctive and disjunctive targets in a visual search task. Saliency maps were computed using three popular models namely Itti-Koch [7], GBVS [3] and SR [4]. The obtained maps were evaluated against gaze maps and ground truth from medical experts. Our results show that GBVS is seen to perform the best (Mdn. ROC area = 0.77) for chest X-Ray images while SR performs the best (ROC area = 0.73) for retinal images, thus asserting that searching for conjunctive targets calls for a more local examination of an image while disjunctive targets call for a global examination. Based on the results of the above study, we propose extensions for the two best performing models. The first extension makes use of top down knowledge such as lung segmentation. This is shown to improve the performance of GBVS to some extent. In the second case the extension is by way of including multi-scale information. This is shown to significantly (by 28.76%) improve abnormality detection. The key insight from these studies is that bottom saliency continues to play a predominant role in examining medical images. © 2012 ACM.

关键词： Ophthalmology

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