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检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Shearlet based medical image fusion using pulse-coupled neural network with fuzzy memberships 10

Shearlet based medical image fusion using pulse-coupled neur...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Mishra, Niladri Shekhar Das, Sudeb Chakrabarti, Amlan Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata700152 India Videonetics Technology Private Limited B.P. Block Sector V Salt Lake City Kolkata700091 India A.K. Choudhury School of Information Technology University of Calcutta Kolkata700009 India

In this article, we propose a novel multimodal Medical image Fusion (MIF) method based on a neuro-fuzzy technique in the transform (Non-Subsampled Shearlet Transform (NSST)) domain for spatially registered, multi-modal medical images. The source medical images are first decomposed by NSST. The low-frequency subbands (LFSs) are fused using the Max-selection rule. Fuzzy triangular memberships are derived from a specific neighborhood-region of each high-frequency coefficient. Then they (high-frequency subbands, HFSs) are fused using a biologically inspired neural network (Pulse Coupled Neural Network (PCNN)) according to our newly proposed rule. Then inverse NSST (INSST) is applied to the fused coefficients to get the fused image. Visual and quantitative analysis and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodality medical images. © Springer International Publishing AG 2017.

关键词： image fusion

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Examining the Influence of Personality and Multimodal Behavior on Hireability Impressions 23

Examining the Influence of Personality and Multimodal Behavi...

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proceedings of the fourteenth indian conference on computer vision, graphics and image processing

作者： Harshit Malik Hersh Dhillon Ravikiran Parameshwara Roland Goecke Ramanathan Subramanian Indian Institute of Technology Ropar IN University of Canberra AU

ISBN: (纸本)9798400716256

While personality traits have been traditionally modeled as behavioral constructs, we novelly posit job hireability as a personality construct. To this end, we examine correlates among personality and hireability measures on the First Impressions Candidate Screening dataset. Modeling hireability as both a discrete and continuous variable, and the big-five OCEAN personality traits as predictors, we utilize (a) multimodal behavioral cues, and (b) personality trait estimates obtained via these cues for hireability prediction (HP). For each of the text, audio and visual modalities, HP via (b) is found to be more effective than (a). Also, superior results are achieved when hireability is modeled as a continuous rather than a categorical variable. Interestingly, eye and bodily visual cues perform comparably to facial cues for predicting personality and hireability. Explanatory analyses reveal that multimodal behaviors impact personality and hireability impressions: e.g., Conscientiousness impressions are impacted by the use of positive adjectives (verbal behavior) and eye movements (non-verbal behavior), confirming prior observations.

关键词： Behavioural cues Classification Hireability Multimodal Personality traits Regression

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Real time indian sign language recognition system

Real time Indian sign language recognition system

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International conference on Artificial Intelligence and Energy Systems (AIES)

作者： Musthafa, Najla Raji, C. G. MEA Engn Coll Dept Comp Sci & Engn Perinthalmanna India

Hand gestures are one of the nonverbal communication modalities used in sign language. It is most often used by deaf and/or dumb individuals who have hearing or speech impairments to communicate with other deaf and/or dumb people or with normal people. Many manufacturers across the world have created various sign language systems;however they are neither adaptable nor cost- effective for end users. To address this, a model has been developed that provides a system prototype that can detect sign language automatically, allowing deaf and dumb individuals to transfer the message more successfully with normal people. The static visuals were processed with a convolutional neural network and the feature extraction approach, and each sign was trained with ten samples. image processing methods are used to determine the fingertip location of static pictures and convert them to text. The suggested technique can recognize the signer's pictures that are taken in real time during the testing phase. The results demonstrate that the suggested Sign Language Recognition System is capable of accurately recognizing *** (c) 2022 Elsevier Ltd. All rights *** and peer-review under responsibility of the scientific committee of the International conference on Artificial Intelligence & Energy Systems.

关键词： Sign Language Recognition Convolutional Neural Network computer vision Feature Extraction

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Exploring Temporal Differences in 3D Convolutional Neural Networks 7th

Exploring Temporal Differences in 3D Convolutional Neural Ne...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Kanojia, Gagan Kumawat, Sudhakar Raman, Shanmuganathan Indian Institute of Technology Gandhinagar Gandhinagar India

ISBN: (纸本)9789811586965

Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less computationally expensive and less memory intensive than 3D CNNs and have shown remarkable results in applications like image classification and object recognition. However, in previous works, it has been observed that they are inferior to 3D CNNs when applied on a spatio-temporal input. In this work, we propose a convolutional block which extracts the spatial information by performing a 2D convolution and extracts the temporal information by exploiting temporal differences, i.e., the change in the spatial information at different time instances, using simple operations of shift, subtract and add without utilizing any trainable parameters. The proposed convolutional block has same number of parameters as of a 2D convolution kernel of size, and has n times lesser parameters than an 3D convolution kernel. We show that the 3D CNNs perform better when the 3D convolution kernels are replaced by the proposed convolutional blocks. We evaluate the proposed convolutional block on UCF101 and ModelNet datasets. All the codes and pretrained models are publicly available at https://***/GaganKanojia/SSA-ResNet. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object recognition

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DeepPFCN: Deep Parallel Feature Consensus Network for Person Re-identification 7th

DeepPFCN: Deep Parallel Feature Consensus Network for Person...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Singh, Shubham Kumar Miyapuram, Krishna P. Raman, Shanmuganathan Indian Institute of Technology Gandhinagar PalajGujarat382355 India

ISBN: (纸本)9789811586965

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep Parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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A Chaos Based Robust and Secure image Hashing Framework 18

A Chaos Based Robust and Secure Image Hashing Framework

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Satendra Pal Singh Gaurav Bhatnagar Indian Institute of Technology Jodhpur India

ISBN: (纸本)9781450366151

In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coefficients are divided into non-overlapping blocks. The selected blocks based on a peace-wise non-linear chaotic map are transformed using discrete cosine transom followed by singular value decomposition. Then a feature matrix is constructed in reliance on Hessian matrix and the final hash values are obtained. The proposed hashing system is resilient to different content-preserving image distortions such as geometric and filtering operations. The simulated results demonstrate the efficiency proposed framework in terms of security and robustness.

关键词： Discrete cosine transformation

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Combining multiple evidence for video classification

Combining multiple evidence for video classification

引用

2nd International conference on Intelligent Sensing and Information processing, ICISIP'05

作者： Suresh, Vakkalanka Mohan, C. Krishna Kumaraswamy, R. Yegnanarayana, B. Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai-600 036 India

ISBN: (纸本)0780388402

In this paper, we investigate the problem of video classification into predefined genre, by combining the evidence from multiple classifiers. It is well known in the pattern recognition community that the accuracy of classification obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. The conventional method for combining individual classifiers weighs each classifier equally (sum or vote rule fusion). In this paper, we study a method that estimates the performances of the individual classifiers and combines the individual classifiers by weighing them according to their estimated performance. We demonstrate the efficacy of the performance based fusion method by applying it to classification of short video clips (20 seconds) into six popular TV broadcast genre, namely cartoon, commercial, news, cricket, football, and tennis. The individual classifiers are trained using different spatial and temporal features derived from the video sequences, and two different classifier methodologies, namely Hidden Markov Models (HMMs) and Support Vector Machines (SVMs). The experiments were carried out on more than 3 hours of video data. A classification rate of 93.12% for all the six classes and 97.14% for sports category alone has been achieved, which is significantly higher than the performance of the individual classifiers. ©2005 IEEE.

关键词： image processing

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High quality depth map estimation by kinect upsampling and hole filling using RGB features and mutual information

High quality depth map estimation by kinect upsampling and h...

引用

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

作者： Nidhi Chahal Santanu Chaudhury Department of Electrical Engineering Indian Institute of Technology Delhi India

High quality depth map estimation is required for better visualization of 3D views as there is great impact of depth map quality on overall 3D image quality. If the depth is estimated from conventional ways using two or more images, some defects come into picture, mostly in regions without texture. We utilised Microsoft Kinect RGBD dataset to obtain input color images and depth maps which also includes some noise factors. We proposed a method to remove this noise and get quality depth images. First the color and depth images are aligned to each other using intensity based image registration. This method of image alignment is mostly used in medical field, but we applied this technique to correct kinect depth maps by which one can avoid cumbersome task of feature based point correspondence between images. There is no requirement of preprocessing or segmentation steps if we use intensity based image alignment method. Second, we proposed an algorithm to fill the unwanted gaps in kinect depth maps and upsampled it using corresponding high resolution color image. Finally we applied 9×9 median filtering on implementation results and get high quality and improved depth maps.

关键词： Mutual information image color analysis Filtering Color Joints image resolution image registration

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Design of Manipuri Keywords Spotting System using HMM

Design of Manipuri Keywords Spotting System using HMM

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

作者： Laishram Rahul Salam Nandakishor L. Joyprakash Singh S. K. Dutta North-Eastern Hill University Shillong Meghalaya IN Department of ECE Speech & Image Processing Laboratory Shillong

This paper aims to discuss the implementation of phoneme based Manipuri Keyword Spotting System (MKWSS). Manipuri is a scheduled indian language of Tibeto-Burman origin. Around 5 hours of read speech are collected from 4 male and 6 female speakers for development of database of MKWSS. The symbols of International Phonetic Alphabet (IPA)(revised in 2005) are used during the transcription of the data. A five state left to right Hidden Markov Model (HMM) with 32 mixture continuous density diagonal covariance Gaussian Mixture Model (GMM) per state is used to build a model for each phonetic unit. We have used HMM tool kit (HTK), version 3.4 for modeling the system. The system can recognize 29 phonemes and a non-speech event (silence) and will detect the present keywords formed by these phonemes. Continuous Speech data have been collected from 5 males and 8 females for analysing the performance of the system. The performance of the system depends on the ability of detection of the keywords. An overall performance of 65.24% is obtained from the phoneme based MKWSS.

关键词： Hidden Markov models Speech Vectors Speech recognition Databases Acoustics Feature extraction

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Automated conversion of 2D to 3D image using manifold learning

Automated conversion of 2D to 3D image using manifold learni...

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

作者： Nidhi Chahal Meghna Pippal Santanu Chaudhury Department of Electrical Engineering Indian Institute of Technology Delhi India

Automatic technique of 2D to 3D image conversion is proposed using manifold learning and sequential labeling which generates very reliable and accurate 3D depth maps that are very close to ground truth depths. In paper, LLE which is a non linear and neighborhood preserving embedding algorithm is used for depth estimation of a 2D image. And then, fixed point supervised learning algorithm is applied to construct consistent and smooth 3D output. The high dimensional data points or pixels of the input frames can be represented by a linear combination of its nearest neighbors and a lower dimensional point is reconstructed while preserving the local and geometric properties of the frames. The neighbors are assigned to each input point in the image data set and their weight vectors are computed that best linearly reconstruct the input point from its neighbors. To get the depth value of input point in new image, the reconstruction weights of its closest neighbors in training samples are multiplied with their corresponding ground truth depth values. The fixed point learning algorithm takes depths from manifold and other image features as input vectors and generates more consistent and accurate depth images for better 3D conversion.

关键词： Manifolds Three-dimensional displays image reconstruction Estimation Supervised learning image color analysis image edge detection

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