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Anomaly from motion: Unsupervised extraction of visual irregularity via motion prediction 6th

Anomaly from motion: Unsupervised extraction of visual irreg...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Majumder, Avishek Babu, R. Venkatesh Chakraborty, Anirban Indian Institute of Science BangaloreKarnataka560012 India

ISBN: (纸本)9789811300196

the problem of automatically extracting anomalous events from any given video is a problem that has been researched from the early days of computer vision. It has still not been fully solved, showing that it is indeed not a trivial problem. the various challenges involved are lack of proper definition, varying scene structure and objects of interest in the scene, just a few to name. In this paper we propose a novel method to extract outliers from motion alone. We employ a stacked LSTM encoder-decoder structure to model the regular motion patterns of the given video sequence. the discrepancy between the motion predicted using the model and the actual observed motion in the scene is analyzed to detect anomalous activities. We perform extensive experimentation on the benchmark datasets of crowd anomaly analysis. We report State of the Art results across all the datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computer vision

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Quantization Based Data Hiding Scheme for Efficient Quality Access Control of images Using DWT via Lifting

Quantization Based Data Hiding Scheme for Efficient Quality ...

引用

indian conference on computer vision, graphics & image processing (icvgip)

作者： Amit Phadikar Santi P. Maity Malay K. Kundu Department of Information Technology MCKV Institute of Engineering Howrah India Department of Information Technology Bengal Engineering and Science University Howrah India Machine Intelligence Unit & Center for Soft Computing Research Indian Statistical Institute Kolkata India

this paper proposes a transform domain data-hiding scheme for quality access control of images. the original image is decomposed into tiles by applying n-level lifting-based discrete wavelet transformation (DWT). A binary watermark image (external information) is spatially dispersed using the sequence of number generated by a secret key. the encoded watermark bits are then embedded into all DWT-coefficients of n th -level and only in the high-high (HH) coefficients of the subsequent levels using dither modulation (DM) but without complete self-noise suppression. It is well known that due to insertion of external information, there will be degradation in visual quality of the host image (cover). the degree of deterioration depends on the amount of external data insertion as well as step size used for DM. If this insertion process is reverted, better quality of images can be accessed. To achieve that goal, watermark bits are detected using minimum distance decoder and the remaining self-noise due to information embedding is suppressed to provide better quality of image. the simulation results have shown the validity of this claim.

关键词： Quantization Data encapsulation Access control Discrete wavelet transforms Watermarking Delta modulation Discrete transforms Tiles Degradation Decoding

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A generative adversarial network for tone mapping HDR images 6th

A generative adversarial network for tone mapping HDR images

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Patel, Vaibhav Amit Shah, Purvik Raman, Shanmuganathan Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar GandhinagarGujarat382007 India Indian Institute of Technology Gandhinagar GandhinagarGujarat382007 India

ISBN: (纸本)9789811300196

A tone mapping operator converts High Dynamic Range (HDR) images to Low Dynamic Range (LDR) images, which can be seen on LDR displays. there has been a lot of research done in the direction of an optimal Tone Mapping Operator which maximizes Tone Mapping Quality Index (TMQI). However, since all the methods approximate Human vision System in one or different way, none of them works for every type of images. We are proposing a novel generative adversarial network to learn a combination of these tone mapping operators. In order to get pixel level accuracy, we are using residual connections between same sized network layers. We compare this method with some of the existing tone mapping operators and observe that our method generates images with comparably high TMQI and indeed works on many different types of images. Because of the residual connections, the network can be scaled to very high dimensional images. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Generative adversarial networks

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Homomorphic incremental directional averaging for noise suppression in SAR images 6th

Homomorphic incremental directional averaging for noise supp...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Aswatha, Shashaank M. Mukhopadhyay, Jayanta Biswas, Prabir K. Aikat, Subhas Indian Institute Technology Kharagpur KharagpurWest Bengal721 302 India

ISBN: (纸本)9789811300196

In recent days, it is found that Synthetic Aperture Radar (SAR) images can be a very useful mode for observing and understanding the surface of Earth. the images formed under SAR modality usually suffer from multiplicative noise, particularly in single-look-complex (SLC) mode. there are extensive works in the literature for denoising SAR data, which are usually applied on amplitude data, or on coherency/covariance data. In this paper, we propose a two-channel filtering technique for noise suppression in complex SAR data. the rectangular format of complex SAR data is represented in phasor form to execute noise filtering over amplitude and phase independently, and then converted back to the rectangular format for subsequent applications. In this approach, it is observed that, the surface texture information is visibly retained while suppressing the noise considerably well, in comparison to reference multi-look image. As an application, we show the advantage of proposed noise suppression technique in classification of SAR images. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Synthetic aperture radar

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Automated translation of human postures from kinect data to labanotation 6th

Automated translation of human postures from kinect data to ...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Sankhla, Anindhya Kalangutkar, Vinanti Bhuyan, Himadri B. G. S. Mallick, Tanwi Nautiyal, Vivek Das, Partha Pratim Majumdar, Arun Kumar Indian Institute of Technology Kharagpur Kharagpur721302 India

ISBN: (纸本)9789811300196

We present a non-intrusive automated system to translate human postures into Labanotation, a graphical notation for human postures and movements. the system uses Kinect to capture the human postures, identifies the positions and formations of the four major limbs: two hands and two legs, converts to the vocabulary of Labanotation and finally translates to a parseable LabanXML representation. We use the skeleton stream to classify the formations of the limbs using multi-class support vector machines. Encoding to XML is performed based on Labanotation specification. A data set of postures is created and annotated for training the classifier and to test its performance. We achieve 80% to 90% accuracy for the 4 limbs. the system can be used as an effective front-end for posture analysis applications in various areas like dance and sports where predefined postures form the basis for analysis and interpretation. the parseability of XML makes it easy for integration in a platform independent manner. © Springer Nature Singapore Pte Ltd. 2018.

关键词： XML

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Accelerating Native Inference Model Performance in Edge Devices using TensorRT 6

Accelerating Native Inference Model Performance in Edge Devi...

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6th IEEE International conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Gopiktrishna, P.B. Marion Lincy G, Rubell Rishekeeshan, Abhishek Deekshitha Indian Institute of Information Technology Computer Science and Engineering Kottayam India AiRender Technology Pvt Ltd Bangalore India

ISBN: (纸本)9798350381689

In this era, deep learning is becoming increasingly popular for solving real-world problems. Due to the extremely high processing power required to execute the most complex deep learning models, graphics processing Units(GPUs) and Neural processing Units(NPUs) are essential for optimal execution. However, it is challenging to incorporate these high-performance processors in every edge device like Internet of things gadgets, embedded systems, and cell phones to execute complex inference models. Hence, deep learning models must be optimized to perform well on edge devices with limited memory, power, and computing capabilities. Optimizing the inference models to accelerate performance on edge devices is one of the significant issues facing, especially in complex computation domains like computer vision. TensorRT, introduced by NVIDIA, is one of the best approaches used to optimize inference models, by accelerating their performance on edge devices without compromising the model accuracy. It presents a variety of optimization approaches, including layer fusion, quantization, dynamic tensor memory management, kernel auto tuning, and precision calibration. Every optimization technique is contingent upon the problem that have selected and the use of all optimization approaches has the potential to significantly speed up the performance of an inference model. this paper explores how TensorRT accelerates the performance of inference models like ResNet-50, VGG16, and Inception V3 in an image classification problem, and the results are analyzed with its native inference models. the utilization of TensorRT in edge devices is expanding significantly, reflecting a growing scope for enhanced performance and efficiency in processing complex neural networks at the edge. © 2024 IEEE.

关键词： graphics processing unit

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SSIM-based joint bit-allocation using frame model parameters for 3D video coding 6th

SSIM-based joint bit-allocation using frame model parameters...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Harshalatha, Y. Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9789811300196

Optimum bit-allocation between texture video and depth map in 3D video results in better virtual view quality. To incorporate this, rate distortion optimization (RDO) property is used. the RDO in 3D video implies minimization of synthesis distortion at available rate. Several bit-allocation methods proposed in literature have not considered perceptual quality improvement. In this paper, we propose bit-allocation criteria that results in better visual quality of synthesized view. To achieve this, visual quality metrics are to be incorporated and structural similarity (SSIM) index is one of the metric that measures perceived quality. As SSIM gives similarity measure, we used dSSIM as distortion metric in mode decision and motion estimation instead of traditional metrics like mean square error (MSE) or sum of squared error (SSE). Synthesis distortion is modeled using dSSIM and joint bit-allocation is formulated as optimization problem that is solved using Lagrange multiplier method. Model parameters are determined at frame level for more accurate calculation of quantization parameters. BD-Rate evaluation shows a reduction in bit rate with improved SSIM. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Motion estimation

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Recognizing human activities in videos using improved dense trajectories over LSTM 6th

Recognizing human activities in videos using improved dense ...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Singh, Krit Karan Mukherjee, Snehasis Indian Institute of Information Technology SriCity Sri City India

ISBN: (纸本)9789811300196

We propose a deep learning based technique to classify actions based on Long Short Term Memory (LSTM) networks. the proposed scheme first learns spatial temporal features from the video, using an extension of the Convolutional Neural Networks (CNN) to 3D. A Recurrent Neural Network (RNN) is then trained to classify each sequence considering the temporal evolution of the learned features for each time step. Experimental results on the CMU MoCap, UCF 101, Hollywood 2 dataset show the efficacy of the proposed approach. We extend the proposed framework with an efficient motion feature, to enable handling significant camera motion. the proposed approach outperforms the existing deep models for each dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Long short-term memory

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Detecting missed and anomalous action segments using approximate string matching algorithm 6th

Detecting missed and anomalous action segments using approxi...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Jain, Hiteshi Harit, Gaurav Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811300196

We forget action steps and perform some unwanted action movements as amateur performers during our daily exercise routine, dance performances, etc. To improve our proficiency, it is important that we get a feedback on our performances in terms of where we went wrong. In this paper, we propose a framework for analyzing and issuing reports of action segments that were missed or anomalously performed. this involves comparing the performed sequence with the standard action sequence and notifying when misalignments occur. We propose an exemplar based Approximate String Matching (ASM) technique for detecting such anomalous and missing segments in action sequences. We compare the results with those obtained from the conventional Dynamic Time Warping (DTW) algorithm for sequence alignment. It is seen that the alignment of the action sequences under conventional DTW fails in the presence of missed action segments and anomalous segments due to its boundary condition constraints. the performance of the two techniques has been tested on a complex aperiodic human action dataset with Warm up exercise sequences that we developed from correct and incorrect executions by multiple people. the proposed ASM technique shows promising alignment and missed/anomalous notification results over this dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： String searching algorithms

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Transfer learning by finetuning pretrained CNNs entirely with synthetic images 6th

Transfer learning by finetuning pretrained CNNs entirely wit...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Rajpura, Param Aggarwal, Alakh Goyal, Manik Gupta, Sanchit Talukdar, Jonti Bojinov, Hristo Hegde, Ravi Indian Institute of Technology Gandhinagar Gandhinagar382355 India Varanasi Varanasi221005 India BITS Hyderabad Hyderabad500078 India Nirma Institute of Technology Ahmedabad382481 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

We show that finetuning pretrained CNNs entirely on synthetic images is an effective strategy to achieve transfer learning. We apply this strategy for detecting packaged food products clustered in refrigerator scenes. A CNN pretrained on the COCO dataset and fine-tuned with our 4000 synthetic images achieves mean average precision (mAP @ 0.5-IOU) of 52.59 on a test set of real images (150 distinct products as objects of interest and 25 distractor objects) in comparison to a value of 24.15 achieved without such finetuning. the synthetic images were rendered with freely available 3D models with variations in parameters like color, texture and viewpoint without a high emphasis on photorealism. We analyze factors like training data set size, cue variances, 3D model dictionary size and network architecture for their influence on the transfer learning performance. Additionally, training strategies like fine-tuning with selected layers and early stopping which affect transfer learning from synthetic scenes to real scenes were explored. this approach is promising in scenarios where limited training data is available. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Network architecture

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