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Multi-modal image analysis for plant stress phenotyping 6th

Multi-modal image analysis for plant stress phenotyping

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

作者： Bhugra, Swati Anupama, Anupama Chaudhury, Santanu Lall, Brejesh Chugh, Archana Indian Institute of Technology Delhi Delhi India

ISBN: (纸本)9789811300196

Drought stress detection involves multi-modal image analysis with high spatio-temporal resolution. Identification of digital traits that characterizes drought stress response (DSR) is challenging due to high volume of image based features. Also, the labelled data that categorizes DSR are either unavailable or subjectively developed, which is a low-throughput and error-prone task. therefore, we propose a novel framework that provides an automated scoring of DSR based on multi-trait fusion. k-means clustering was used to extract latent drought clusters and the relevant traits were identified using Support Vector Machine-Recursive Feature Extraction (SVM-RFE). Using these traits, SVM based DSR classification model was constructed. the framework has been validated on visible and thermal shoot images of rice plants, yielding 95% accuracy. Various imaging modalities can be integrated with the proposed framework, thus making it scalable as no prior information about the DSR was assumed. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Support vector machines

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Dataset augmentation with synthetic images improves semantic segmentation 6th

Dataset augmentation with synthetic images improves semantic...

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

作者： Goyal, Manik Rajpura, Param Bojinov, Hristo Hegde, Ravi Varanasi Varanasi221005 India Indian Institute of Technology Gandhinagar Gandhinagar382355 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our approach is thus a promising solution to the problems of annotation and dataset collection. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantic Segmentation

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Trajectory based integrated features for action classification from depth data 6th

Trajectory based integrated features for action classificati...

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

作者： Shukla, Parul Arora, Noopur Biswas, Kanad K. Indian Institute of Technology Delhi India Works Application Singapore Singapore Bennett University Greater NoidaUttar Pradesh India

ISBN: (纸本)9789811300196

We present an approach for Human Action Recognition based on amalgamation of features from depth maps and body-joint data. this Integrated feature set consists of depth features based on gradient orientation and motion energy, in addition to features from 3D- skeleton data capturing its statistical details. Feature selection is carried out to extract a relevant set of features for action recognition. the resultant set of features are evaluated using SVM classifier. We validate our proposed method on various benchmark datasets for Action Recognition such as MSR-Daily Activity and UT-Kinect dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Metals

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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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Improved low resolution heterogeneous face recognition using re-ranking 6th

Improved low resolution heterogeneous face recognition using...

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

作者： Mudunuri, Sivaram Prasad Venkataramanan, Shashanka Biswas, Soma Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9789811300196

Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. this can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Face recognition

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Automatic synthesis of boolean expression and error detection from logic circuit sketches 6th

Automatic synthesis of boolean expression and error detectio...

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

作者： Dhiman, Sahil Garg, Pushpinder Sharma, Divya Chattopadhyay, Chiranjoy Indian Institute of Technology Jodhpur JodhpurRajasthan India

ISBN: (纸本)9789811300196

Automatic techniques to recognize and evaluate digital logic circuits are more efficient and require less human intervention, as compared to, traditional pen and paper methods. In this paper, we propose LEONARDO (Logic Expression fOrmatioN And eRror Detection framewOrk), a hierarchical approach to recognize boolean expression from hand drawn digital logic gate diagram. the key contributions in the proposed approach are: (i) a novel hierarchical framework to synthesize boolean expression from a hand drawn logic circuit diagram;and (ii) identification of anomalies in drawing. Extensive experimentation was performed through qualitative and quantitative analysis. Results were also compared with existing techniques proposed on the similar problem. Upon experimentation and analysis, our system proved to be more robust to user variability in design and yielded an accuracy of 95.2 %, which is a 4 % gain over others. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Error detection

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Flow-free video object segmentation 6th

Flow-free video object segmentation

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

作者： Vora, Aditya Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (纸本)9789811300196

Segmenting foreground object from a video is a challenging task because of large deformations of objects, occlusions, and background clutter. In this paper, we propose a frame-by-frame but computationally efficient approach for video object segmentation by clustering visually similar generic object segments throughout the video. Our algorithm segments object instances appearing in the video and then performs clustering in order to group visually similar segments into one cluster. Since the object that needs to be segmented appears in most part of the video, we can retrieve the foreground segments from the cluster having maximum number of segments. We then apply a track and fill approach in order to localize the object in the frames where the object segmentation framework fails to segment any object. Our algorithm performs comparably to the recent automatic methods for video object segmentation when benchmarked on DAVIS dataset while being computationally much faster. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Motion compensation

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

Homomorphic incremental directional averaging for noise supp...

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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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A generative adversarial network for tone mapping HDR images 6th

A generative adversarial network for tone mapping HDR images

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

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

引用

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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