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EHANet: Efficient Hybrid Attention Network towards Real-time Semantic Segmentation

EHANet: Efficient Hybrid Attention Network towards Real-time...

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International conference on computer and Communications (ICCC)

作者： Zhenfeng Xue Weijie Mao Wei Jiang Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Semantic segmentation suffers from the contradiction between inference speed and model accuracy. State-of-the-art real-time methods improve inference rapidity by sacrificing feature representation and model capacity. this paper proposes a novel Efficient Hybrid Attention Network (EHANet) to remedy this dilemma. the EHANet follows an encoder-decoder structure, where the encoder is composed of Reduced Basic-Block (RBB) with very few parameters. At decoding stages, a hybrid attention mechanism is designed to re-weight the feature map. the attention mechanism employs contextual attention for deep features and spatial attention for shallow features. the proposed architecture makes a trade off between inference speed and segmentation performance. As a result, the proposed model achieves 66.1% mIoU on the Cityscapes validation set. Meanwhile, it can operate at a speed of 113 FPS on one NVIDIA Titan XP GPU for an input image with size of 1024×512.

关键词： image segmentation Visualization conferences Semantics graphics processing units Real-time systems Decoding

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PSNR and robustness comparison between DCT and SVD based digital image watermarking against different noise and attacks 6th

PSNR and robustness comparison between DCT and SVD based dig...

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6th International conference on Advancement of Science and Technology, ICAST 2018

作者： Rathi, Tarun Maheshwari, Rudra P. Tripathy, Manoj Chaudhary, Vikas Department of Electrical and Computer Engineering Maddawalabu University Bale Robe Ethiopia Raj Kumar Goel Institute of Technology Ghaziabad India Department of Electrical Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Science and Engineering KIET Ghaziabad Ghaziabad India

ISBN: (纸本)9783030153564

this paper presents a comparison of extracted watermark image quality and robustness between DCT and SVD based digital watermarking techniques. Here two transform domain DCT and SVD based watermarking algorithms are implemented. For better comparison purpose original host image and watermark image are kept same in both algorithms. Before extracting watermark, different image processing attacks and noise are inserted in watermarked image then DCT and SVD based watermark extraction are performed to check PSNR and robustness against those image processing attacks and noise. Obtained experimental results show that between these two algorithms, robustness, and quality of extracted watermark images are better in SVD based watermarking method. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2019 Published by Springer Nature Switzerland AG 2019. All Rights Reserved.

关键词： Discrete cosine transforms

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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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Adaptive Ensemble Clustering for image Segmentation in Remote Sensing 6th

Adaptive Ensemble Clustering for Image Segmentation in Remot...

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6th International conference on Communications, Signal processing, and Systems (ICCSPS)

作者： Yao, Tingting Liu, Chang Deng, Zhian Liu, Xiaoming Liu, Jiacheng Dalian Maritime Univ Dalian Peoples R China Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9789811065712;9789811065705

image segmentation is a fundamental computer vision task. Although many approaches have been proposed, obtaining accurate results in some special applications are still not easy. In this paper, we propose a novel image segmentation method for remote sensing based on adaptive cluster ensemble learning. the clustering parameter of each image is calculated with affinity propagation automatically. then, multiple clusterers are trained separately and the predictions of them are combined under the ensemble learning framework. In this way, the robustness of each clusterer could be enhanced. Experimental results demonstrate the effectiveness of our proposed method.

关键词： Ensemble learning Affinity propagation Remote sensing image segmentation

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Zero-shot Learning using Graph Regularized Latent Discriminative Cross-domain Triplets 2018

Zero-shot Learning using Graph Regularized Latent Discrimina...

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

作者： Omkar Gune Meet Vora Biplab Banerjee Subhasis Chaudhuri Indian Institute of Technology Bombay India Indian Institute of Technology Roorkee India

ISBN: (纸本)9781450366151

Zero-shot learning (ZSL) for visual recognition aims at identifying the previously unseen class samples given a trained model on the labeled visual samples of seen classes and additional class-level semantic side information for all classes. Often ZSL is tackled by learning an embedding function from the visual to semantic space or vice-versa. However, learning this mapping often results in loss of discriminative property of learned embedding space, thus severely compromising the recognition performance on the test samples. In order to ensure improved discrimination in the embedding space, we introduce a ZSL framework by leveraging the intuitive idea of cross-domain triplets based metric learning for learning such a space. Additionally, we introduce a novel graph Laplacian based regularizer which aligns the graph structures of the visual and semantic spaces in the learned embedding space. Simultaneously optimizing both the criteria results in a compact, discriminative, and meaningful embedding space, which is experimentally found to be superior to most of its existing counterparts on both the standard ZSL (AwA and CUB) and the challenging generalized ZSL (AwA1, AwA2, CUB) settings.

关键词： Zero-shot learning cross-domain alignment

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A Proposal of FPGA-Based Low Cost and Power Efficient Autonomous Fruit Harvester

A Proposal of FPGA-Based Low Cost and Power Efficient Autono...

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International conference on Control, Automation and Robotics (ICCAR)

作者： Kumar Nilay Swarnabha Mandal Yash Agarwal Rishabh Gupta Manthan Patel Sumeet Kumar Poojan Shah Sombit Dey Annanya Indian Institute of Technology Kharagpur India

ISBN: (数字)9781728161396

ISBN: (纸本)9781728161402

In this paper, we present a power-efficient and low- cost prototype of a robotic harvester which employs multiple subsystems such as fruit detection, odometry, localization, proficient manipulation through computer vision, deep learning and a novel end-effector design. Fruit Plucking is performed using an end effector, and 3-degree of freedom (DOF) arm (made out of the integration of two linear actuators and a rotating platform) consolidated with a 4-wheeled differential drive mobile platform. Effective implementation of the visual processing is executed on the FPGA Fabric of the Xilinx PYNQ-Z2 Board, which accelerates Deep Neural Networks (DNNs) with improved Latency and Energy Efficiency as compared to a CPU or GPU based implementation.

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Texture and Colour Gradient Features for Grade analysis of Pomegranate and Mango Fruits using kernel-SVM Classifiers

Texture and Colour Gradient Features for Grade analysis of P...

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Yogeswararao Gurubelli R. Malmathanraj P. Palanisamy National Institute of Technology Tiruchirappalli India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

there is an extended demand for quality fruits and vegetables for processing into juice, wine and syrup in today's competitive market. Traditional fruit and vegetable processing and grading of good and fresh quality is time consuming and requires more skilled labour. computer vision and Machine learning approaches are the best solutions to the above mentioned problem. the present paper implements a novel approach of grade classification of pomegranate and mango fruits with texture and colour gradient features. Texture of the fruits are modelled using structural features using local binary pattern (LBP) and statistical features using pixel run length matrix (PRLM) and GLCM, while colour gradients (CG) of the fruits are calculated using average colour gradients, variances and colour coordinates of the three primary colours red, green and blue. Kernel support vector machine (KSVM) is used to grade/classify the extracted features from the proposed and existing methods. the statistical performance results show that the proposed approach is effective in grade classification and defect identification of the fruits with varying texture and colour gradients to an acceptable degree.

关键词： image color analysis Feature extraction computer vision Measurement Training Testing Machine learning

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Bag of Visual Words based Correlation Filter Tracker (BoVW-CFT) 18

Bag of Visual Words based Correlation Filter Tracker (BoVW-C...

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

作者： Priya Mariam Raju Deepak Mishra Gorthi Rama Krishna Sai Subrahmanyam Indian Institute of Space Science and Technology Thiruvananthapuram Kerala Indian Institute of Technology Tirupati Andhra Pradesh

ISBN: (纸本)9781450366151

Accurate and robust visual object tracking is one of the most challenging computer vision problems. Recently, discriminative correlation filter trackers have shown promising results on benchmark datasets with continuous performance improvements in tracking accuracy and robustness. Still, these algorithms fail to track as the target object and background conditions undergo drastic changes over time. they are also incapable to resume tracking once the target is lost, limiting the ability to track long term. the proposed BoVW-CFT is a classifier-based generic technique to handle tracking uncertainties in correlation filter trackers. Tracking failures in correlation trackers are automatically identified and an image classifier with training, testing and online update stages is proposed as detector in the tracking scenario using Bag of Visual Words (BoVW) features. the proposed detector falls under the parts based model and is quite well suited in the tracking framework. Further, the online training stage in the proposed framework with updated model or training samples, incorporates temporal information, helping to detect rotated, blurred and scaled versions of the target. On detecting a target loss in the correlation tracker, the trained classifier, referred to as detector, is invoked to re-initialize the tracker with the actual target location. therefore, for each tracking uncertainty, two output patches are obtained, one each from the base tracker and the classifier. the final target location is estimated using the normalized cross-correlation with the initial target patch. the method has the advantages of mitigating the model drift in correlation trackers and learns a robust model that tracks long term. Extensive experimental results demonstrate an improvement of 4.1% in the expected overlap, 1.86% in accuracy and 15.46% in robustness on VOT2016 and 1.82% in overlap precision, 2.32% in AUC and 2.87% in success rates on OTB100.

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Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences 18

Epipolar Geometry based Learning of Multi-view Depth and Ego...

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

作者： Vignesh Prasad Dipanjan Das Brojeshwar Bhowmick Embedded Systems & Robotics TCS Research & Innovation Kolkata

ISBN: (纸本)9781450366151

Deep approaches to predict monocular depth and ego-motion have grown in recent years due to their ability to produce dense depth from monocular images. the main idea behind them is to optimize the photometric consistency over image sequences by warping one view into another, similar to direct visual odometry methods. One major drawback is that these methods infer depth from a single view, which might not effectively capture the relation between pixels. Moreover, simply minimizing the photometric loss does not ensure proper pixel correspondences, which is a key factor for accurate depth and pose *** contrast, we propose a 2-view depth network to infer the scene depth from consecutive frames, thereby learning inter-pixel relationships. To ensure better correspondences, thereby better geometric understanding, we propose incorporating epipolar constraints to make the learning more geometrically sound. We use the Essential matrix obtained using Nistér's Five Point Algorithm, to enforce meaningful geometric constraints, rather than using it as training labels. this allows us to use lesser no. of trainable parameters compared to state-of-the-art methods. the proposed method results in better depth images and pose estimates, which capture the scene structure and motion in a better way. Such a geometrically constrained learning performs successfully even in cases where simply minimizing the photometric error would fail.

关键词： computer vision

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De-husked Coconut Quality Evaluation using image processing and Machine Learning Techniques 19

De-husked Coconut Quality Evaluation using Image Processing ...

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Proceedings of the 6th International conference on Bioinformatics Research and Applications

作者： Tito C. Lim Jaedy O. Torregosa Aubrey Rose A. Pescadero Rodrigo S. Pangantihon University of Mindanao College of Engineering Education Computer Engineering Program Matina Davao City Philippines

ISBN: (纸本)9781450372183

Qualitative evaluation provides the basis for determining if the quality of products meets the target specifications. Manual evaluation of de-husked coconuts is still being performed by coconut farmers, however, it is time consuming and costly. Ergo this study aiming to replace the manual inspection, a prototype was developed for objective and automated quality evaluation of de-husked coconuts through the application of computer vision and machine learning, identifying good-quality de-husked coconuts from defective ones with respect to its RGB color space. JavaFX platform was utilized to create the system performing K-Nearest Neighbor and Arduino technology played a significant role in the hardware control of the device. the image samples were captured by a CMOS camera in an imaging chamber with invariant illumination on top of a conveyor belt. image processing is done to get the required features of the sample and by comparing the average RGB value from the custom dataset, then the maturity level of the coconut is determined. With the accuracy of 86.67%, the system is able to evaluate de-husked coconuts which are good for further processing used in export and premature coconuts that are to be rejected.

关键词： OpenCV k-NN JavaFX Machine learning Coconut quality

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