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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1830 条记录，以下是931-940 订阅

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Efficient Training and Inference in Highly Temporal Activity Recognition 2

Efficient Training and Inference in Highly Temporal Activity...

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2nd international conference on image and Video processing, and Artificial Intelligence (IPVAI)

作者： Charkhabi, Masoud Rahurkar, Nivedita Stanford Univ 450 Serra Mall Stanford CA 94305 USA

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

High-performance Activity Recognition models from video data are difficult to train and deploy efficiently. We measure efficiency in performance, model size, and run-time;during training and inference. Researchers have demonstrated that 3D convolutions capture the space-time dynamics well [13]. The challenge is that 3D convolutions are computationally intensive. [8] Propose the Temporal Shift Module (TSM) for train-efficiency, and [5] proposes DeepCompression for inference-efficiency. TSM is a simple yet effective way to gain near 3D convolution performance at 2D convolution computation cost. We apply these efficiency techniques to a newly labeled activity recognition data set through transfer learning. Our labeling strategy is meant to create highly temporal activity. We benchmark against a 2D ResNet50 backbone trained on individual frames, and a multilayer 3DCNN on multi-frame short videos. Our contributions are: 1. A new highly temporal activity recognition dataset based on egoHands [1]. 2. results that show a 3D backbone on videos outperforms a 2D one on frames. 3. With TSM we achieve 5x train efficiency in run-time with negligible performance loss. 4. With Quantization alone we achieve 10x inference efficiency in model size with negligible performance loss.

关键词： Activity Recognition computer vision Machine Learning

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A Review of Motion Segmentation: Approaches and Major Challenges

A Review of Motion Segmentation: Approaches and Major Challe...

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international Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Jana Mattheus Hans Grobler Adnan M. Abu-Mahfouz University of Pretoria South Africa Council for Scientific and Industrial Research South Africa

ISBN: (数字)9781728195209

ISBN: (纸本)9781728195216

Motion segmentation has applications in, amongst others, robotics, traffic monitoring, sports analysis, inspection, video surveillance, compression, and video indexing. However, the performance of most methods is limited compared to human capabilities. Based on extensive literature the following challenges remain: occlusions, temporary stopping, missing data, and segmenting multiple objects. In this paper, several popular and state-of-the-art methods were reviewed, with the focus on the most important attributes. These methods were classified according to the main approach taken, namely image Difference, Optical Flow, Wavelet, Statistical, Layers, Manifold Clustering, Template Matching, and Deep Learning. The investigated methods are compared and major research challenges are highlighted. Based on the review, improvements are identified as a basis for future research.

关键词： Manifolds computer vision Motion segmentation Video surveillance Trajectory Video signal processing Sports

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A New Approach for Distorted image Restoration By Distance-based Correction and Cubic Spline Interpolation 2019

A New Approach for Distorted Image Restoration By Distance-b...

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Proceedings of the 2019 2nd international conference on Watermarking and image processing

作者： Min Qi Guanrong Yu Yuelei Xu School of Electronics and Information Northwestern Polytechnical University Dongda Town Xi'an Shaanxi China Unmanned System Research Institute Northwestern Polytechnical University Xi'an Shaanxi China

ISBN: (纸本)9781450372800

Fish-eye lenses are used more and more widely in the field of photography and computer vision nowadays. However, images captured with these lenses suffer from high barrel-type spatial distortion, so severely errors are brought into the camera calibration, feature extraction, parameter calculation and following analyses. This paper proposes a new and effective method to correct the high distortion. With the improved model in this paper, the degree of correction changes in accordance with the distance from the center pixel location. And the inverse mapping from corrected images space to the distorted images space based on the cubic spline interpolation method is taken to eliminate the vacant pixels in the corrected images caused by forwarding mapping. The final correction results are satisfactory by using this improved method.

关键词： barrel distortion cubic spline interpolation Fish-eye lens image correction inverse mapping

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Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots 2nd

Shot-Net: A Convolutional Neural Network for Classifying Dif...

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2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Foysal, Md. Ferdouse Ahmed Islam, Mohammad Shakirul Karim, Asif Neehal, Nafis Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh College of Engineering and IT Charles Darwin University Darwin Australia

ISBN: (纸本)9789811391804

Artificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different Machine Learning and computer vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as "Shot-Net" in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Data Analytics

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Research on Vehicle Control Technology of Brain-computer Interface Based on SSVEP 2

Research on Vehicle Control Technology of Brain-Computer Int...

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2nd international conference on image and Video processing, and Artificial Intelligence (IPVAI)

作者： Long, Sheng Zhou, Zongtan Yu, Yang Liu, Yadong Zhang, Nannan Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In this paper, we propose an asynchronous paradigm for controlling a car using steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) and conduct experimental tests on real car outside the laboratory. The paradigm uses six stimulation frequencies to classify targets by canonical correlation analysis (CCA) method and generates multi-task vehicle control strategies, including left and right turn signals, wipers, horns, doors and hazard lights. Four healthy volunteers participated in the online car control experiment, and the average correct rate reached 88.43%. Subject S1 showed the most satisfactory BCI-based performance, and its true positive rate and false positive rate were in line with expectations. The research shows the feasibility and effectiveness of the paradigm in automotive control applications, which lays the foundation for future research and development of related brain-controlled automotive technologies, thereby helping individuals with mobility impairments to provide supplements or alternatives, and can also provide an auxiliary vehicle driving strategy for healthy people.

关键词： Brain-computer interface (BCI) steady-state visual evoked potentials (SSVEP) canonical correlation analysis (CCA) brain-actuated car asynchronous control

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image enhancement using fuzzy logic techniques 2nd

Image enhancement using fuzzy logic techniques

引用

2nd international conference of the series Soft Computing: Theories and Applications, SoCTA 2017

作者： Mittal, Preeti Saini, R.K. Jain, Neeraj Kumar The Department of Mathematical Sciences and Computer Applications Bundelkhand University Jhansi India

ISBN: (纸本)9789811305887

image enhancement is the preprocessing task in digital image processing. It helps to improve the appearance or perception of the image so that the image can be used for analytics and human visual system. image enhancement techniques lie in three broad categories—spatial domain, frequency domain, and fuzzy domain-based enhancement. A lot of work has been done on image enhancement. Most of the work has been done/performed on grayscale image. This paper concentrates on image enhancement using fuzzy logic approach and gives an insight into previous research work and future perspectives. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image enhancement

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Improvement of stereo matching algorithm based on guided filtering and Kernel Regression 2

Improvement of stereo matching algorithm based on guided fil...

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2019 2nd international conference on Advanced Algorithms and Control Engineering, ICAACE 2019

作者： Cao, Ying Shan Liu, Jin Gen Wen, Tie Xiang Bi, Xu School of Information Engineering Wuhan University of Technology WuhanHubei430070 China

Stereo matching is one of the most dynamic fields in computer vision. Though its relevant research has already stepped into a mature stage, there are still certain challenges to obtain real-time and high-precision disparity maps from stereo image pairs. This paper presents a novel local stereo matching algorithm with better performance in edge preserving. In the first stage, this paper measures matching cost through combining truncated absolute differences (TAD) of the color and gradient. In the cost aggregation stage, this paper is creatively to combined the weighted guided filtering and adaptive steering kernel regression algorithm, which effectively preserves image edge and depth information. In the final stage, an adaptive steering kernel regression algorithm is employed in interpolation to refine the final disparity map. According to the Middlebury benchmark experiments, the algorithm proposed in this paper could have better performance than other local stereo matching algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Stereo image processing

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Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine vision 2nd

Automatic Classification of Normal and Affected Vegetables B...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Madgi, Manohar Danti, Ajit K. L. E. Institute of Technology Hubballi580030 India Bengaluru560074 India

ISBN: (纸本)9789811391866

This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Backpropagation

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Skeleton Joint Difference Maps for 3D Action Recognition with Convolutional Neural Networks 2nd

Skeleton Joint Difference Maps for 3D Action Recognition wit...

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2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Naveenkumar, M. Domnic, S. Department of Computer Applications National Institute of Technology Trichy India

ISBN: (纸本)9789811391804

Action recognition is a leading research topic in the field of computer vision. This paper proposes an effective method for action recognition task based on the skeleton data. Four features are proposed based on the joint differences from 3D skeleton data. From the differences of 3D coordinates of corresponding joints in successive frames, three maps are extracted related to x, y and z coordinates respectively and then these maps are encoded into 2D color images, named as Joint Difference Maps (JDMs). The fourth JDM is formed by mapping the individual x, y and z difference maps into red, green and blue values. Hence, the 3D action recognition problem is converted into 2D image classification problem. It enables us to fine tune CNNs to learn informative features for 3D action recognition problem. The proposed method achieved 79.30% recognition rate on UTD MHAD dataset. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Automatic Multiclass Classification of Foliar Leaf Diseases Using Statistical and Color Feature Extraction and Support Vector Machine 2nd

Automatic Multiclass Classification of Foliar Leaf Diseases ...

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2nd international conference on Computational Intelligence, Communication, and Business Analytics, CICBA 2018

作者： Datta, Aparajita Dey, Abhishek Dey, Kashi Nath Department of Computer Science and Engineering University of Calcutta Kolkata India Department of Computer Science Bethune College University of Calcutta Kolkata India Department of Computer Science and Engineering DSCSDEC JIS GROUP Kolkata India

ISBN: (纸本)9789811385773

Disease detection in crops and plants is essential for production of good and improved quality of food, life and a stable agricultural economy. It becomes tedious and time consuming to observe the infected parts of plants manually. Recourses with proper expertise are also required to have continuous monitoring. Digital image processing along with computer vision techniques can be applied to automate early detection of plant diseases and it can save significant amount of resources. In this paper, an automated approach based on image processing and machine learning techniques is proposed which can detect three major kinds of diseases Downy Mildew, Frogeye Leaf Spot and Septoria Leaf Blight that affects apple, grapes, soybean, tomatoes and many other major plants of economic value. Generally, leaves are the most affected part of the plants. So, instead of the whole plant, concentration is given on the leaf. In this paper, image pre-processing methods like noise removal and contrast enhancement followed by colour space transformation and k-means clustering is used to segment affected parts of soybean leaves, after that both texture and colour features are extracted from segmented samples and Support Vector Machine (SVM) classification is used to separate three kinds of diseases mentioned previously. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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