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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Non-uniform deblurring from blurry/noisy image Pairs 4th

Non-uniform deblurring from blurry/noisy image Pairs

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4th international conference on computer vision and image processing, cvip 2019

作者： Deepa, P.L. Jiji, C.V. Mar Baselios College of Engineering and Technology Thiruvananthapuram India College of Engineering Thiruvananthapuram India

ISBN: (纸本)9789811540141

In this paper, we address the problem of recovering a sharp image from its non-uniformly blurred version making use of a known but noisy version of the same scene. The recovery process includes three main steps - motion estimation, segmentation and uniform deblurring. The noisy image is first denoised and then used as a reference image for estimating the motion occurred in the non-uniformly blurred image. From the obtained motion vectors, the blurred image is segmented into image blocks encountered with uniform motion. To deblur these uniformly blurred segments, we use a two step process where we first generate an unnatural representation under an l0 minimization frame work followed by a hyper-Laplacian prior based non-blind deconvolution. The resulting deblurred segments are finally concatenated to form the output image. The proposed method gives better results in comparison with other state of the art methods. © Springer Nature Singapore Pte Ltd 2020.

关键词： Motion estimation

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Feature learning for effective content-based image retrieval 4th

Feature learning for effective content-based image retrieval

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4th international conference on computer vision and image processing, cvip 2019

作者： Marab, Snehal Pawar, Meenakshi SVERI PandharpurMaharashtra India

ISBN: (纸本)9789811540141

Increase in the use of mobile phones for scene capturing turns into the exponential increase in the size of digital libraries. Content-Based image Retrieval (CBIR) is an effective solution to handle this enormous data. Most of the existing approaches are relied on the hand-crafted feature extraction for CBIR. These approaches could fail in large-scale databases. Thus, in this paper, we have proposed an end-to-end deep network for CBIR. Proposed network is characterized by the residual learning and thus does not undergoes the vanishing gradient problem. Performance of the proposed method has been evaluated on two benchmark databases namely Corel-10K and GHIM-10K with the help of two evaluation parameters i.e. recall and precision. Comparison with the state-of-the-art methods reveiles that proposed method outperforms existing methods by a large margin on all of the databases. © Springer Nature Singapore Pte Ltd 2020.

关键词： Digital libraries

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Fast Ship Detection in Remote Sensing images Based on Multi-Attention Mechanism 5

Fast Ship Detection in Remote Sensing Images Based on Multi-...

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5th international conference on Algorithms, Computing and Systems (ICACS)

作者： Huang, Jian Chen, Zhuo Xu, Hao Zhang, Xiushan Naval Univ Engn Sch Elect Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781450385084

With the continuous development of deep learning in computer vision, object detection technology is constantly employed for processing remote sensing images. Especially, ship detection has become a significant and challenging task due to complex environmental factors (strong waves, clouds interference, etc.) and object issues (orientation, scale variety, density, etc.). Current detection methods pay more attention to the detection accuracy while ignoring the detection speed. In contrast with accuracy, detection speed is more important in some cases such as marine rescue and vessel tracking. Aiming at addressing these problems, we propose an enhanced YOLOv4(C-YOLOv4) which contains the feature fusion attention module (FAM) with a channel correlation loss(C-loss). C-loss is proposed to constrain the relations between object classes and channels while maintaining the intra-class and the inter-class separability. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. According to the experimental results, our proposed approach outperforms the baselines.

关键词： Object detection Remote sensing image Convolutional neural networks Ship detection

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A convolutional fuzzy min-max neural network for image classification 4th

A convolutional fuzzy min-max neural network for image class...

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4th international conference on computer vision and image processing, cvip 2019

作者： Chavan, Trupti R. Nandedkar, Abhijeet V. SGGS Institute of Engineering and Technology NandedMaharashtra431606 India

ISBN: (纸本)9789811540172

Convolutional neural network (CNN) is a well established practice for image classification. In order to learn new classes without forgetting learned ones, CNN models are trained in offline manner which involves re-training of a network considering seen as well as unseen data samples. However, such training takes too much time. This problem is addressed using proposed convolutional fuzzy min-max neural network (CFMNN) avoiding the re-training process. In CFMNN, the online learning ability is added to network by introducing the idea of hyperbox fuzzy sets for CNNs. To evaluate the performance of CFMNN, benchmark datasets such as MNIST, Caltech-101 and CIFAR-100 are used. The experimental results show that drastic reduction in the training time is achieved for online learning of CFMNN. Moreover, compared to existing methods, the proposed CFMNN has compatible or better accuracy. © Springer Nature Singapore Pte Ltd 2020.

关键词： image classification

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Multimodal query based approach for document image retrieval 4th

Multimodal query based approach for document image retrieval

引用

4th international conference on computer vision and image processing, cvip 2019

作者： Nandedkar, Amit V. Nandedkar, Abhijeet V. SGGS Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789811540141

Scanning and storage of documents are regular practices. Retrieval of such documents is necessary to support office work. In this paper, a novel multimodal query based approach for retrieving documents using text, non-text contents is presented. This work focuses on logos, stamps, signatures for non-text query;and dates and keywords for text query to do retrieval. The proposed algorithm is called as multimodal document image retrieval algorithm (MDIRA), uses separation of text and non-text to simplify document indexing using both textual and non-textual contents. A single feature space for non-text contents is proposed for indexing. Various date formats can be recognized using regular expressions and mapped to uniform representation useful for indexing and retrieval. The proposed algorithm supports formation of multimodal queries using multiple attributes of documents. Results on a publicly available color document dataset, show the effectiveness of the proposed technique for document retrieval. © Springer Nature Singapore Pte Ltd 2020.

关键词： Indexing (of information)

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A Multi-objective Assignment Algorithm for UAVs in Multiple Task with Different Workloads

A Multi-objective Assignment Algorithm for UAVs in Multiple ...

引用

2023 Asia-Pacific conference on image processing, Electronics and computers, IPEC 2023

作者： Ding, Rui Zhu, Yuhan Feng, Xianbin Chai, Baojie Ji, Ran College of Computer Science and Information Technology Mudanjiang Normal University Heilongjiang Mudanjiang157000 China

ISBN: (纸本)9781665457033

UAVs have been widely used in military and civilian fields. The research on UAV task assignment mainly involves trajectory planning, task assignment, and specific applications for various scenarios. Moreover, most of the previous studies are for single UAV. Studies for swarm of UAVs are relatively few. In this paper, we focus on the multi-objective assignment algorithm of UAVs for multiple tasks which have homogeneous application scenarios with different locations, same priorities and different workloads. First, a coding method that can simultaneously reflect path planning and task allocation is designed, Based on this, a multiple task assignment algorithm for UAVs swarm is designed, In order to balance the tasks completion time, the workload of each task and the total transfer distance, a multi-objective assignment model is further constructed, and the complete multi-objective UAVs task assignment algorithm is proposed. The task assignment scheme obtained by the algorithm can make the UAVs complete all tasks in a balanced time and resource consumption. The experimental results show that our proposed method can determine the UAVs task assignment plan effectively and efficiently. © 2023 IEEE.

关键词： Multiobjective optimization

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Automated industrial quality control of pipe stacks using computer vision 4th

Automated industrial quality control of pipe stacks using co...

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4th international conference on computer vision and image processing, cvip 2019

作者： Chatterjee, Sayantan Chaudhuri, Bidyut B. Nandi, Gora C. Department of Robotics and Artificial Intelligence Indian Institute of Information Technology Allahabad AllahabadUttar Pradesh211012 India Department of Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata KolkataWest Bengal700108 India

ISBN: (纸本)9789811540141

In this work, we describe an automated quality assurance system for pipes in warehouses and yards using simple handheld and mobile equipment like smartphone cameras. Currently, quality inspection for bent and crooked pipe ends is done manually, which entails additional labour costs and is relatively slower than a mechanised approach. We propose an efficient and robust method of detecting the perfectly circular cross-sections of the pipes in stacks using an adaptive variation of Hough Transform algorithm. As a multistage approach, our proposed method first intensifies the foreground features relative to the background and then applies Canny edge detection to obtain the gradient details. The gradient directions are in turn fed to the modified Hough Transform algorithm using Hough gradient to detect the cross-sections. The novel Hough Transform modification features a region-based processing in a "coarse to fine" manner by dividing the image into smaller grids and detecting circular cross-sections per grid, which has computational advantages of using a smaller accumulator and is less memory intensive. Experiments were performed on real industrial images to validate the efficiency of the proposed algorithm and the results show that the proposed method can successfully and accurately highlight the perfectly circular cross-sections while leaving the faulty pipe-ends undetected. © Springer Nature Singapore Pte Ltd 2020.

关键词： Hough transforms

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Improved performance of visual concept detection in images using bagging approach with support vector machines 4th

Improved performance of visual concept detection in images u...

引用

4th international conference on computer vision and image processing, cvip 2019

作者： Patil, Sanjay M. Bhoyar, Kishor K. Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Information Technology Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9789811540172

With rapid advances in imaging devices and internet, millions of images are uploaded on the internet without much information about the image. An efficient method is necessary for detecting the concept of the desired image from this vast collection of images. In this paper, Support Vector Machine (SVM) based architecture is presented to detect concept of a given input image. To enhance the performance of proposed system, a bagging approach is implemented. Color moments, HSV Color Histogram, Grey level co-occurrence matrix, Wavelet Transform and Edge orientation histogram are used for image representation purpose. These low-level feature descriptors are used to train multiple SVM models. The final concept of the query image is obtained by voting from outputs of these multiple models. The proposed system is evaluated on Wang’s Corel 10K. Results of proposed system indicate its improved performance over existing systems. © Springer Nature Singapore Pte Ltd 2020.

关键词： Support vector machines

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A study on deep learning for breast cancer detection in histopathological images 4th

A study on deep learning for breast cancer detection in hist...

引用

4th international conference on computer vision and image processing, cvip 2019

作者： Singh, Oinam Vivek Choudhary, Prakash Thongam, Khelchandra Department of Computer Science and Engineering NIT Manipur Imphal India Department of Computer Science and Engineering NIT Hamipur Hamirpur India

ISBN: (纸本)9789811540172

Pathological examination is the most accurate method for the diagnosis of cancer. Breast cancer histopathology evaluation analyses the chemical and cellular characteristics of the cells of a suspicious breast tumor. A computer-aided automatic classifier with the help of machine learning can improve the diagnosis system in terms of accuracy and time consumption. These types of system can automatically distinguish a benign and malignant pattern in a breast histopathology image. It can reduce the workload of pathologists and can provide a more accurate process. In recent years, like in other areas, deep networks have also attracted for histopathology image analysis. Convolution Neural Network has become a preferred choice for images analysis including breast histopathology. In this paper, we review various deep learning concepts applied to breast cancer histopathology analysis and summarizes contributions to this field. We present a summary of the recent developments and a discussion about the best practices done using deep in breast histopathology analysis and improvements that can be done in future research. © Springer Nature Singapore Pte Ltd 2020.

关键词： image analysis

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Asymmetric wide tele camera fusion for high fidelity digital zoom 4th

Asymmetric wide tele camera fusion for high fidelity digital...

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4th international conference on computer vision and image processing, cvip 2019

作者： Manne, Sai Kumar Reddy Prasad, B. H. Pawan Rosh, K. S. Green Samsung R&D Institute Bangalore Bangalore560037 India

ISBN: (纸本)9789811540141

Asymmetric multi-camera systems are growing popular among smartphone manufacturers due to their ability to enhance image quality in applications such as low light imaging and camera zoom. One such multi-camera system is a tele-wide configuration with single or multiple tele and wide cameras. The images from these multiple cameras can be fused to generate an output image with improved image quality as opposed to individual input images. Major challenges in this problem include handling of photometric and Field-of-View differences between frames from an asymmetric camera system. In this paper, novel techniques for both multi-camera image fusion as well as multi-camera transition addressing the aforementioned challenges to create a seamless user experience are presented. The proposed method is evaluated qualitatively using comparisons against the existing method in Galaxy Note 8 and quantitatively against similar methods published in literature, showing an improvement in PSNR of 20% for synthetic datasets and 3% improvement for real datasets. © Springer Nature Singapore Pte Ltd 2020.

关键词： image quality

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