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

共 1407 条记录，以下是401-410 订阅

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The System Design for Improving Vechicle Detection Precision Based on image processing

The System Design for Improving Vechicle Detection Precision...

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international conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Li Congsheng He Zhaoyang Harbin Inst Technol Weihai Weihai 264209 Shandong Peoples R China

ISBN: (纸本)9780769559483

Research on moving vehicle detection based on Video and image processing is a significant research topic in image processing and computer vision, and is also a basic and key work in intelligent traffic system field, which directly affects the follow-on object identification and tracking, analysis and understanding of the traffic scene. In this paper detection of moving vehicles has been studied, analyed and summarized the existing moving object detection technology, an moving vehicle detection system is introduced and designed. In the system the improved frame difference method combined with image contrast enhancement and morphological filter is proposed, which can effectively solve the background difference method and frame difference method can not extract the regions of interest effectively, besides, using a connected region labeling method to fill the voids in the middle of the vehicle image, the target area becomed more complete and clear. Using this kind of system can reduce computation, improve identification speed, and receive better robustness and higher recognition effect to recognize vehicles more efficiently. Therefore the research contents and the system design have the practical significance to the research of vehicle detection technology based on the video.

关键词： ITS Vehicle detection system Frame difference method image processing

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Appearance-Invariant Entry-Exit Matching Using Visual Soft Biometric Traits 5th

Appearance-Invariant Entry-Exit Matching Using Visual Soft B...

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5th international conference on computer vision and image processing, cvip 2020

作者： Vinay Kumar, V. Nagabhushan, P. Department of Studies in Computer Science University of Mysore Mysuru India Indian Institute of Information Technology Allahabad Prayagraj India

ISBN: (纸本)9789811610851

The problem of appearance invariant subject re-identification for Entry-Exit surveillance applications is addressed. A novel Semantic Entry-Exit matching model that makes use of ancillary information about subjects such as height, build, complexion, and clothing color to endorse exit of every subject who had entered private area is proposed in this paper. The proposed method is robust to variations in appearances such as clothing, carrying, and head masking. Each describing attribute is given equal weight while computing the matching score, and hence the proposed model achieves high rank-k accuracy on benchmark datasets. The soft biometric traits used as a combination though, cannot achieve high rank-1 accuracy, it helps to narrow down the search to match using reliable biometric traits such as gait and face whose learning and matching time is costlier when compared to the soft biometrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Encoder decoder based image semantic space creation for clothing items retrieval 4th

Encoder decoder based image semantic space creation for clot...

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

作者： Kedia, Keshav Kumar Jain, Gaurav Kumar Grover, Vipul Samsung R&D Institute India Bangalore BangaloreKarnataka560037 India

ISBN: (纸本)9789811540141

Suggesting clothing items using query images is very convenient in online shopping, however feature extraction is not very easy for the intended query due to other objects and background variations present in these images. We need better feature extractor which extracts features only from intended clothing item in query image and rejects strong features from remaining part. To accomplish this we propose single step encoder-decoder based image translation and feature extraction approach with reverse weight transfer method for creating a common space between catalog of shopping images and query images. We present generated images and retrieved images from test set and compute the exact image retrieval accuracy. We achieved 30.6%, 46.8% and 56.3% exact image retrieval accuracy for top 1, top 3 and top 5 suggestions respectively. We also conducted a subjective survey on similar items retrieval and on an average 3.33 clothing items are relevant out of 5 items retrieved for ~500 responses which shows the effectiveness of this method. © Springer Nature Singapore Pte Ltd 2020.

关键词： Feature extraction

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image contrast enhancement using hybrid elitist ant system, Elitism-based immigrants genetic algorithm and simulated annealing 1

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2nd international conference on computer vision and image processing, cvip 2017

作者： Kumar, Rajeev Gupta, Anand Gupta, Apoorv Bansal, Aman Department of Computer Engineering NSIT University of Delhi New Delhi India Department of Information Technology NSIT University of Delhi New Delhi India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

Contrast enhancement is a technique which is used to expand the range of intensities within the image to make its features more distinct and easily perceptible to the human eye. It has found many applications ranging from medical to satellite imagery where the primary aim is to find hidden or minute details within an image. Through literary research, the authors have realised that the existing approaches lag behind in enhancing the contrast of an image. Hence in the present paper, an improved contrast enhancement technique is proposed which is based on the hybrid combination of nature-based metaheuristics: Elitist Ant System (EAS), Elitism-based Genetic Algorithm (EIGA) and Simulated Annealing (SA). EAS and EIGA work together to search globally for the optimum solution which is then refined by SA locally. Through experiment, it is observed that the proposed algorithm is efficiently improving the contrast of an image when compared with existing algorithms. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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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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Deep Dilated Convolutional Network for Single image Dehazing 7th

Deep Dilated Convolutional Network for Single Image Dehazing

引用

7th international conference on computer vision and image processing, cvip 2022

作者： Deivalakshmi, S. Sandana, J. Sudaroli Department of Electronics and Communication Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031314162

The visual quality of the images gets decreased due to bad weather conditions. The image captured under hazy weather conditions have serious attenuation in terms of color and saturation. In addition, these hazy images have very low contrast and the visual quality will be drastically poor. Moreover, object detection in hazy environment is too challenging. So, single image dehazing is a demanding, challenging and ill-posed problem. In this paper, we propose a 9-layer convolutional neural network with deep dilated filters of different dilation rates to achieve an end-to-end mapping from haze image to haze free image. Exponential expansion of receptive field is possible with the dilated filters without increasing the model complexity. Furthermore, the dilated convolutional layers help for efficient model compactness. We did experiments on synthetic dataset and on naturally obtained hazy images. The results show that our network achieves outstanding performance over the existing algorithms in terms of PSNR, SSIM and visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Convolutional neural networks

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Infrared and Visible image Fusion Using Morphological Reconstruction Filters and Refined Toggle-Contrast Edge Features

Infrared and Visible Image Fusion Using Morphological Recon...

引用

international conference on computer vision and Machine Intelligence, CVMI 2022

作者： Roy, Manali Mukhopadhyay, Susanta Dhanbad Jharkhand826004 India

ISBN: (纸本)9789811978661

In this paper, the authors have discussed a simple scheme for infrared-visible image fusion to combine the complementary information captured using multiple sensors with different properties. It employs a pair of morphological connected filters, i.e., opening and closing by reconstruction applied at each scale, brings out categorical bright and dark features from the source images. At each specific scale, the bright (or dark) features at a given spatial location from the constituting images are mutually compared in terms of clarity or prominence. Additionally, the cumulative gradient information is extracted using multiscale toggle-contrast operator which is further refined using a guided filter with respect to the source images. The final fusion is achieved by combining the best bright (or dark) features along with the refined edge information onto a base image. Experiments are conducted on pre-registered infrared-visible image pairs from the TNO IR-visible image dataset along with subjective and objective performance evaluation with due comparison against other state-of-the-art methods. The proposed method yields realistic fusion outputs and exhibits appreciable levels of performance with a better representation of complementary information which could facilitate decision-making in higher levels of image processing tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

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A Transformer-Based U-Net Architecture for Fast and Efficient image Demoireing 7th

A Transformer-Based U-Net Architecture for Fast and Efficien...

引用

7th international conference on computer vision and image processing, cvip 2022

作者： Puthussery, Densen Hrishikesh, P.S. Jiji, C.V. Founding Minds Software Trivandrum India SRM University-AP Andhra Pradesh Amaravati India

ISBN: (纸本)9783031314162

Recently, transformer based deep neural networks have been found useful in solving various image restoration tasks like image denoising, deblurring, deraining etc., producing significant improvement in PSNR and SSIM over CNN based techniques on benchmark datasets. These networks have effectively addressed quadratic computational complexity issue with increasing image resolution by making use of novel self attention strategies on local image windows. In this paper, we propose a fast and efficient UNet based architecture using transformer modules for the image demoireing task. The proposed architecture is computationally very efficient as the transformer blocks perform non-overlapping window-based self-attention instead of global self attention. We further improve upon the computational complexity by using decreasing window sizes across scales under the proposed U-Net multi resolution framework. To the best of our knowledge, ours is the first deep network architecture using transformer blocks for the image demoireing problem producing comparable results with state of the art techniques both visually and quantitatively on the CFAMoire challenge dataset [23]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Restoration

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Skin Disease Detection Using Saliency Maps and Segmentation Techniques 7th

Skin Disease Detection Using Saliency Maps and Segmentation ...

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7th international conference on computer vision and image processing, cvip 2022

作者： Toteja, Rishit Gupta, Dhruv Gautam, Vibhor Vishwakarma, Dinesh Kumar Delhi Technological University Shahbad Daulatpur Main Bawana Road Delhi110042 India

ISBN: (纸本)9783031314063

Skin diseases are the most common disease on the planet. When detecting skin diseases, dermatologists must have a high degree of expertise and precision, which is why computer-aided diagnosis is so helpful. An approach for detecting skin diseases has been presented to give more information. Several investigations have been carried out to aid in diagnosing skin disorders such as skin cancer and skin tumours. However, exact identification of skin disease is challenging to cure because of the following factors: visual likeness, little contrast in-between the lesions and skin that is between the affected and non-diseased areas, etc. In this paper we propose a model that uses an image set which is pre-processed by various techniques like deblurring, noise reduction, and then enhanced. The images are then segmented using U-Net and MobileNetV2 is used for classification. The disease is detected at the output for a corresponding input image based on the definite pattern pertaining to a dispersant in the processed image. Then, we compute a class saliency map for the provided image and class. We demonstrate how ConvNets may classify and segment skin diseases using such maps. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image segmentation

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An adaptive detection approach for autonomous forest path following using stereo vision 14

An adaptive detection approach for autonomous forest path fo...

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14th international conference on Control, Automation, Robotics and vision, ICARCV 2016

作者： Fleischmann, Patrick Kneip, Johannes Berns, Karsten Robotics Research Lab Department of Computer Science University of Kaiserslautern Kaiserslautern67663 Germany

ISBN: (纸本)9781509035496

In this paper, an image-based segmentation method to improve autonomous robot navigation in the forest is presented. The detection is supported by a filtered image generated from a stereo-based pre-processing which is a byproduct of our obstacles detection system. To cope with the large variability of forest paths, the classifier is dynamically adapted to the current situation and the segmentation relies on different image features to ensure robustness against illumination changes. Furthermore, it is summarized how the detection results are transformed to the 3D space, using a plane which is extracted from the stereo data, to be stored and maintained in a probabilistic grid map. © 2016 IEEE.

关键词： Stereo vision

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