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

共 1410 条记录，以下是121-130 订阅

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Robustly Clipped Sub-equalized Histogram-Based Cosine-Transformed Energy-Redistributed Gamma Correction for Satellite image Enhancement 3rd

Robustly Clipped Sub-equalized Histogram-Based Cosine-Transf...

引用

3rd international conference on computer vision and image processing (cvip)

作者： Singh, Himanshu Kumar, Anil Balyan, L. K. Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9789813292918;9789813292901

In this paper, a new proposal is reported for image quality enhancement. Here, statistically clipped, bi-histogram equalization-based adaptive gamma correction along with its cosine-transformed energy redistribution is introduced for improvement of low-contrast dark images. This approach computes the clipping limit adaptively by observing stretched histogram bins, mean, and median values for each sub-histogram. Considering the clipping limit as the lowest of these three values, this limit ensures the conservation of information content of the image to a great extent. This adaptive clipping limit selection also resolves the issue of over-emphasization of high-frequency bins during sub-histogram equalization. For harvesting more information and better illumination, gamma correction is imparted by using the adaptive gamma value-set. The corresponding gamma value-set is itself derived from the previously sub-equalized interim intensity channel. In addition to this, two-dimensional (2D) discrete cosine transformation (DCT) for gamma-corrected channel is also employed for incorporating the energy-based textural enhancement framework. Validation is performed here by analyzing the enhancement for various remotely sensed dark satellite images by evaluating standard performance indices.

关键词： Adaptive gamma correction Bi-histogram equalization image quality enhancement Optimal histogram clipping Remotely sensed images

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Swift Convergence: Federated Learning Enhanced with GMMs for image Classification 1

引用

8th international conference on computer vision and image processing (cvip)

作者： Palit, Aditi Nandanavanam, Sai Pragna Yeturu, Kalidas Indian Inst Technol Tirupati Dept Comp Sci & Engn Tirupati Andhra Pradesh India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

In Federated learning (FL), FederatedAveraging(FedAvg) is widely used to compute the weighted mean of local models in the parametric space over time on a central server by exchanging intermediate updates over multiple rounds of communication. However, this approach requires many communication rounds for the central model to learn data generalization. Each local model updates the central model parameters in different directions of the multi-dimensional parameter space. To address this challenge, we propose FedGMMinit: Federated Initialization with Gaussian Mixture Model, which adjusts initial central model gradients by pre-training the model on synthetic data generated from a Gaussian Mixture Model (GMM). For each label in the client's dataset, a GMM is built. The pre-trained weights are then communicated to the selected clients to initialize FedAvg. To maintain data privacy, only the client's representation of the Gaussian is passed to the server. Our proposed approach is tested on MNIST digit datasets for image classification. It shows a reduction of 10-15 communication rounds required by the central model to achieve target accuracy for both IID and non-IID distributions. In the scope of the study, we also discovered that clustering clients and training them with global models also contributed to the overall improvement of convergence. We call this clustering method as FedGMMCluster.

关键词： Communication rounds Gaussian mixture model Initialization Proxy dataset Clustering

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MotionFormer: An Improved Transformer-Based Architecture for Multi-object Tracking 1

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8th international conference on computer vision and image processing (cvip)

作者： Agrawal, Harshit Halder, Agrya Chattopadhyay, Pratik Indian Inst Technol BHU Varanasi Uttar Pradesh India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Multi-object tracking (MOT) is a crucial task in computer vision with numerous real-world applications. In recent years, Transformer-based models have shown promising results in MOT. However, existing methods still face challenges in scenarios involving short-term object occlusion, camera motion, and ambiguous detection especially in low frame-rate videos. In this work, we propose a novel variant of the TrackFormer model that addresses these limitations by integrating an online motion prediction module based on the Kalman Filter that incorporates the important temporal information present in the videos. The addition of the Kalman Filter helps the model study the tracked pedestrians' motion patterns and leverage them for effective association among targets across the video frames even in the case of short-term occlusions without adding much to the computational complexity of the overall framework. The proposed model is termed MotionFormer due to its inherent ability to utilize long-term motion information. Through extensive evaluations using popular tracking datasets, namely MOT-17 and MOT-20, we demonstrate the effectiveness of our approach over existing approaches. Results show that the MotionFormer provides reasonably good tracking accuracy with a much lesser identity switching rate as compared to the other models. Further, it significantly outperforms the base TrackFormer model in terms of tracking accuracy, F1-score, as well as identity switching rate for MOT-17 private and public and MOT-20 private data.

关键词： Multi Object Tracking computer vision Motion Prediction MotionFormer Kalman Filter

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Low Cost Embedded vision System for Location and Tracking of a Color Object 6th

Low Cost Embedded Vision System for Location and Tracking ...

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6th international conference on computer vision and image processing, cvip 2021

作者： Ayala, Diego Chavez, Danilo Robles, Leopoldo Altamirano Puebla Mexico Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9783031113451

This paper describes the development of an embedded vision system for detection, location, and tracking of a color object;it makes use of a single 32-bit microprocessor to acquire image data, process, and perform actions according to the interpreted data. The system is intended for applications that need to make use of artificial vision for detection, location and tracking of a color object and its objective is to have achieve at reduced terms of size, power consumption, and cost. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Color

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On the Choice of Coil Combination Weights for Phase-Sensitive GRAPPA Reconstruction in Multichannel SWI 3rd

On the Choice of Coil Combination Weights for Phase-Sensitiv...

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3rd international conference on computer vision and image processing (cvip)

作者： Madhusoodhanan, Sreekanth Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Med Image Comp & Signal Proc Grp Trivandrum Kerala India

ISBN: (纸本)9789813290884;9789813290877

The feasibility of applying Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) techniques has been established, with a two-fold or more reduction in scan time without compromising vascular contrast in Susceptibility Weighted Imaging (SWI) by choosing an optimal sensitivity map for combining the coil images. The overall SNR performance in GRAPPA is also dependent on the weights used for combining the GRAPPA reconstructed coil images. In this article, different methods for estimating the optimal coil combination weights are qualitatively and quantitatively analysed for maximizing the structural information in the tissue phase. The performance of various methods is visually evaluated using minimum Intensity Projection (mIP), Among the three methods, sensitivity estimated using the dominant eigenvector mentioned as ESPIRiT-based sensitivity in this article shows superior performance over the other two methods including estimating the sensitivity from the centre k-space line and from reconstructed channel images. Combining channel images using ESPIRiT sensitivity shows its ability to preserve the local phase variation and reduction in noise amplification.

关键词： Coil combination Parallel imaging Sensitivity SWI

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A robust scheme for extraction of text lines from handwritten documents

A robust scheme for extraction of text lines from handwritte...

引用

international conference on computer vision and image processing, cvip 2016

作者： Biswas, Barun Bhattacharya, Ujjwal Chaudhuri, Bidyut B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata108 India

ISBN: (纸本)9789811021060

Considering the vast collection of handwritten documents in various archives, research studies for their automatic processing have major impact in the society. Line segmentation from images of such documents is a crucial step. The problem is more difficult for documents of major Indian scripts such as Bangla because a large number of its characters have either ascender or descender or both and the majority of its writers are accustomed in extremely cursive handwriting. In this article, we describe a novel strip based text line segmentation method for handwritten documents of ***, the proposed method has been found to perform efficiently on English and Devanagari handwritten *** conducted extensive experimentations and its results show the robustness of the proposed approach on multiple scripts. © Springer Science+Business Media Singapore 2017.

关键词： image segmentation

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Improving image Quality and Convergence Rate of Perona-Malik Diffusion Based Compressed Sensing MR image Reconstruction by Gradient Correction 3rd

Improving Image Quality and Convergence Rate of Perona-Malik...

引用

3rd international conference on computer vision and image processing (cvip)

作者： Joy, Ajin Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Med Image Comp & Signal Proc Lab Trivandrum Kerala India

ISBN: (纸本)9789813290884;9789813290877

A memory-based reconstruction algorithm is developed for optimizing image quality and convergence rate of Compressed Sensing-Magnetic Resonance image (CS-MRI) reconstruction using Perona-Malik (PM) diffusion. The PM diffusion works by estimating the underlying structure of an image and diffusing the image in a nonlinear fashion to preserve the sharpness of edge information. The edges due to undersampling artifacts are generally characterized as weak edges (false edges) identified by the comparatively smaller gradient magnitude associated with it. The convergence rate for CS-MRI reconstruction based on PM diffusion, therefore, depends on the extent by which the gradients attributed to false edges are diffused off per iteration. However, if the undersampling interference in high, gradient magnitudes of false edges can become comparable to that of true edges (boundaries of actual anatomical features). This might either lead to preservation of false edges or diffusion of true edges. This reduces the quality of reconstructed images. In such scenarios, we assume that the gradient information in past iterations contains useful structural information of the image which is lost while diffusing the image. Hence, we propose to overcome this problem by correcting the estimate of the underlying structure of the image using a combination of gradient information from a number of past iterations. This reduces the diffusion of weak edges by restoring the otherwise lost weak structural information at every iteration.

关键词： Perona-Malik diffusion Compressed sensing Gradient correction Memory Nonlinear diffusion

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DCT-SwinGAN: Leveraging DCT and Swin Transformer for Face Synthesis from Sketch and Thermal Domains 8th

DCT-SwinGAN: Leveraging DCT and Swin Transformer for Face Sy...

引用

8th international conference on computer vision and image processing (cvip)

作者： Kotadiya, Haresh Kumar Singh, Satish Kumar Dubey, Shiv Ram Yadav, Nand Kumar Indian Inst Informat Technol Comp Vis & Biometr Lab CVBL Allahabad Prayagraj India

ISBN: (纸本)9783031581731;9783031581748

Face generation remains a crucial task owing to its applications in crime investigation, entertainment, etc. Sketch to face synthesis is an important task accomplished using Generative Adversarial Network (GAN) models. The generator of the GAN is usually designed using Convolutional Neural Networks (CNNs). GANs are able to deliver promising outcomes in some cases but fail in others. Owing to the local nature of CNN, it is unable to keep track of long range dependencies, limiting the model to local information and delivering underwhelming results. However, recently developed Transformers encode the global context having long range dependencies. Swin Transformer is designed to be suitable for images with promising performance on several vision tasks. We utilize the Swin Transformer along with Discrete Cosine Transform (DCT) in the GAN framework and propose DCT-SwinGAN for face synthesis from sketch and thermal domains. DCT-SwinGAN comprises a multi scale discriminator paired with a generator comprising an attention module, DCT ResNet Convolution, Deconv decoder, and Swin Transformer. The proposed model captures not only local but also global information to generate realistic face images. The generated model outperforms the existing state-of-the-art models on CUHK sketch-to-face and the WHU-IIP thermal-to-face datasets.

关键词： Face Synthesis Sketch Thermal GAN DCT Swin Transformer Multi Scale Discriminator image Translation

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Segmentation and Labeling of Vertebra Using SegFormer Architecture 8th

Segmentation and Labeling of Vertebra Using SegFormer Archit...

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8th international conference on computer vision and image processing (cvip)

作者： Ghosh, Archan Ghosh, Debgandhar Ghoshal, Somoballi Chakrabarti, Amlan Sur-Kolay, Susmita Univ Calcutta Kolkata India Maulana Abul Kalam Azad Univ Technol Kolkata India Indian Stat Inst Kolkata India

ISBN: (纸本)9783031581731;9783031581748

Vertebra segmentation and labeling in MR images of the spine play a vital role in the identification of diseases or anomalies. MRI captures the tissue structure of a spine accurately, hence it is essential to demarcate and identify the vertebra in the MRI image. There are both supervised and unsupervised methods for vertebra segmentation and labeling. However, the acquisition of requisite data is a challenge to designing methods with very high accuracy. In this work, we have modified a transformer-based architecture called Segformer for semantic segmentation of 3D sliced data. Our method leverages transfer learning on low-population data. With a new advanced masking logic, we achieve 99% accuracy for segmentation and labeling of lumbar spine MR images.

关键词： Semantic segmentation of image Vertebra labeling Transfer learning Segformer architecture

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computer vision and image processing 1

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丛书名： Communications in computer and Information Science

1000年

作者： Satish Kumar Singh P. Nagabhushan Partha Roy Balasubramanian Raman

ISBN: (数字)9789811610868

ISBN: (纸本)9789811610851

This three-volume set (CCIS 1376-1378) constitutes the refereed proceedings of the 5th international conference on computer vision and image processing, cvip 2020, held in Prayagraj, India, in December 2020. Due to the COVID-19 pandemic the conference was partially held online.;The 134 papers papers were carefully reviewed and selected from 352 submissions. The papers present recent research on such topics as biometrics, forensics, content protection, image enhancement/super-resolution/restoration, motion and tracking, image or video retrieval, image, image/video processing for autonomous vehicles, video scene understanding, human-computer interaction, document image analysis, face, iris, emotion, sign language and gesture recognition, 3D image/video processing, action and event detection/recognition, medical image and video analysis, vision-based human GAIT analysis, remote sensing, and more.

关键词： computer Imaging, vision, Pattern Recognition and Graphics Artificial Intelligence computer Applications Systems and Data Security computers and Education

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