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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1497 条记录，以下是251-260 订阅

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Two-View Triangulation: A Novel Approach Using Sampson's Distance 3rd

Two-View Triangulation: A Novel Approach Using Sampson's Dis...

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3rd International conference on computer vision and image processing (CVIP)

作者： Verma, Gaurav Poddar, Shashi Kumar, Vipan Das, Amitava CSIR Cent Sci Instruments Org CSIO Chandigarh 160030 India Acad Sci Innovat & Res AcSIR CSIR CSIO Campus Chandigarh 160030 India

ISBN: (纸本)9789813290884;9789813290877

With the increase in the need for video-based navigation, the estimation of 3D coordinates of a point in space, using images, is one of the most challenging tasks in the field of computer vision. In this work, we propose a novel approach to formulate the triangulation problem using Sampson's distance, and have shown that the approach theoretically converges toward an existing state-of-the-art algorithm. The theoretical formulation required for achieving optimal solution is presented along with its comparison with the existing algorithm. Based on the presented solution, it has been shown that the proposed approach converges closely to Kanatani-Sugaya-Niitsuma algorithm. The purpose of this research is to open a new frontier to view the problem in a novel way and further work on this approach may lead to some new findings to the triangulation problem.

关键词： Triangulation Stereovision Monocular vision Fundamental matrix Epipolar geometry

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Discriminative BULBPH Descriptor with KDA for Palmprint Recognition 3rd

Discriminative BULBPH Descriptor with KDA for Palmprint Reco...

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3rd International conference on computer vision and image processing (CVIP)

作者： Tamrakar, Deepti Khanna, Pritee Jabalpur Engn Coll Jabalpur India PDPM Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9789813290884;9789813290877

This work proposes Block-wise uniform local binary pattern histogram (BULBPH) followed by kernel discrimination analysis (KDA) as descriptor for palmprint recognition. BULBPH provides distribution of uniform patterns (such as line and wrinkles) in local region and can be better used as palmprint features. KDA is applied on BULBPH to reduce dimension and enhance discriminative capability using chi-RBF kernel. The experiments are conducted on four palmprint databases and performance is compared with related descriptors. It is observed that KDA on BULBPH descriptor achieves more than 99% accuracy with 4.04 decidability index on four palmprint databases.

关键词： Palmprint Blockwise uniform local binary pattern Kernel discriminant analysis

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Classification of Abandoned and Unattended Objects, Identification of Their Owner with Threat Assessment for Visual Surveillance 3rd

Classification of Abandoned and Unattended Objects, Identifi...

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3rd International conference on computer vision and image processing (CVIP)

作者： Agarwal, Harsh Singh, Gursimar Siddiqui, Mohammed Arshad PDPM Indian Inst Informat Technol Design & Mfg Ja Jabalpur India

ISBN: (纸本)9789813290884;9789813290877

Terrorism is on an ever-increasing rise and is one of the major threats the world is facing today. Terrorist attacks mostly take place in crowded areas such as railway stations and airports. They involve the use of explosives which are placed inside suspicious abandoned objects like bags, suitcases, etc. In this paper, we are proposing a model that can classify abandoned and unattended objects separately and backtrack to identify the owner as well as find the last known location of the owner in a social environment using visual surveillance feed in real time for rapid alert and action.

关键词： Video surveillance Human detection and tracking Background modeling Foreground analysis Scene perception

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image Embedding for Detecting Irregularity 3rd

Image Embedding for Detecting Irregularity

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3rd International conference on computer vision and image processing (CVIP)

作者： Sharma, M. K. Sheet, D. Biswas, Prabir Kumar IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Elect Engn Kharagpur W Bengal India IIT Kharagpur Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (纸本)9789813292918;9789813292901

Detecting irregularity in an image or video is an important task in quality control or automatic visual inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in images. This can further be utilized in image screening application. In the proposed architecture, deep adversarial autoencoder is trained to extract the features from images. Using these features and skip-gram model, we develop the image2vec architecture to capture contextual probability in an image. Various score aggregation techniques are explored and its performance is reported. As a case study, we present a scenario of foreign body object detection in clinical-grade X-ray images. The proposed approach is found to correctly detect and localize abnormality in images.

关键词： Visual inspection image embedding Contextual abnormality detection Adversarial autoencoder Skip-gram Negative sampling

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Robust Single image Super Resolution Employing ADMM with Plug-and-Play Prior 3rd

Robust Single Image Super Resolution Employing ADMM with Plu...

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3rd International conference on computer vision and image processing (CVIP)

作者： Rahiman, V. Abdu George, Sudhish N. Govt Coll Engn Kannur Kannur India Natl Inst Technol Calicut Kozhikode India

ISBN: (纸本)9789813292918;9789813292901

image super resolution is a signal processing technique to post-process a captured image to retrieve its high-resolution version. Majority of the conventional super resolution methods fail to perform in presence of noise. In this paper, a noise robust reconstruction based single image super resolution (SISR) algorithm is proposed, using alternating directionmethod of multipliers (ADMM) and plug-and-play modeling. The plug-and-play prior concept is incorporated to the two variable update steps in ADMM. Therefore, a fast SISR model and a denoiser are used in ADMM to implement the proposed robust SISR scheme. The experimental results show that the noise performance of the proposed approach is better than the conventional methods. The impact of parameter selection on the performance of the algorithm is experimentally analyzed and the results are presented.

关键词： Single image super resolution Plug-and-play prior Noise robust

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S3DMT-Net: Improving soft sharing based multi-task CNN using task-specific distillation and cross-task interactions 21

S3DMT-Net: Improving soft sharing based multi-task CNN using...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Jha, Ankit Banerjee, Biplab Chaudhuri, Subhasis Indian Institute of Technology Bombay India

ISBN: (纸本)9781450391276

We deal with the problem of multi-task learning (MTL) in the context of performing multiple related visual dense prediction tasks from single image inputs. The soft-sharing-based deep MTL Convnets (CNN) have separate networks for each of the tasks with additional constraints on the model parameters. Although such MTL models have shown convincing performances for tasks including semantic segmentation, depth estimation, and surface normal estimation from monocular images, they have two inherent bottlenecks: i) the constraints imposed on such models do not in general leverage the inter-task information which otherwise can boost the joint training, and ii) the performances of the individual task-specific networks are not explicitly optimized. We hypothesize that the MTL performance can be enhanced comprehensively if the aforesaid issues are taken into account in soft-sharing-based MTL models. To this end, we introduce a novel MTL architecture in this paper called S3 DMT-Net, where i) the task-specific networks are trained under the notion of self-distillation, which aims at inheriting the features from the deeper layers into the shallower layers, thus enriching the capacity of the network, and ii) the idea of cross-task interactions are utilized where the feature-maps of the task-specific encoders are shared amongst each other. We validate the model performance on two scenes: indoor (NYUv2 and Mini Taskonomy) and urban (CityScapes and ISPRS), where substantial improvements are recorded for all the tasks over the existing literature. © 2021 ACM.

关键词： Learning systems

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Copy-Move image Forgery Detection Using Gray-Tones with Texture Description 3rd

Copy-Move Image Forgery Detection Using Gray-Tones with Text...

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3rd International conference on computer vision and image processing (CVIP)

作者： Dixit, Anuja Bag, Soumen Indian Sch Mines Dept Comp Sci & Engn Indian Inst Technol Dhanbad Bihar India

ISBN: (纸本)9789813292918;9789813292901

Copy-move forgery is a well-known image forgery technique. In this image manipulation method, a certain area of the image is replicated and affixed over the same image on different locations. Most of the times replicated segments suffer from multiple post-processing and geometrical attacks to hide sign of tampering. We have used block-based method for forgery detection. In block-based proficiencies, image is parted into partially overlapping blocks. Features are extracted corresponding to blocks. In the proposed scheme, we have computed Gray-Level Co-occurrence Matrix (GLCM) for blocks. Singular Value Decomposition (SVD) is applied over GLCM to find singular values. We have calculated Local Binary Pattern (LBP) for all blocks. The singular values and LBP features combinedly construct feature vector corresponding to blocks. These feature vectors are sorted lexicographically. Further, similar blocks discovered to identify replicated section of image. To ensure endurance of the proposed methods, Detection Accuracy (DA), False Positive Rate (FPR), and F-Measure are calculated and compared with existing methods. Experimental results establish the validity of proposed scheme for precise detection, even when meddled region of image sustain distortion due to brightness change, blurring, color reduction, and contrast adjustment.

关键词： Copy-move image forgery Feature extraction image forensics Local binary pattern Singular value decomposition

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Mushroom Classification Using Feature-Based Machine Learning Approach 3rd

Mushroom Classification Using Feature-Based Machine Learning...

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3rd International conference on computer vision and image processing (CVIP)

作者： Maurya, Pranjal Singh, Nagendra Pratap MMM Univ Technol Dept CSE Gorakhpur 273010 Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

Mushroom is an important fungus which contains a good source of vitamin B and a large amount of protein when compared to all other vegetables. It helps to prevent cancer, useful in weight loss and increases the immunity power of human. On the other hand, some mushrooms are toxic and can prove dangerous if we eat them. Therefore, it is a prominent task to differentiate, the edible and poisonous mushrooms. This paper focuses on developing a method for classification of mushroom using its texture feature, which is based on the machine learning approach. The performance of the proposed approach is 76.6% by using SVM classifier, which is found better with respect to the other classifiers like KNN, Logistic Regression, Linear Discriminant, Decision Tree, and Ensemble classifiers.

关键词： Decision trees

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A Bag of Constrained Visual Words Model for image Representation 3rd

A Bag of Constrained Visual Words Model for Image Representa...

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3rd International conference on computer vision and image processing (CVIP)

作者： Mukherjee, Anindita Sil, Jaya Chowdhury, Ananda S. Dream Inst Technol Kolkata India IIEST Sibpur Howrah India Jadavpur Univ Kolkata 700032 India

ISBN: (纸本)9789813292918;9789813292901

We propose a bag of constrained visual words model for image representation. Each image under this model is considered to be an aggregation of patches. SURF features are used to describe each patch. Two sets of constraints, namely, the must-link and the cannot-link, are developed for each patch in a completely unsupervised manner. The constraints are formulated using the distance information among different patches as well as statistical analysis of the entire patch data. All the patches from the image set under consideration are then quantized using the Linear-time-Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained k-means algorithm. The resulting clusters, which we term as constrained visual words, are then used to label the patches in the images. In this way, we model an image as a bag (histogram) of constrained visual words and then show its utility for image retrieval. Clustering as well as initial retrieval results on COIL-100 dataset indicate the merit of our approach.

关键词： image representation Constrained visual words LCVQE image retrieval

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Diagnosis of Prostate Cancer with Support Vector Machine Using Multiwavelength Photoacoustic images 3rd

Diagnosis of Prostate Cancer with Support Vector Machine Usi...

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3rd International conference on computer vision and image processing (CVIP)

作者： Borkar, Aniket Sinha, Saugata Dhengre, Nikhil Chinni, Bhargava Dogra, Vikram Rao, Navalgund Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur Maharashtra India Univ Rochester Med Ctr Dept Imaging Sci Rochester NY 14642 USA

ISBN: (纸本)9789813290884;9789813290877

Photoacoustic (PA) imaging is an emerging soft tissue imaging modality which can be potentially used for the detection of prostate cancer. computer-aided diagnosis tools help in further enhancing the detection process by assisting the physiologist in the interpretation of medical data. In this study, we aim to classify the malignant and nonmalignant prostate tissue using a support vector machine algorithm applied to the multiwavelength PA data obtained from human patients. The performance comparison between two feature sets, one consisting of multiwavelength PA image pixel values and the other consisting of chromophore concentration values are reported. While chromophore concentration values detected malignant prostate cancer more efficiently, the PA image pixels detected the nonmalignant prostate specimens with higher accuracy. This study shows that multiwavelength PA image data can be efficiently used with the support vector machine algorithm for prostate cancer detection.

关键词： Photoacoustic imaging Prostate cancer Support vector machine

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