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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1642 条记录，以下是51-60 订阅

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Zernike Moment and Mutual Information Based Methods for Multimodal image Registration 3rd

Zernike Moment and Mutual Information Based Methods for Mult...

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

作者： Kashyap, Suraj Kumar Jat, Dinesh Bhuyan, M. K. Vishwakarma, Amit Gadde, Prathik Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India Indiana Univ Purdue Univ Sch Informat & Comp 535 West Michigan St Indianapolis IN 46202 USA

ISBN: (纸本)9789813292918;9789813292901

image registration enables joint operations between images obtained from diverse sources. However, there have been limited advances in the registration of multichannel images. The accuracy of registration is a significant concern for medical applications, among others. Two methods, PCA-ZM and CED-ZM, have been proposed for registration based on Zernike moment and enhanced mutual information. Edge detection by Zernike moment and identification of common features in multichannel images are used as a foundation to improve accuracy over single-channel registrations. Single-channel registration accuracy for MRI and SPECT brain images is found to surpass the methods compared against. PCA-ZM demonstrates good accuracy for MR-MR registration, while CED-ZM has good accuracy for MR-SPECT registration. These measures improve upon accurate registration for images, especially where many modalities are available, such as in medical diagnosis.

关键词： Multimodal Multichannel MRI SPECT Medical image registration Zernike moment Enhanced mutual information Principal component analysis Canny edge detector Gradient information Edge overlap

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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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Multi-scale image Fusion Scheme Based on Gray-Level Edge Maps and Adaptive Weight Maps 3rd

Multi-scale Image Fusion Scheme Based on Gray-Level Edge Map...

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

作者： Pradhan, Jitesh Raj, Ankesh Pal, Arup Kumar Banka, Haider Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9789813292918;9789813292901

Digital cameras and other digital devices cannot focus on all significant objects within the single frame due to their limited depth of focus. Consequently, few objects get attention in the captured image while the rest of the objects becomes background information. This problem can be overcome using different multi-focus image fusion techniques because it combines all the partially focused objects of different parent images into a single fully focused fused image. Hence, the final fused image focuses on each and every object of the parent images. In this paper, a novel multi-focus image fusion technique has been proposed which uses different edge-finding operators (like Sobel, Prewitt, Roberts, and Scharr) on preprocessed images. These different edge-finding operators have great pixel discrimination property which helps us to locate all the vital textural information of different partially focused images. Subsequently, an adaptive weight calculation approach has been introduced to generate weight maps of different parent images. Finally, all these parent image weight maps have been deployed into the winner-take-all scheme to integrate all parent images into a single fused image. Further, we have also considered different sets of partially focused images for experimental analysis. The experimental outcomes reveal that the proposed scheme is outperforming as compared to recent state of the arts.

关键词： Adaptive weight maps Depth of focus Edge maps image fusion Multi-focus

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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...

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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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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...

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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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Recent Advancements in image-Based Prediction Models for Diagnosis of Plant Diseases 3rd

Recent Advancements in Image-Based Prediction Models for Dia...

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

作者： Verma, Shradha Chug, Anuradha Singh, Amit Prakash GGSIP Univ USICT Delhi India

ISBN: (纸本)9789813290884;9789813290877

India plays a significant role in the world as a major contributor to the overall food industry. Farming being a major occupation, crop protection from plant diseases has become a serious concern. The occurrence of plant diseases is hugely dependent on environmental factors which are uncontrollable. Ongoing agricultural research and the advanced computational technologies can be coalesced to determine an effective solution, which can improve the yield and result in better harvests. The objective is to minimize the economic and production losses. Researchers have developed several modeling techniques, viz. Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Deep Learning for prediction and preferably early detection of diseases in plants. Mostly, images of the diseased plant are given as input to these prediction models. Early detection leads to minimizing the pesticide usage, hence, resulting in lowering the expense along with the ecological impact. Also, it is essential that the prediction techniques are nondestructive in nature. This paper delves into the Machine Learning based plant disease prediction models, along with a brief study of diverse imaging techniques, viz., RGB, multi- and hyperspectral, thermal, fluorescence spectroscopy, etc., catering to different features/parameters of a plant, useful in the prediction of diseases. Around 35 research papers from reputed peer-reviewed journals were studied and analyzed for this systematic review. It highlights the latest trends in plant disease diagnosis as well as identifies the future scope with the application of modern era technologies.

关键词： Imaging Machine Learning Plant diseases Neural Networks Deep Learning Agriculture

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Analysis of Depression Based on Facial Cues on A Captured Motion Picture 3

Analysis of Depression Based on Facial Cues on A Captured Mo...

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IEEE 3rd international conference on Signal and image processing (ICSIP)

作者： Dadiz, Bryan G. Marcos, Nelson De La Salle Univ Coll Comp Studies Technol Inst Philippines Manila Philippines De La Salle Univ Coll Comp Studies Manila Philippines

ISBN: (纸本)9781538663967

Depression is one of the causes of suicide in the world next to other health issues that makes up an alarming point of mortality living in this lifetime. Melancholy that in the field of computer vision and signal processing has been tackled in various ways. Thus, this paper presents the classification model of detecting depression based on local binary pattern (LBP) texture features an image processing approach for pattern recognition on images. The study used the video recording from the SEMAINE database. The face image is cropped from a video and extracting Uniformed LBP features in every single frame. Part of the classification is to implement PCA eigenvalues from the original features to see the effects. The result of the accuracy was 81% of the SVM using RBF kernel classifier when detecting Depressed to Not Depressed Behavior on a captured motion picture.

关键词： computer vision Local Binary Pattern Facial Features

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Activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques 3rd

Activity Recognition for Indoor Fall Detection in 360-Degree...

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

作者： Dhiraj Manekar, Raunak Saurav, Sumeet Maiti, Somsukla Singh, Sanjay Chaudhury, Santanu Neeraj Kumar, Ravi Chaudhary, Kamal Cent Elect Engn Res Inst CSIR Pilani Rajasthan India Samsung Res India New Delhi India

ISBN: (纸本)9789813292918;9789813292901

Human activity recognition (HAR) targets the methodologies to recognize the different actions from a sequence of observations. vision-based activity recognition is among the most popular unobtrusive technique for activity recognition. Caring for the elderly who are living alone from a remote location is one of the biggest challenges of modern human society and is an area of active research. The usage of smart homes with an increasing number of cameras in our daily environment provides the platform to use that technology for activity recognition also. The omnidirectional cameras can be utilized for fall detection activity which minimizes the requirement of multiple cameras for fall detection in an indoor living scenario. Consequently, two vision-based solutions have been proposed: one using convolutional neural networks in 3D-mode and another using a hybrid approach by combining convolutional neural networks and long short-term memory networks using 360-degree videos for human fall detection. An omnidirectional video dataset has been generated by recording a set of activities performed by different people as no such 360-degree video dataset is available in the public domain for human activity recognition. Both, the models provide fall detection accuracy of more than 90% for omnidirectional videos and can be used for developing a fall detection system for indoor health care.

关键词： Activity recognition Omnidirectional video 3D convolutional networks Long short-term memory networks Fall activity Daily activity

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Object Labeling in 3D from Multi-view Scenes Using Gaussian-Hermite Moment-Based Depth Map 3rd

Object Labeling in 3D from Multi-view Scenes Using Gaussian-...

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

作者： Enan, Sadman Sakib Rahman, S. M. Mahbubur Haque, Samiul Howlader, Tamanna Hatzinakos, Dimitrios BUET Dept EEE Dhaka 1205 Bangladesh North Carolina State Univ Dept ECE Raleigh NC 27606 USA Univ Dhaka ISRT Dhaka 1000 Bangladesh Univ Toronto Dept ECE Toronto ON M5S 2E4 Canada

ISBN: (纸本)9789813292918;9789813292901

Depth as well as intensity of a pixel plays a significant role in labeling objects in 3D environments. This paper presents a novel approach of labeling objects from multi-view video sequences by incorporating rich depth information. The depth map of a scene is estimated from focus-cues using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. It is expected that the depth map obtained from GHMs provides robust features as compared to that provided by other popular depth maps such as those obtained from Kinect and defocus cue. We use the rich depth and intensity values of a pixel to score every point of a video frame for generating labeled probability maps in a 3D environment. These maps are then used to create a 3D scene wherein available objects are labeled distinctively. Experimental results reveal that our proposed approach yields excellent performance of object labeling for different multi-view scenes taken from RGB-D object dataset, in particular showing significant improvements in precision-recall characteristics and F1-score.

关键词： Depth map Object labeling 3D scene

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image-Based Facial Expression Recognition Using Local Neighborhood Difference Binary Pattern 3rd

Image-Based Facial Expression Recognition Using Local Neighb...

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

作者： Saurav, Sumeet Singh, Sanjay Yadav, Madhulika Saini, Ravi Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vanasthali Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Automatic facial expression recognition (FER) has gained enormous interest among the computer vision researchers in recent years because of its potential deployment in many industrial, consumer, automobile, and societal applications. There are a number of techniques available in the literature for FER;among them, many appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP) have been shown to be very efficient and accurate. In this paper, we propose a new descriptor called local neighborhood difference binary pattern (LNDBP). This new descriptor is motivated by the recent success of local neighborhood difference pattern (LNDP) which has been proven to be very effective in image retrieval. The basic characteristic of LNDP as compared with the traditional LBP is that it generates binary patterns based on a mutual relationship of all neighboring pixels. Therefore, in order to use the benefit of both LNDP and LBP, we have proposed LNDBP descriptor. Moreover, since the extracted LNDBP features are of higher dimension, therefore a dimensionality reduction technique has been used to reduce the dimension of the LNDBP features. The reduced features are then classified using the kernel extreme learning machine (K-ELM) classifier. In order to, validate the performance of the proposed method, experiments have been conducted on two different FER datasets. The performance has been observed using well-known evaluation measures, such as accuracy, precision, recall, and F1-score. The proposed method has been compared with some of the state-of-the-art works available in the literature and found to be very effective and accurate.

关键词： Facial expression recognition (FER) Local neighborhood difference pattern (LNDP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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