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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281-290 订阅

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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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Enabling Text-Line Segmentation in Run-Length Encoded Handwritten Document image Using Entropy-Driven Incremental Learning 3rd

Enabling Text-Line Segmentation in Run-Length Encoded Handwr...

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

作者： Amarnath, R. Nagabhushan, P. Javed, Mohammed Univ Mysore Dept Studies Comp Sci Mysore Karnataka India Indian Inst Informat Technol Dept IT Allahabad Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

In today's digital era, archival and transmission of document images are generally carried out in a compressed form in order to avoid wastage of storage space and bandwidth. In the case of CCITT Group 3 and Group 4, the compressed representation is a stream of white and black pixel intensity values called runs, correspondingly indicating background and foreground regions of the document image. In this research paper, we propose a novel entropy-driven incremental learning technique that directly works on the compressed stream of runs, and subsequently facilitates text-line segmentation in handwritten document images using entropy and connected component analysis. Spatial Entropy Quantifier (SEQ) is extracted from the stream of runs based on a suitable window. Further, incremental entropy and connected component analysis are carried out thus separating text and non-text regions leading to automatic text-line segmentation. The proposed method is validated with the compressed dataset of handwritten document images and performance is reported.

关键词： Compressed document Compressed stream CCITT Group 3 Group 4 Entropy Incremental learning Text-line segmentation

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Identification of Fraudulent Alteration by Similar Pen Ink in Handwritten Bank Cheque 3rd

Identification of Fraudulent Alteration by Similar Pen Ink i...

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

作者： Roy, Priyanka Bag, Soumen Indian Inst Technol Indian Sch Mines Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

ISBN: (纸本)9789813292918;9789813292901

In the research field of document image analysis, especially in handwritten documents, fraudulent alteration identification is a crucial task due to several forgery activities that are happening for few decades which affect a nation economically. In this paper, we are differentiating visually identical ink of different pens used for alteration in such documents by considering this problem as a binary classification problem. Here, we have used YCbCr color model. To formulate the problem a number of statistical and texture features are extracted to create feature vectors. These feature vectors are accommodated to generate data set which are classified by multilayer perceptron technique. The method has been executed on both blue and black ink samples. On average, the proposed method produces an efficient result of accuracy of more than 93% for known and 82.30% for unknown pen data, respectively. This performance measurement shows the efficacy of the proposed method comparing with other existing methods.

关键词： Fraudulent alteration Handwritten document Multilayer perceptron Texture features YCbCr color model

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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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U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments 7th

U-RME: Underwater Refined Motion Estimation in Hazy, Clutter...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Gupta, Shilpi Mukherjee, Prerana Chaudhury, Santanu Lall, Brejesh Indian Institute of Technology Delhi India Indian Institute of Information Technology Sri CityAndhra Pradesh India Indian Institute of Technology Jodhpur India

ISBN: (纸本)9789811586965

Optical Flow is a popular method of computer vision for motion estimation. In this paper, we present a refined optical flow estimation method. Central to our approach is exploiting contour information as most of the motion lies on the edges. Further, we have formulated it as sparse to dense motion estimation. Proposed method has been evaluated on challenging real life image sequences of KITTI and Fish4Knowledge database. Results demonstrate that method performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optical flows

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Pose-Invariant Face Recognition in Surveillance Scenarios Using Extreme Learning Machine Based Domain Adaptation 3rd

Pose-Invariant Face Recognition in Surveillance Scenarios Us...

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

作者： Bhattacharjee, Avishek IIT Madras Dept CS&E Chennai Tamil Nadu India

ISBN: (纸本)9789813290884;9789813290877

Face Recognition (FR) under adversarial conditions has been a big challenge for researchers in the computer vision community. FR performance deteriorates in surveillance condition due to poor illumination, blur, noise, and pose variation in test samples (probe), when compared to training samples (gallery). Even recent deep learning methods fail to perform well in such conditions. This paper proposes a novel framework called PIFR-EDA (Pose-Invariant Face Recognition using Extreme learning machine based Domain Adaptation) that performs pose-invariant face recognition (PIFR) in cross-domain settings. It consists of two stages where the first stage performs face frontalization using a single unmodified 3D facial model and the second stage performs the task of robust domain adaptation by simultaneously learning a category transformation matrix and an l(1,1)-regularized sparse extreme learning machine classifier. The proposed method outperforms state-of-the-art shallow and deep methods (in terms of rank-1 recognition rates) when experimented on three real-world face datasets captured using surveillance cameras.

关键词： Face recognition l(1,1)-regularized sparse extreme learning machine Face frontalization Domain adaptation

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Things at Your Desk: A Portable Object Dataset 3rd

Things at Your Desk: A Portable Object Dataset

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

作者： Adak, Saptakatha IIT Madras VP Lab Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9789813290884;9789813290877

Object Recognition has been a field in computer vision research, which is far from being solved when it comes to localizing the object of interest in an unconstrained environment, captured from different viewing angles. Lack of benchmark datasets clogs the progress in this field since the last decade, barring the subset of a single dataset, alias the Office dataset, which attempted to boost research in the field of pose-invariant detection and recognition of portable object in unconstrained environment. A new challenging object dataset with 30 categories has been proposed with a vision to boost the performances of the task of object recognition for portable objects, thus enhancing the study of cross domain adaptation, in conjunction to the Office dataset. images of various hand-held objects are captured by the primary camera of a smartphone, where they are photographed under unconstrained environment with varied illumination conditions at different viewing angles. The monte-carlo object detection and recognition has been performed for the proposed dataset, facilitated by existing state-of-the-art transfer learning techniques for cross-domain recognition of objects. The baseline accuracies for existing Domain Adaptation methods, published recently, are also presented in this paper, for the kind perusal of the researchers. A new technique has also been proposed based on the activation maps of the AlexNet to detect objects, alongwith a Generative Adversarial Network (GAN) based Domain Adaptation technique for Object Recognition.

关键词： Domain adaptation Generative adversarial network (GAN) Object detection Object recognition

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A Novel Deep Learning Approach for the Removal of Speckle Noise from Optical Coherence Tomography images Using Gated Convolution-Deconvolution Structure 3rd

A Novel Deep Learning Approach for the Removal of Speckle No...

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

作者： Menon, Sandeep N. Reddy, V. B. Vineeth Yeshwanth, A. Anoop, B. N. Rajan, Jeny Natl Inst Technol Karnataka Dept Comp Sci & Engn Surathkal India

ISBN: (纸本)9789813292918;9789813292901

Optical coherence tomography (OCT) is an imaging technique widely used to image retina. Speckle noise in OCT images generally degrades the quality of the OCT images and makes the clinical diagnosis tedious. This paper proposes a new deep neural network despeckling scheme called gated convolution-deconvolution structure (GCDS). The robustness of the proposed method is evaluated on the publicly available OPTIMA challenge dataset and Duke dataset. The quantitative analysis based on PSNR shows that the results of the proposed method are superior to other state-of-the-art methods. The application of the proposed method for segmenting retinal cyst from OPTIMA challenge dataset was also studied.

关键词： OCT GCDS Speckle noise Denoising

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CluSpa: Computation Reduction in CNN Inference by exploiting Clustering and Sparsity 22

CluSpa: Computation Reduction in CNN Inference by exploiting...

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proceedings of the Second International conference on AI-ML Systems

作者： Imlijungla Longchar Amey Varhade Chetan Ingle Saurabh Baranwal Hemangee K. Kapoor CSE Indian Institute of Technology Guwahati IN

ISBN: (纸本)9781450398473

Convolutional Neural Networks (CNNs) have grown in popularity and usage tremendously over the last few years, spanning across different task such as computer vision tasks, natural language processing, video recognition, and recommender systems. Despite the algorithmic advancements that drove the growth of CNN still has considerable computational and memory overhead that poses challenges in achieving real-time performance. Each input image requires millions to even billions of elementary arithmetic operations before the network obtains the result. In CNNs, convolutional and pooling layers are followed by activation layers involving various activation functions. Hence, a lot of work has been done to reduce these costs in the last few years. Numerous optimizations have addressed at both hardware and software levels. In this paper, we propose a software-based solution for improving the performance of inference of networks. We suggest a technique for the approximate computation of the convolution operation based on clustering and sharing of weights. We have utilized Gaussian Mixture Models for clustering. We exploit weight sparsity to further reduce computations on top of the clustering method. We were able to achieve a considerable reduction in the MAC operations and the overall computation speedup on popular CNN architectures

关键词： Weight sharing CNNs Quantization Compression Deep Neural Networks DNNs Convolutional Neural Networks Performance Approximate Computing Sparsity Clustering Computation Reduction Inference

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Investigation on the Muzzle of a Pig as a Biometric for Breed Identification 3rd

Investigation on the Muzzle of a Pig as a Biometric for Bree...

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

作者： Chakraborty, Shoubhik Karthik, Kannan Banik, Santanu Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India Anim Genet & Breeding ICAR Natl Res Ctr Pig Gauhati 781015 Assam India

ISBN: (纸本)9789813290884;9789813290877

Visual features associated with cattle and pigs cannot just be used for individual biometric identification, they can also be used to organize the domain into distinct breeds. In this paper, we use cropped muzzle images of pigs for breed identification. Gradient patch density maps are first created, and then the patch density profile distribution tailored to a specific breed is learnt to characterize the feature space for each of the four pig breeds: Duroc, Ghungroo, Hampshire, and Yorkshire. A Maximal Likelihood (ML) inferencing at the patch level followed by a second-tier decision fusion based on majority vote has been used to detect the breed from any query feature computed from an arbitrary muzzle image. Duroc, Ghungroo, and Yorkshire show good classification accuracies of 75.86%, 70.59%, and 100%, respectively, while the accuracy drops for Hampshire to 58.78% on account of the intrinsic white patch diversity in the breed and its similarity to Duroc and Ghungroo on some counts.

关键词： Gradient significance map Pig Breed identification Biometric Maximal likelihood Conditional density function Decision fusion Majority vote

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