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

共 1468 条记录，以下是741-750 订阅

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Stamp and Logo Detection from Document images by Finding Outliers 5

Stamp and Logo Detection from Document Images by Finding Out...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Dey, Soumyadeep Mukherjee, Jayanta Sural, Shamik Indian Inst Technol Kharagpur Sch Informat Technol Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

Stamps and logos are generally used for authenticating the source of a document. For automatic document processing, identification and segmentation of stamps and logos are essential. In the past, methods to detect stamps and logos were limited to specific shapes, colors, or training data. However, stamps and logos can be of any shape or color. In this paper, we have proposed a novel stamp and logo detection technique. Our approach is based on the fact that stamps and logos, in general, are not the primary contents of a document. This fact motivates us to propose an outlier detection technique for the same purpose in a feature space. Based on some geometric features, the detected outliers are classified as stamps and logos. Our method shows good performance in case of separating them from text. Moreover, this technique is capable of detecting logos as well as chromatic and achromatic stamps.

关键词： Statistics

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A Novel Stereo Matching Approach for Pedestrian Re-identification 5

A Novel Stereo Matching Approach for Pedestrian Re-identific...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Biswas, Soma Indian Inst Sci Dept Elect Engn Bangalore Karnataka India

ISBN: (纸本)9781467385640

Automatic and reliable identification of pedestrians from multiple camera views is very important for video surveillance and can save a lot of manual effort. The significant variations in viewpoints, poses, illumination and occlusions makes this problem very challenging. Most of the existing approaches addressing this problem handle drastic viewpoint change in a supervised way and thus require labelling new training data for a different pair of camera views. In this paper, we present a novel approach for pedestrian re-identification using stereo matching, which does not require any kind of training. The cost of the stereo matching of two images is used for evaluating the similarity of the images, without performing 3-D reconstruction. We show that this cost is robust to the large pose variations observed in the images captured from multiple cameras. The proposed pedestrian re-identification algorithm is built on top of a dynamic programming stereo matching algorithm. Experimental evaluation on the challenging VIPeR dataset shows the effectiveness of the proposed approach.

关键词： Dynamic programming

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Use of leaf colour for Drought stress analysis in Rice 5

Use of leaf colour for Drought stress analysis in Rice

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Bhugra, Swati Chaudhury, Santanu Lall, Brejesh Indian Inst Technol Dept Elect Engn Delhi India

ISBN: (纸本)9781467385640

We propose a novel approach of utilizing phenomic traits to automatically quantify stress in plants using machine learning techniques. Moisture deficit conditions cause change in leaf color due to decrease in chlorophyll content as chloroplast is damaged by active oxygen species. Therefore, the proposed technique uses leaf color as the phenomic trait to assess stress levels using Relative water content (RWC) as a quantitative proxy. We extracted the change in leaf color in response to drought stress using the color features obtained using Random forest. A regressor has been modeled to predict the stress level of rice genotypes via RWC by employing colour histogram as a feature vector. The experiment was performed with pot images of different rice genotypes under normal and drought stressed conditions. We report a correlation coefficient of 0.89 obtained using this model demonstrating the capability of the presented technique for stress level predictions.

关键词： Regressor Random Forest Phenomics Colour Histogram Relative Water Content

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Adaptive Crypto-Steganosystem for videos based on Information Content and Visual Perception 5

Adaptive Crypto-Steganosystem for videos based on Informatio...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Mukherjee, Prerana Srivastava, Siddharth Lall, Brejesh Asolkar, Saisha Pai, Meera Indian Inst Technol Delhi India Natl Inst Technol Ponda Goa India

ISBN: (纸本)9781467385640

Steganography is the art of hiding secret data inside a carrier media. Most steganographic techniques suffer from the drawback that they are unable to retain the perceptual quality. Using saliency cues for developing an adaptive steganographic technique can help to alleviate this problem. In this work, a novel perception driven robust crypto-steganographic algorithm is proposed for embedding secure data in videos. The proposed scheme selects the payload regions based on natural scene statistics. To further strengthen the scheme and ensure intractability of secure data, the encrypted secret data is embedded in a random manner using jumbling sequence generator in the frames. We utilize perceptual hashing to evaluate the number of bit insertions that will not compromise the perceptual quality. A comprehensive performance evaluation of the proposed scheme is provided to detail the effectiveness. We demonstrate that the scheme shows a lot of promise in being robust against statistical and saliency based attacks.

关键词： Saliency Steganography Encryption Perceptual Quality

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Automated Conversion of 2D to 3D image using Manifold Learning 5

Automated Conversion of 2D to 3D Image using Manifold Learni...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Chahal, Nidhi Pippal, Meghna Chaudhury, Santanu Indian Inst Technol Dept Elect Engn Delhi 110016 India

ISBN: (纸本)9781467385640

Automatic technique of 2D to 3D image conversion is proposed using manifold learning and sequential labeling which generates very reliable and accurate 3D depth maps that are very close to ground truth depths. In paper, LLE which is a non linear and neighborhood preserving embedding algorithm is used for depth estimation of a 2D image. And then, fixed point supervised learning algorithm is applied to construct consistent and smooth 3D output. The high dimensional data points or pixels of the input frames can be represented by a linear combination of its nearest neighbors and a lower dimensional point is reconstructed while preserving the local and geometric properties of the frames. The neighbors are assigned to each input point in the image data set and their weight vectors are computed that best linearly reconstruct the input point from its neighbors. To get the depth value of input point in new image, the reconstruction weights of its closest neighbors in training samples are multiplied with their corresponding ground truth depth values. The fixed point learning algorithm takes depths from manifold and other image features as input vectors and generates more consistent and accurate depth images for better 3D conversion.

关键词： Learning algorithms

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An Improved Multi-layer Codebook Model to Eliminate Ghost Region 5

An Improved Multi-layer Codebook Model to Eliminate Ghost Re...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Badal, Tapas Nain, Neeta Ahmed, Mushtaq Sharma, Vishakha MNIT Dept Comp Sci & Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9781467385640

Codebook model is a widely used method for segmenting foreground pixels. However, it often generates the erroneous detection results with a dynamic background. This paper proposed a multi-layer codebook model for segmenting foreground pixels and a separate layer is utilize to disparate static background from dynamic background. It improve processing speed as system keep history of uncovered background region. Proposed method reduces erroneous positive pixels detected conventionally as ghost region when pixel belongs to background suddenly start moving. To eliminate shadow/illumination effects cone shaped color distance map is utilized in lieu of cylindrical. During experimentation proposed method is tested over numerous videos with complex illumination and background situations. The experimental result shows improvement over rudimentary codebook model and other state-of-art background subtraction model by reducing erroneous positive pixels detected such as ghost region and enhance foreground detection.

关键词： Pixels

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Information set based approach for salient object detection 5

Information set based approach for salient object detection

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Kapoor, Aditi Biswas, K. K. Hanmandlu, M. Indian Inst Technol Delhi Amar Nath & Shashi Khosla Sch Informat Technol New Delhi 110016 India Indian Inst Technol Delhi Dept Comp Sci & Engn New Delhi 110016 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)9781467385640

Human attention tends to get focused on the most prominent components of a scene which are in sharp contrast with the background. These are termed as salient regions. Saliency is defined in terms of local and global feature contrasts. The human brain perceives an object of salient type based on its difference with the surroundings in terms of color and texture. There have been many color based approaches in the past for salient object detection. In this paper, we define the uncertainty of a window being salient or background in terms of information extracted from different color components. The uncertainty associated with the elements of a fuzzy set is described by a membership function, which gives the degree of association of each element to the set. The overall uncertainty is sought to be quantified by an entropy function. To locate the salient parts of the image, we make use of the entropy to compute a new set of features from color and luminance components of the image. Extensive comparisons with the state-of-the-art methods in terms of precision, recall and F-Measure are made on a publicly available dataset to prove the effectiveness of this approach.

关键词： Membership functions

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Feature Extraction and Identification of indian Currency Notes 5

Feature Extraction and Identification of Indian Currency Not...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Kamal, Snigdha Chawla, Simarpreet Singh Goel, Nidhi Raman, Balasubramanian Delhi Technol Univ Dept Elect & Commun Engn New Delhi India Indian Inst Technol Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9781467385640

Banknote identification systems, with their wide applications in Automated Teller Machines (ATMs), vending machines and currency recognition aids for the visually impaired, are one of the most widely researched fields today. The present paper proposes a novel technique for recognition of indian currency banknotes by adopting a modular approach. The proposed work extracts distinct and unique features of indian currency notes such as central numeral, RBI seal, colour band and identification mark for the visually impaired and employs algorithms optimized for the detection of each specific feature. The proposed technique has been evaluated over a large data set for recognition of indian banknotes of various denominations and physical conditions including new notes, wrinkled notes and non-uniform illumination. Thorough analysis yields a high true positive rate (desired feature identified correctly) of 95.11% and a low false positive rate (undesired feature recognition minimized) of 0.09765% for emblem recognition, an accuracy of 97.02% for central numeral detection, and 100% accuracies for both recognition of identification mark and colour matching in CIE LAB colour space.

关键词： Feature extraction

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Integrated Algorithm for Different Tracking Challenges 5

Integrated Algorithm for Different Tracking Challenges

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shah, Jay Sai Subrahmanyam, G. R. K. Mishra, Deepak Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India Indian Inst Space Sci & Technol Dept Earth & Space Sci Trivandrum 695547 Kerala India

ISBN: (纸本)9781467385640

This paper presents an efficient combination of two well-known tracking algorithms, Tracking-Learning-Detection (TLD) and Compressive Tracking (CT) to devise an algorithm which takes advantages of both and outperforms them on their short-ends by virtue of other. TLD fails in cases including full out-of-plane rotation, fast motion and articulated object tracking. While CT fails in resuming tracking once the object leaves the frame and comes back. We propose a combining algorithm mentioned as Algorithm 1, which robustly handles all the tracking challenges. Different thresholds are set which can be varied to weigh each component as required. The proposed algorithm is tested on different test sequences involving challenging tracking scenarios such as fast motion and their success rates are calculated in Table I. The proposed algorithm works favourably against both algorithms in terms of robustness and success rate.

关键词： Tracking (position)

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A Hierarchical Class-Grouping Approach, and a Study of Classification Strategies for Leaf Classification 5

A Hierarchical Class-Grouping Approach, and a Study of Class...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Prajapati, Ravinder Bhavsar, Arnav Sao, Anil Indian Inst Technol Sch Comp & Elect Engn Mandi India

ISBN: (纸本)9781467385640

Fine-grained visual classification has been considered for image data in various domains of environmental importance such as birds, animals and plants. This work considers the classification problem of the latter, based on the leaf shape. Traditional works in such areas typically propose better features, or sophisticated classification frameworks. In this work, we ask a different question: Given simple and efficient features, and a well-known binary classifier such as support vector machine (SVM), among various strategies, what may be a good way to pose the multi-class classification problem as multiple binary classifications ? In this respect, we compare three different strategies, all of which use the same set of features. From our results, we conclude that, one of these three approaches, based on hierarchical class-grouping, clearly outperforms the others, with high classification accuracy. This suggest that classification strategy is an important aspect for the given features and classifiers. To our knowledge, such a study in the fine-grained classification area (and particularly for the nascent area of leafclassification), has not yet been explored.

关键词： Classification (of information)

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