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

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Mean-shift based segmentation of cell nuclei in cervical PAP-smear images

Mean-shift based segmentation of cell nuclei in cervical PAP...

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

作者： Paridhi Agarwal Anil Sao Arnav Bhavsar Center for Converging Technologies University of Rajasthan School of Computing and Elec. Engg. Indian Institute of Technology Mandi

Segmentation of cell nuclei in PAP-smear cervical images is of preeminent importance in computer-aided-diagnostic screening technique for cervical cancer. This paper proposes a novel nuclei segmentation approach which builds upon the mean-shift method. The mean-shift method is applied on the cell images which first undergo a decorrelation-stretch contrast enhancement. The results of mean-shift based approach is refined further using morphological operations. We have validated results of segmentation on dataset which includes 900 images with the given ground truth. We demonstrate that our simple and efficient approach yields high validation rate on a large image dataset. In addition, we also show encouraging visual results on another set of more complex real images.

关键词： image segmentation image color analysis Morphological operations Biomedical imaging Cervical cancer Microscopy Correlation

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Gradient sensitive kernel for image Denoising, using Gaussian Process Regression

Gradient sensitive kernel for Image Denoising, using Gaussia...

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

作者： Arka Ujjal Dey Gaurav Harit Dept. of Computer Science and Engineering Indian Institute of Technology Jodhpur

We target the problem of image Denoising using Gaussian Processes Regression (GPR). Being a non-parametric regression technique, GPR has received much attention in the recent past and here we further explore its versatility by applying it to a denoising problem. The focus is primarily on the design of a local gradient sensitive kernel that captures pixel similarity in the context of image denoising. This novel kernel formulation is used to shape the smoothness of the joint GP prior. We apply the GPR denoising technique to small patches and then stitch back these patches, this allows the priors to be local and relevant, also this helps us in dealing with GPR complexity. We demonstrate that our GPR based technique gives better PSNR values in comparison to existing popular denoising techniques.

关键词： Kernel Noise reduction Signal to noise ratio Noise measurement Ground penetrating radar Gaussian processes Mathematical model

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Deep transfer learning with ontology for image classification

Deep transfer learning with ontology for image classificatio...

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

作者： Umang Gupta Santanu Chaudhury Dept. of Electrical Engineering Indian Institute of Technology Delhi

Purely, data-driven large scale image classification has been achieved using various feature descriptors like SIFT, HOG etc. Major milestone in this regards is Convolutional Neural Networks (CNN) based methods which learn optimal feature descriptors as filters. Little attention has been given to the use of domain knowledge. Ontology plays an important role in learning to categorize images into abstract classes where there may not be a clear visual connect between category and image, for example identifying image mood - happy, sad and neutral. Our algorithm combines CNN and ontology priors to infer abstract patterns in indian Monument images. We use a transfer learning based approach in which, knowledge of domain is transferred to CNN while training (top down transfer) and inference is made using CNN prediction and ontology tree/priors (bottom up transfer ). We classify images to categories like Tomb, Fort and Mosque. We demonstrate that our method improves remarkably over logistic classifier and other transfer learning approach. We conclude with a remark on possible applications of the model and note about scaling this to bigger ontology.

关键词： Ontologies Training Feature extraction Linear programming Logistics Shape Neural networks

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Semantic description of a video using representative frames

Semantic description of a video using representative frames

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

作者： Ishan Jindal Shanmuganathan Raman Electrical Engineering Indian Institute of Technology Gandhinagar

ISBN: (纸本)9781467385657

Analysis of a very long video and semantically describe the contents is a challenging task in computer vision. The present approaches such as video shot detection and summarization address this problem partially while maintaining the temporal coherency. To reduce the user efforts for seeing the whole video we have introduced a new technique which combines similar content irrespective of their presence at different time instants. In this approach, we automatically identify only the representative frames corresponding to similar scenes which were captured at different instants of time. We also provide the labels of the objects that are present in the representative frames along with the compact representation for the video. We achieve the task of semantic labelling of frames in a unified framework using a deep learning framework involving pre-trained features through a convolutional neural network. We show that the proposed approach is able to address the semantic labelling effectively as justified by the results obtained for videos of different scenes captured through different modalities.

关键词： Semantics Histograms Cameras YouTube Surveillance Feature extraction Labeling

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An enhanced decision based Unsymmetric Trimmed Median Filter for removal of high density salt and papper noise

An enhanced decision based Unsymmetric Trimmed Median Filter...

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

作者： Shruti Singh Anil Kumar Tiwari The LNM Institute of Information Technology Jaipur India Indian Institute of Technology Jodhpur India

This paper proposes a new algorithm for restoration of gray scale images corrupted by salt and pepper noise(SPN). The proposed algoritm identifies a pixel as noisy if its intensity value is 0 or 255 and processes it using pixels in a 3×3 window. If the window consists of noisy and non-noisy pixels, then the pixel to be processed is replaced with the trimmed median value of the non-noisy pixels. However, if only noisy pixels are there in the window then their mean value is used to process the pixel. The proposed method uses processed (i.e. the de-noised) pixels in the window while processing the noisy pixels and shows significantly better performance, particularly at high noise density, as compared to various methods reported in literature. Experimental results show improvements both visually and quantitatively compared to other reported methods.

关键词： Noise measurement Filtering algorithms Noise reduction PSNR image edge detection Simulation Algorithm design and analysis

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Integrated algorithm for different tracking challenges

Integrated algorithm for different tracking challenges

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

作者： Jay Shah Sai Subrahmanyam G. R. K Deepak Mishra Dept. of Avionics Indian Institute of Space sciences and Technology Trivandrum Kerala India Dept. of Earth & space Science Indian Institute of Space sciences and Technology Trivandrum Kerala India

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： Target tracking Detectors Feature extraction Object tracking Robustness Aerospace electronics

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Global vessel symmetry for optic disc detection in retinal images

Global vessel symmetry for optic disc detection in retinal i...

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

作者： Rashmi Panda N. B. Puhan G. Panda School of Electrical Sciences Indian Institute of Technology Bhubaneswar India

Optic disc (OD) detection is an important step in developing computer aided screening systems suitable for glaucoma analysis. In this paper, we present a new method for automatic optic disc detection in retinal (fundus) images. The method is based upon the distribution of major blood vessels. The blood vessels originate from the OD and their random distribution pattern can be approximately divided into two halves by a global symmetric axis passing through the centroid and near the optic disc. We detect this symmetry axis by using partial Hausdorff distance (PHD) measure. Then, the OD center is detected by applying the brightness property of the optic disc region. The proposed method is evaluated and compared on DRIVE, STARE and HRF databases. The average performance of the proposed method is found as: 97.5% in DRIVE, 97.5% in STARE and 100% in HRF database.

关键词： Blood vessels Retina Optical imaging Databases image color analysis Biomedical optical imaging

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Biometric recognition system for challenging faces

Biometric recognition system for challenging faces

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

作者： Saiyed Umer Bibhas Chandra Dhara Bhabatosh Chanda Sciences Unit Indian Statistical Institute India

A novel method for face recognition system using challenging profile and frontal faces is proposed in this paper. The proposed face recognition system consists of pre-processing, feature extraction and classification components. In this work, for pre-processing, the face region is extracted using facial landmark points, obtained by the tree structured part model. During feature extraction, SIFT descriptors are computed from the detected face region, and Spatial Pyramid Matching approach based on Locality constraints Linear Coding technique is employed for feature representation. Finally multi-class linear SVM classifier is employed to do the classification job. Extensive experimental results have been performed to show that the proposed algorithm has satisfying performance as compared to existing methods for IITK, CASIA-FACE-V5, LIBOR, ORL and Extended YALE-B face databases.

关键词： Feature extraction Databases Face recognition Encoding Training Testing Histograms

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Use of leaf colour for drought stress analysis in rice

Use of leaf colour for drought stress analysis in rice

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

作者： Swati Bhugra Santanu Chaudhury Brejesh Lall Department of Electrical Engineering Indian Institute of Technology Delhi

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： image color analysis Stress Feature extraction Histograms Estimation Predictive models image segmentation

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A novel approach for image dehazing combining visible-NIR images

A novel approach for image dehazing combining visible-NIR im...

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

作者： Ashish V. Vanmali Samrudha G. Kelkar Vikram M. Gadre Department of Electrical Engineering Indian Institute of Technology Bombay Powai Mumbai India

image dehazing either using single visible image or using visible and near-infrared (NIR) image pair has seen growing interest in last decade for improving visibility in landscape photographs. In this paper, we propose a novel approach for image dehazing scheme using a pair of visible and NIR images. The dehazing mechanism estimates depth map and airlight color using the visible-NIR scene statistics and uses them to form a haze-free image. Experiments on a variety of hazy images demonstrate that our method achieves high degree of detail recovery over the existing image dehazing algorithms. The resultant images exhibit a very good blend of details, contrast and color. The proposed algorithm is less computationally demanding and is fully automatic. The results are superior in both visual as well as quantitative analysis compared to state-of-the-art image dehazing algorithms.

关键词： image color analysis Estimation Color Channel estimation Attenuation Databases Algorithm design and analysis

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