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检索条件"任意字段=6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013"

共 71 条记录，以下是1-10 订阅

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Facial Expression recognition using Spatial Feature Extraction and Ensemble Deep Networks 6

Facial Expression Recognition using Spatial Feature Extracti...

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6th International conference on pattern recognition and image analysis (IPRIA)

作者： Afshar, Elham Khotanlou, Hassan Alighardash, Elham Bu Ali Sina Univ Dept Comp Engn Hamadan Iran

ISBN: (纸本)9798350314557

Researchers have shown that 55% of concepts are conveyed through facial emotion and only 7% are conveyed by words and sentences, so facial expression plays an important role in conveying concepts in human communications. In recent years, due to the improvement of artificial neural networks, many studies have been conducted related to facial expression recognition. this paper presents a method based on ensemble classification using convolutional neural networks to recognize facial emotions. the concatenation of spatial features with global features is used as a feature map for the classification stage in the committee network. Two committee networks are fed separately with LBP and raw images. After training the two committee networks, to classify the emotion, the maximum probability between the two networks is considered as the final output. the proposed method was applied and tested on the FER2013 dataset. Our proposed method is more accurate than many leading methods, and in competition with the successful model that has a more complex architecture and higher computational cost, it has been able to achieve acceptable results with a simple architecture.

关键词： Facial Expression recognition Local Binary pattern image Spatial Features Ensemble Networks Deep Learning

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An End-to-End Approach for Seam Carving Detection Using Deep Neural Networks 10th

An End-to-End Approach for Seam Carving Detection Using Deep...

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10th iberian conference on pattern recognition and image analysis (ibpria)

作者： Moreira, thierry P. Santana, Marcos Cleison S. Passos, Leandro A. Papa, Joao Paulo da Costa, Kelton Augusto P. Sao Paulo State Univ Dept Comp Av Eng Luiz Edmundo Carrijo Coube 14-01 BR-17033360 Bauru SP Brazil Univ Wolverhampton Sch Engn & Informat CMI Lab Wolverhampton England

ISBN: (纸本)9783031048814;9783031048807

Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant information, i.e., regions composed of pixels with similar intensity, it can also be used for tampering images by inserting or removing relevant objects. therefore, detecting such a process is of extreme importance regarding the image security domain. However, recognizing seam-carved images does not represent a straightforward task even for human eyes, and robust computation tools capable of identifying such alterations are very desirable. In this paper, we propose an end-to-end approach to cope with the problem of automatic seam carving detection that can obtain state-of-the-art results. Experiments conducted over public and private datasets with several tampering configurations evidence the suitability of the proposed model.

关键词： Seam carving Convolutional neural networks image security

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image Feature Detection Based on Phase Congruency by Monogenic Filters with New Noise Estimation 9th

Image Feature Detection Based on Phase Congruency by Monogen...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Jacanamejoy Jamioy, Carlos Meneses-Casas, Nohora Forero, Manuel G. Univ Ibague Fac Ingn Ibague Colombia Univ Ibague Fac Ciencias Nat & Matemat Ibague Colombia Univ Ibague Carrera 22 Calle 67 B Ambala Ibague Colombia

ISBN: (纸本)9783030313326;9783030313319

Phase congruency is a relative unknown and powerful image processing technique for segmentation, having been used in diatom image processing, microscopic algae found in water and used to evaluate its quality. However, an important limitation of phase congruence is its sensitivity to noise. To prevent noise from affecting segmentation results, a good noise level estimation is necessary. It can be done with the analysis of the image of local energy. In this paper, we propose the use of the Weibull distribution to estimate the noise profile of the local energy image. the results are compared, in diatom images, with those obtained with the commonly employed Rayleigh distribution and the exponential. the results showed that the Weibull distribution allows a better estimation of the noise level.

关键词： image segmentation Phase congruency Weibull distribution Diatom Noise estimation Rayleigh distribution Exponetial distribution Noise rejection

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On the Direction Guidance in Structure Tensor Total Variation Based Denoising 9th

On the Direction Guidance in Structure Tensor Total Variatio...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Demircan-Tureyen, Ezgi Kamasak, Mustafa E. Istanbul Kultur Univ Fac Engn Dept Comp Engn TR-34156 Istanbul Turkey Istanbul Tech Univ Fac Comp & Informat Dept Comp Engn TR-34390 Istanbul Turkey

ISBN: (纸本)9783030313326;9783030313319

this paper introduces a new analysis-based regularizer, which incorporates the neighborhood-awareness of the structure tensor total variation (STV) and the tunability of the directional total variation (DTV), in favor of a pre-selected direction with a pre-selected dose of penalization. In order to show the utility of the proposed regularizer, we consider the problem of denoising uni-directional images. Since the regularizer is convex, we develop a simple optimization algorithm by realizing its proximal map. the quantitative and the visual experiments demonstrate the superiority of our regularizer over DTV (only for scalar-valued images) and STV.

关键词： image denoising Total variation Structure tensor Directional total variation Convex optimization Inverse problems

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Food recognition by Integrating Local and Flat Classifiers 9th

Food Recognition by Integrating Local and Flat Classifiers

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Aguilar, Eduardo Radeva, Petia Univ Catolica Norte Antofagasta Chile Univ Barcelona Barcelona Spain Comp Vis Ctr Bellaterra Spain

ISBN: (纸本)9783030313326;9783030313319

the recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier;otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier.

关键词： CNNs Deep learning Epistemic Uncertainty image classification Food recognition

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Wave Front Tracking in High Speed Videos Using a Dynamic Template Matching 9th

Wave Front Tracking in High Speed Videos Using a Dynamic Tem...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Maharjan, Samee Univ South Eastern Norway USN Dept Proc Energy & Environm Technol Notodden Norway

ISBN: (纸本)9783030313326;9783030313319

In recent years, image processing has been evolving as an important tool to estimate the properties of a generated wave during a gas experiments. the wave properties were estimated by tracking a wave fronts in the images of a high speed video, captured during the experiment. In this work, we purposed a dynamic template matching method, based on the mean square error (MSE) between the intensity values of a template and its foot print in an image to track the wave front. At first, a dynamic template of a predefined size [5 x 20] was created, whose values varies according to the minimum and maximum intensity of the considered image. Secondly to reduce the processing time, a bounding box was set around the area of interest in the considered image, such that the matching process is limited within the area covered by the bounding box. the purposed method was tested for four different high speed videos from four different gas experiments conducted two different experimental set-ups. All the results from the purposed method are within an acceptable accuracy.

关键词： image processing application Dynamic template Front tracking

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BELID: Boosted Efficient Local image Descriptor 9th

BELID: Boosted Efficient Local Image Descriptor

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Suarez, Iago Sfeir, Ghesn Buenaposada, Jose M. Baumela, Luis Univ Politecn Madrid Dept Inteligencia Artificial Campus Montegancedo S-N Boadilla Del Monte 28660 Spain Ctr Empresas UPM Graffter Campus Montegancedo S-N Pozuelo De Alarcon 28223 Spain Univ Rey Juan Carlos ETSII C Tulipan S-N Mostoles 28933 Spain

ISBN: (纸本)9783030313326;9783030313319

Efficient matching of local image features is a fundamental task in many computer vision applications. Real-time performance of top matching algorithms is compromised in computationally limited devices, due to the simplicity of hardware and the finite energy supply. In this paper we present BELID, an efficient learned image descriptor. the key for its efficiency is the discriminative selection of a set of image features with very low computational requirements. In our experiments, performed both in a personal computer and a smartphone, BELID has an accuracy similar to SIFT with execution times comparable to ORB, the fastest algorithm in the literature.

关键词： Computer vision for smartphones Feature descriptors extraction Learned descriptors Boosting

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Impact of Fused Visible-Infrared Video Streams on Visual Tracking 9th

Impact of Fused Visible-Infrared Video Streams on Visual Tra...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Vujasinovic, Stephane Becker, Stefan Scherer-Negenborn, Norbert Arens, Michael Fraunhofer Inst Optron Syst Technol & Image Explo Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9783030313326;9783030313319

Currently state-of-the-art trackers rely on fully convolutional neural network (FCNN) for extracting salient features in order to create an appearance representation of the target. Ordinarily, most of them intend to work with input streams from the visible spectrum, yet how does an input stream from the infrared spectrum and a fused visible-infrared stream affect their performances and how does it benefit or detriment them? Towards this end, we compare the performance of various reference trackers utilizing FCNN for feature extraction, on visible, infrared and fused spectrums. By utilizing a carefully processed publicly available data set for the evaluation, containing visible-infrared paired sequences, we ensure to find synchronized and same attributes at the same locations, effectively studying only the impact of a spectral change. thus, by analyzing quantitative results, we identify visual attributes which benefit or detriment from a fused approach on typical visual tracking scenarios.

关键词： Visual tracking Infrared imagery image fusion visible-infrared

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Evaluating the Impact of Color Information in Deep Neural Networks 9th

Evaluating the Impact of Color Information in Deep Neural Ne...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Buhrmester, Vanessa Muench, David Bulatov, Dimitri Arens, Michael Fraunhofer IOSB Ettlingen Germany

ISBN: (纸本)9783030313326;9783030313319

Color images are omnipresent in everyday life. In particular, they provide the only necessary input for deep neural network pipelines, which are continuously being employed for image classification and object recognition tasks. Although color can provide valuable information, effects like varying illumination and specialties of different sensors still pose significant problems. However, there is no clear evidence how strongly variations in color information influence classification performance throughout rearward layers. To gain a deeper insight about how Convolutional Neural Networks make decisions and what they learn from input images, we investigate in this work suitability and robustness of different color augmentation techniques. We considered several established benchmark sets and custom-made pedestrian and background datasets. While decreasing color or saturation information we explore the activation differences in the rear layers and the stability of confidence values. We show that Luminance is most robust against changing color system in test images irrespective of degraded texture or not. Finally, we present the coherence between color dependence and properties of the regarded datasets and classes.

关键词： Color space representation Degraded texture Color dependence Robustness of color model change image classification

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Modified DBSCAN Algorithm for Microscopic image analysis of Wood 9th

Modified DBSCAN Algorithm for Microscopic Image Analysis of ...

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9th iberian conference on pattern recognition and image analysis (ibpria)

作者： Martins, Aurora L. R. Marcal, Andre R. S. Pissarra, Jose Univ Porto Fac Ciencias Porto Portugal FCUP GreenUPorto Res Ctr Sustainable Agrifood Prod Porto Portugal

ISBN: (纸本)9783030313326;9783030313319

the analysis of the intern anatomy of wood samples for species identification is a complex task that only experts can perform accurately. Since there are not many experts in the world and their training can last decades, there is great interest in developing automatic processes to extract high-level information from microscopic wood images. the purpose of this work was to develop algorithms that could provide meaningful information for the classification process. the work focuses on hardwoods, which have a very diverse anatomy including many different features. the ray width is one of such features, with high diagnostic value, which is visible on the tangential section. A modified distance function for the DBSCAN algorithm was developed to identify clusters that represent rays, in order to count the number of cells in width. To test both the segmentation and the modified DBSCAN algorithms, 20 images were manually segmented, obtaining an average Jaccard index of 0.66 for the segmentation and an average index M = 0.78 for the clustering task. the final ray count had an accuracy of 0.91.

关键词： Wood anatomy Ray width image processing DBSCAN

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