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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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Visual analysis of leadership style practical knowledge graph under the background of big data— —based on Citespace and Python

Visual analysis of leadership style practical knowledge grap...

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Education, Knowledge and Information Management (ICEKIM), International conference on

作者： Xiaona Bao Tian Ou School of Management Dalian Polytechnic University Dalian China

ISBN: (纸本)9781665416672

Leadership style refers to the work behavior mode reflected by the leaders in the enterprise when they carry out management work. When facing different work situations, leaders usually adopt different behavior patterns to achieve their own goals. The behavior pattern of leaders is related to personal experience and work practice, and has a strong personalized color. Therefore, the research on the leadership style of different leaders has high theoretical and practical value. Based on the relevant theories of management, on the basis of referring to a large number of literature and previous relevant research, this study uses CiteSpace and Python software, selects the papers related to leadership style collected by web of science as the data source, and uses the bibliometric analysis method to analyze the current situation, hot spots and trends of leadership style research.

关键词： Leadership Visualization image color analysis Soft sensors Education Bibliometrics Market research

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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...

引用

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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Leveraging Sentinel-2 Multispectral Data and Machine Learning Algorithms for Land Use Land Cover Mapping in Semi-Arid Regions

Leveraging Sentinel-2 Multispectral Data and Machine Learnin...

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Advancement in Computation & Computer Technologies (InCACCT), International conference on

作者： Narayan Vyas Koushik Sundar Amit Sharma Pradeep Shree Adhikari Department of Computer Science and Application Vivekananda Global University Jaipur India Citibank NA Jersey City NJ USA Department of Computer Science and Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Land use and land cover (LULC) classification is a fundamental approach for landscape pattern mapping and monitoring of natural ecosystems and anthropogenic activities and is, therefore, an essential base for resource management to support sustainability. The land cover classes were classified in this study using multispectral data. The study utilizes Sentinel-2 data captured in February 2024, and a median composite image was generated using the Google Earth Engine (GEE) platform. Three different classification algorithms, random forest (RF), classification and regression tree (CART), and naive bayes (NB), were used to produce LULC maps. The algorithms are efficient in capturing different land cover patterns with spectral diversity. The performance with essential measures such as overall accuracy (OA) and Kappa coefficient (KC) has been evaluated to assess classification outcomes. Among these classifiers, the RF classifier with the maximum OA (96.91%) and KC (96.10%) exhibited optimal results and robustness for LULC classification. CART followed this, with OA of 95.88% and KC of 94.81%. NB achieved an OA of 85.57% and KC of 81.93%. It confirms the validity of the suggested method for obtaining LULC maps through Sentinel-2 data and ML algorithms, achieving maximum accuracy in semi-arid regions.

关键词： Earth Machine learning algorithms Accuracy Satellites Land surface Classification algorithms Internet Random forests Engines Classification tree analysis

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Annotating Documents using Active Learning Methods for a Maintenance analysis Application 20

Annotating Documents using Active Learning Methods for a Mai...

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3rd International conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Pope, James Terwilliger, Mark Connell, J. A. (Jim) Talley, Gabriel Blozik, Nicholas Taylor, David Univ North Alabama Florence AL 35632 USA Univ Montevallo Montevallo AL USA Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)9781450375511

The aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machine learning techniques has been shown to be effective in recognising these documents for further information extraction. A well known deficiency of supervised learning approaches is that annotating sufficient documents to create an effective model requires valuable human effort. This paper first shows how to obtain a representative sample from a supplier's corpus. Given this sample of unlabelled documents an active learning approach is used to select which documents to annotate first using a normalised certainty measure derived from a soft classifier's prediction distribution. Finally the accuracy of various selection approaches using this certainty measure are compared along each iteration of the active learning cycle. The experiments show that a greedy selection method using the uncertainty measure can significantly reduce the number of annotations required for a certain accuracy. The results provide valuable information for users and more generally illustrate an effective deployment of a machine learning application.

关键词： Active Learning Document Classification

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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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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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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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