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检索条件"主题词=Detection algorithms"

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Pneumonia detection in chest X-rays: a deep learning approach based on ensemble RetinaNet and Mask R-CNN

Pneumonia Detection in chest X-rays: a deep learning approac...

引用

International Conference on Advanced Cloud and Big Data (CBD)

作者： Liu mao Tan Yumeng Chen Lina College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China College of Physics and Electronic Information Engineering Zhejiang Normal Univercity Jinhua China

The following topics are dealt with: learning (artificial intelligence); feature extraction; convolutional neural nets; pattern classification; traffic engineering computing; cloud computing; recurrent neural nets; de... 详细信息

关键词： Deep learning Training Radiography Performance evaluation Lung Robustness detection algorithms

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Change detection in the amazon rainforest with radiometric rotation technique RCEN multi-spectral case study: Guarayos - Bolivia.

Change detection in the amazon rainforest with radiometric r...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： H. Ferrufino Ugarte T. Zawila-Niedzwiecki J. R. Santos F. D. Maldonado Fh-Eberswalde-Forestry Faculty University of Applied Sciences Eberswalde Brandenburg Germany INPE-Instituto Nacional de Pesquisas Espaciais Brazil Instituto Nacional de Pesquisas da Amazônia-INPA Manaus Brazil

A working group of three institutions was set up to develop this study: University of Applied Sciences Eberswalde (Germany), National Institute for Space Research (INPE, Brazil) and National Institute for Amazon Research (INPA, Brazil). The main task is to apply in the Guarayos region (Bolivia), the multi- temporal change detection algorithm "RCEN multi-spectral". The study area is located in Guarayos-Bolivia, characterized by two main high forests landscapes, "The Amazon Region" and "The Brazilian Paranaense Region"; the approach of the change detection is taken under multivariate analysis (three spectral bands), with data coming from two kinds of sensor ETM+/Landsat-7 and CCD/CBERS-2. The image detection was transformed from continuous image (floating-point) to thematic, through slicing and labeling process. Hence it is possible to discriminate five thematic classes: two related to degradation, two referring to regeneration and one of no-change. The change detection map shows: in the timeframe studied 11% of all area under study presents deforestation patterns, on the other hand the regeneration class is not significant. In conclusion the methodology has good performance and it is evolving in landscapes with high humidity complications.

关键词： Radiometry detection algorithms Spectral analysis Sensor phenomena and characterization Satellites Remote sensing Charge coupled devices Labeling Degradation Humidity

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A spatial outlier detection algorithm based multi-attributive correlation

A spatial outlier detection algorithm based multi-attributiv...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhan-Quan Wang Shen-Kang Wang Tao Hong Xiao-Hui Wan Institute of Artificial Intelligence College of Computer Science University of Zhejiang Hangzhou China

ISBN: (纸本)0780384032

Spatial outlier is a spatial object whose non-spatial attributive values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, useful spatial patterns for further analysis. Drawbacks of the existing methods are that they can only detect outliers under attributes when the attributive correlation is not considered and the normal objects tend to be falsely detected as spatial outliers when their neighborhood contains true spatial outliers. This paper presents a spatial outlier detection algorithm to overcome the disadvantages. In addition, the results demonstrated that our approach could accurately detect spatial outliers when the attributive correlation was calculated, and our approach not only avoided detecting false spatial outliers but also found true spatial outliers ignored by the existing methods in a real-world geographical data set.

关键词： detection algorithms Object detection Geographic Information Systems Transportation Multidimensional systems Statistics Graphics Testing Scattering Machine learning algorithms

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Detecting micro-calcifications in mammograms by using an intelligent computer-aided detection algorithm

Detecting micro-calcifications in mammograms by using an int...

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International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Bai-kun Wan Qing-kai Liu Rui-ping Wang Xuchen Cao Department of Biomedical Engineering Tianjin University TJU Tianjin China Division of Breast Tianjin Medical University Tumor Hospital Tianjin China

It is well known that clustered micro-calcifications (MCC) on mammograms are important hints of breast cancer. Nevertheless, it is a complex and difficult task for radiologists to detect the clustered MCC from the tissue background of mammograms only by the naked eye. This paper describes a method for computer-aided detection of MCC in digital mammograms. The detection algorithm mainly consists of two different methods. The first one, based on the difference-image technique, recognizes high-frequency signals and very high-frequency noise. The second one is able to extract high-frequency signals by exploiting a wavelet based noise suppression and artificial neural network (ANN) classification. In the false-positive reduction step, false signals are separated from MCC by means of an AND operation on signals from the two methods. The algorithm was tested with a series of clinical mammograms. The results were compared with the judgment of radiological experts, and a sensitivity of 78% was obtained at a relatively low false-positive (FP) detection per image.

关键词： detection algorithms Breast cancer Mammography Artificial neural networks Cancer detection Biomedical computing Biomedical imaging Hospitals Low-frequency noise Pixel

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Fire detection based on color filters and Bag-of-Features classification

Fire detection based on color filters and Bag-of-Features cl...

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Student Conference on Research and Development (SCOReD)

作者： Kumaraguru Poobalan Siau-Chuin Liew Faculty of Computer Science and Software Engineering University Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9781467395731

Incidents or fire outbreaks are very common accidents occurring in Malaysia. The damage caused by this type of incident is very catastrophe towards nature and humans. Due to this fact, the need for fire detection application has greatly increase in recent years. In this paper we proposed a fire detection algorithm base using a combination of RGB and HSL filter to detect the color of the fire which is mainly comprehended by the intensity of the component R which is red color. Then Bag-of-Features (BoF) classification model was used to classify and calculate the rate for fire present. The overall accuracy of the algorithm obtain is 98% and the efficiency is 89%. The classification rate for the present of fire is 97.6%.

关键词： Fires Image color analysis Classification algorithms Algorithm design and analysis Filtering algorithms Conferences detection algorithms

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Copy-move forgery detection in digital image

Copy-move forgery detection in digital image

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Congress on Image and Signal Processing, CISP

作者： Li Kang Xiao-ping Cheng College of Computer and Information Science Southwest University Chongqing China

Copy-move forgery is a specific type of image tampering where a part of the image is copied and pasted somewhere else in the image with the intent to cover an important image feature. Hence, the goal in detection of copy-move forgeries is to detect image areas that are same or extremely similar. In this paper, we investigate the problem of detecting the copy-move forgery and describe an efficient and reliable passive-blind detection method. The method used block-matching procedures, which first divided the image into the same size block, then applied improved singular value decomposition to all of the image blocks to yield a reduced dimension representation for forming the singular value feature matrix of image blocks which was lexicographically sorted. Later, a matching step took place where the aim is to find the duplicated blocks based on their feature vectors. A forgery detection decision is made only if correlation coefficient threshold reached which we set. The experimental result shows that the algorithm has strong detection capability and anti-noise capability.

关键词： Forgery Singular value decomposition Matrix decomposition Correlation Pixel detection algorithms

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An automatic siren detection algorithm using Fourier Decomposition Method and MFCC

An automatic siren detection algorithm using Fourier Decompo...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Binish Fatimah A. Preethi V. Hrushikesh Akhilesh Singh B. Harshanikethan R. Kotion CMR Institute of Technology Bengaluru

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

In this work, an automatic ambulance detector has been proposed using features extracted from the siren of the ambulance. The smart ambulance detector can reduce the time required by an ambulance to reach their destination in emergency situations. In the present scenario, the cars are designed to provide more and more luxury to the driver and insulation from the outside noise. Along with the traffic noise, the siren sounds also get muffed, leaving the drivers clueless to the emergency vehicles in their vicinity. In these cases, the proposed system can be used to alert the driver about the approaching emergency vehicle. The proposed system uses audio sensors to record the siren sound and pre-process the acquired signal using a bandpass filter. In this work, two sets of features are computed, the first set consists of Mel-frequency cepstrum coefficients of the filtered signal. For the second set, the signal is decomposed in frequency domain using Fourier decomposition method. Statistical features such as kurtosis, energy, and variance are computed from each of the sub-bands. Relevant features are selected based on the Kruskal-Wallis test. The selected feature set is then used to train a machine learning model to identify siren sounds from the background traffic noise. In this work, we have compared the performance of various machine learning algorithms like kNN, SVM and ensemble bagged trees to select the best model. The dataset used in this work includes signals from two publicly available datasets with ambulance siren audio files and traffic sound files, and also audio data collected from different sources on the internet. We have also recorded the siren sounds in city traffic conditions.

关键词： Machine learning algorithms Computational modeling Cepstrum Detectors Feature extraction detection algorithms Vehicles

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Application of Computer Vision for Efficient Maintenance of Cleanliness and Order in Commercial Areas: An Invention Approach for Automated Management in Commercial Zones 8

Application of Computer Vision for Efficient Maintenance of ...

引用

8th International Conference on Communication and Information Systems, ICCIS 2024

作者： Llacta, Jesus David Villon Simeon, Wiler Yosef Hurtado Rosales, Jorge Martin Vega Universidad Continental Facultad de Ingeniería Huancayo Peru Universidad Continental Faculty of Engineering Huancayo Peru

ISBN: (纸本)9798331531171

This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing commercial spaces, this paper proposes a technological model that utilizes strategically placed cameras and image processing algorithms to detect disorganized or dirty tables, sending real-time alerts to cleaning staff via a mobile application. The goal is to optimize resource use, reduce response times, and enhance customer experience. To validate the model, an experiment was conducted with a sample of 30 customers, measuring their satisfaction levels before and after the system's implementation. The results, analyzed through statistical tests, show a significant improvement in customer satisfaction. Additionally, the study discusses its limitations and proposes future research directions, including an evaluation of the costs associated with the automated system compared to traditional methods. © 2024 IEEE.

关键词： automation cleaning management commercial areas Computer vision customer experience detection algorithms image processing operational efficiency

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Parallel processing for distance-based outlier detection on a multi-core CPU

Parallel processing for distance-based outlier detection on ...

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IEEE International Workshop on Computational Intelligence & Applications (IWCIA)

作者： Junki Oku Keiichi Tamura Hajime Kitakami Graduate School of Information Sciences Hiroshima City University Japan

Outliers are data objects that are not highly likely to occur. These are unusualness data objects such as errors, fraud data, and rare data. In the last few decades, outlier detection has attracted much attention from researchers, because it is widely used for many different application domains. Distance-based outlier detection, which is a non-parametric approach, identifies unusual data objects in a database, where their distance to neighbors is used as a measure of unusualness. algorithms for distance-based outlier detection are known for their significant computation time. One of the most successful algorithms for the improvement of the distance-based outlier detection algorithms is Orca, which is based on nested loop with randomization and a simple pruning rule. In this paper, we propose a new parallelization model for the parallel processing of Orca-based outlier detection on a multi-core CPU. The proposed parallelization model utilizes data parallelism and a multi-thread model. In the processing of Orca, we need to share an outlier-score table and a cutoff value for pruning among worker threads. To reduce conflicts on sharing, the proposed parallelization model manages outlier-score tables hierarchically and makes the cache of the cutoff value on each worker thread. The experimental results show that the proposed parallelization model outperforms a conventional parallelization model, which utilizes only the data parallelism without managing outlier-score tables hierarchically.

关键词： Instruction sets Databases Parallel processing detection algorithms Data models Object recognition Image color analysis

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Hybrid Error Concealment with Automatic Error detection for Transmitted MPEG-2 Video Streams over Wireless Communication Network

Hybrid Error Concealment with Automatic Error Detection for ...

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International Conference on Information Technology: Research and Education (ITRE)

作者： Ofer Hadar Merav Huber Revital Huber Communication System Engineering Department Ben-Gurion University of the Negev Beersheba Israel

This work presents a complete error concealment system, for overcoming visible distortions in video sequences which are transmitted over a lossy communication network. The system we propose provides an error concealment solution from the point of receiving the transmitted sequence by the decoder, until it is presented to viewers, without human interference. The system is composed of an automatic error detection algorithm, and a decision tree error concealment algorithm. The performance of the detection algorithm is estimated, along with a performance evaluation of the decision tree algorithm by comparing it to the other three error concealment methods. The results are evaluated using two quality measures. We show that our error concealment method achieves the highest quality compared to the other methods for most of the conducted tests.

关键词： Streaming media Wireless communication detection algorithms Decision trees Video sequences Propagation losses Communication networks Decoding Humans Interference

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