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Satellite image retrieval of random forest (rf-PNN) based probablistic neural network

EARTH SCIENCE INFORMATICS 2022年第2期15卷 941-949页

作者： Devi, N. Bharatha Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

The image retrieval process grows a massive problem due to the huge number of data exist on the web. So, the accurate retrieval of satellite image of the given query is one of the necessary requirement. The paper propose a classifier of Probabilistic Neural Network based Random Forest (rf-PNN), which is retrieving an exact match of a classified data as per user's need. Various techniques of Adaptive Median Filter (for pre-processing), Discrete Cosine Transform based Discrete Orthogonal Stockwell Transform (for segmentation) and linear binary pattern (for feature extraction) are presented to process the trained dataset as well as given query. Then, both the feature extracted samples are assigned to compare with the classified network. The experimental setup is demonstrated on MATLAB tool. Then the relevant feature retrieval are analyze under the performance measures of 92% accurate rate, sensitivity for 89.25%, specificity for 94.1% and precision at a rate of 90.08%.

关键词： Discrete Cosine Transform based Discrete Orthogonal Stockwell Transform (DCT-DOST) linear binary pattern Adaptive median filter Discrete Cosine Stockwell Transform (DCST) rf-PNN

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Image analysis and machine learning for detecting malaria

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TRANSLATIONAL RESEARCH 2018年 194卷 36-55页

作者： Poostchi, Mahdieh Silamut, Kamolrat Maude, Richard J. Jaeger, Stefan Thoma, George NIH US Natl Lib Med Bethesda MD 20894 USA Mahidol Univ Mahidol Oxford Trop Med Res Unit Bangkok Thailand Univ Oxford Nuffield Dept Med Ctr Trop Med & Global Hlth Oxford England Harvard Univ Harvard TH Chan Sch Publ Hlth Boston MA 02115 USA

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.

关键词： GLRLM Gray Level Run Length Matrix HoG Histogram of Gradient HSV Hue Saturation Value IEEE Institute of Electrical and Electronics Engineers LBP linear binary pattern LED Light Emitting Diode NIH National Institute of Health NLM National Library of Medicine NM Nearest Mean P Plasmodium PCR Polymerase Chain Reaction PLOS Public Library of Science QFT Quaternion Fourier Transform QPI Quantitative Phase Imaging RDT Rapid Diagnostic Test RGB Red Green Blue RNA RiboNucleic Acid SBFSEM Serial Block-Face Scanning Electron Microscopy SEM Scanning Electron Microscope SightDx Sight Diagnostics SROFM Sub-pixel Resolving Optofluidic Microscope SUSAN Smallest Univalue Segment Assimilating Nucleus SVM Support Vector Machine WHO World Health Organization

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Privacy Preserving LBP Based Feature Extraction on Encrypted Images 7

Privacy Preserving LBP Based Feature Extraction on Encrypted...

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7th International Conference on Computer Communication and Informatics (ICCCI)

作者： Sultana, Sayyada Fahmeeda Shubhangi, D. C. PDA Coll Engn CSE Dept Kalaburgi India VTU PG Ctr Dept CSE Kalaburgi India

ISBN: (纸本)9781467388566;9781467388559

"An Image is worth a thousand words." To provide security images always encrypted and stored in the cloud. Along with it, there is also a continuous need to outsource image computation with high complexity to the cloud for its economic computing resources and on-demand ubiquitous access. Feature extraction and representation on encrypted images is a crucial step for multimedia processing on the cloud. Extraction of ideal features from encrypted images without revealing the intrinsic content of the images is privacy preservation. In this paper, we propose an efficient scheme PP-LBP (Privacy preserving - Local binary patterns) that retrieves LBP based image features from encrypted images. In the Proposed approach, the encryption algorithm is applied on MSB (Most Significant Bit) plane of Image. All the operations are performed on encrypted images without revealing any information to cloud service providers. The method generates exactly same LBP based feature between encrypted and unencrypted images without any overhead of communication between cloud service provider and client. All the Computational overheads complexities are on cloud not to client.

关键词： linear binary pattern Privacy Preserving-linear binary pattern Feature Extraction Privacy Preserved Security MSB

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Emotion Recognition: A Step Ahead of Traditional Approaches 3rd

Emotion Recognition: A Step Ahead of Traditional Approaches

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3rd International Conference on Information System Design and Intelligent Applications (INDIA)

作者： Agarwal, Surbhi Madhurima, Madhulika Azamgarh India

ISBN: (纸本)9788132227526;9788132227502

Emotion recognition is an intriguing issue these days. It affects essential applications in numerous regions for example surveillance, defense, financial services etc. Determining a particular expression from face images effectively is a crucial venture. In this paper, we have demonstrated a novel approach to recognize emotions displayed in video sequences. The authors have considered seven basic emotions measuring factors: anger, fear, disgust, happiness, sadness, surprise and neutral. These factors are constantly encountered in our day to day life. The focus of this paper is towards contemplates a combination of extended biogeography based optimization algorithm, support vector machines and local binary patterns to obtain the best possible results.

关键词： Support vector machine Biometrics Feature extraction Viola jones linear binary pattern Extended

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Evaluation of Textural Features for Multispectral Images

Evaluation of Textural Features for Multispectral Images

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Conference on Image and Signal Processing for Remote Sensing XVII

作者： Bayram, Ulya Can, Gulcan Duzgun, Sebnem Yalabik, Nese C3S Ltd Command Control & Cybernet Syst ODTU Teknokent Ankara Turkey Middle East Tech Univ Dept Comp Engn Ankara Turkey Middle East Tech Univ Dept Mining Engn Geodet & Geograph Inf Tech Ankara Turkey Yalabik Engn Co ODTU Ankara Turkey

ISBN: (纸本)9780819488077

Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.

关键词： Gray level co-occurrence matrix histogram of oriented gradients Gabor feature linear binary pattern color histogram diffusion distance textural features remote sensing

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Robust vehicle detection in aerial images based on salient region selection and superpixel classification

Robust vehicle detection in aerial images based on salient r...

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Conference on Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII

作者： Sahli, Samir Duval, Pierre-Luc Sheng, Yunlong Lavigne, Daniel A. Univ Laval Ctr Opt Photon & Laser Ste Foy PQ G1K 7P4 Canada Def Res & Dev Canada Quebec City PQ G3J 1X5 Canada

ISBN: (纸本)9780819485946

For detecting vehicles in large scale aerial images we first used a non-parametric method proposed recently by Rosin to define the regions of interest, where the vehicles appear with dense edges. The saliency map is a sum of distance transforms (DT) of a set of edges maps, which are obtained by a threshold decomposition of the gradient image with a set of thresholds. A binary mask for highlighting the regions of interest is then obtained by a moment-preserving thresholding of the normalized saliency map. Secondly, the regions of interest were over-segmented by the SLIC superpixels proposed recently by Achanta et al. to cluster pixels into the color constancy sub-regions. In the aerial images of 11.2 cm/pixel resolution, the vehicles in general do not exceed 20 x 40 pixels. We introduced a size constraint to guarantee no superpixels exceed the size of a vehicle. The superpixels were then classified to vehicle or non-vehicle by the Support Vector Machine (SVM), in which the Scale Invariant Feature Transform (SIFT) features and the linear binary pattern (LBP) texture features were used. Both features were extracted at two scales with two size patches. The small patches capture local structures and the larger patches include the neighborhood information. Preliminary results show a significant gain in the detection. The vehicles were detected with a dense concentration of the vehicle-class superpixels. Even dark color cars were successfully detected. A validation process will follow to reduce the presence of isolated false alarms in the background.

关键词： Vehicle detection aerial image SVMs target detection salient regions superpixels over-segmentation SIFT linear binary pattern

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