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Mapping stochastic Collective Behavior Distinguishes Subtle Mutations in Social Bacteria

PRX Life 2023年第2期1卷 023011-023011页

作者： Merrill E. Asp Eduardo A. Caro Roy D. Welch Alison E. Patteson BioInspired Institute Syracuse University Syracuse New York 13244 USA Physics Department Syracuse University Syracuse New York 13244 USA Biology Department Syracuse University Syracuse New York 13244 USA

The genotype-to-phenotype problem (G2P) for multicellular development asks how genetic inputs control collective phenotypic outputs. However, this is a challenging problem due to gene redundancy and stochasticity, causing mutations to have subtle phenotypic effects and replicates to display significant variation. We approach this problem using the model organism Myxococcus xanthus, a motile self-organizing bacterium that forms three-dimensional cell aggregates that mature into spore-filled fruiting bodies when under starvation stress. We develop a high-throughput imaging method using three-dimensional-printed microscopes to efficiently collect large phenotypic datasets. Our automated methods for analysis and visualization produce a map of phenotypic variation in M. xanthus development. We demonstrate that even subtle effects on developmental dynamics caused by mutation can be identified, discriminated, characterized, and given statistical significance, with implications for future gene annotation studies and the effect of environmental factors on G2P.

关键词： Developmental biology image processing Bacteria Biofilms

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Hyperspectral Anomaly Detection based on Frequency Analysis of Repeated Spatial Patterns

Hyperspectral Anomaly Detection based on Frequency Analysis ...

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Iranian Machine Vision and image processing (MVIP)

作者： Ashkan Taghipour Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168333

Hyperspectral anomaly detection has brought significant attention to researchers during the last decade. In the remote sensing domain, it has lots of applications like environmental monitoring, mine source detection, human safety and, etc. Different methods have been proposed to overcome the challenges of anomaly detection, which mainly are a highly accurate detection rate and also having low false alarm rates. Background complexity of the hyperspectral data causes false detection of pixels as anomalies, and several statistical and sparse based methods try to solve it. However, the computation burden of the statistical and sparse based methods make them unpractical. Also, they often do not utilize the spatial information of hyperspectral data for increasing the detection accuracy. In this paper, we proposed a frequency analysis based approach that suppresses the repeatable patterns of the scene which mainly represent the background and considers the remainder regions as anomalies. Owe to the Fourier analysis advantage, it fast and accurate detects anomalous regions. Two well-known data sets have been chosen to challenge the potency of the proposed method, and the results are compared with some state-of-the-art methods. The visual and quantitative results confirm the supremacy of the proposed method to competitors in computation complexity and detection accuracy point of view.

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Sorting Pixels based Face Recognition Using Discrete Wavelet Transform and statistical Features 3

Sorting Pixels based Face Recognition Using Discrete Wavelet...

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3rd International conference on Imaging, Signal processing and Communication (ICISPC)

作者： Divya, A. Raja, K. B. Venugopal, K. R. Bangalore Univ Univ Visvesvaraya Coll Engn Dept ECE Bangalore Karnataka India Bangalore Univ Bangalore Karnataka India

ISBN: (纸本)9781728136639

Face recognition is used to identity a person effectively and most effective physiological biometric trait. In this paper, we propose sorting pixels-based face recognition using Discrete Wavelet Transform (DWT) and statistical features. The novel concept of sorting pixel values in ascending order is introduced and segmented into two parts viz., Low Pixel Values (LPV) and High Pixel Values (HPV). The DWT is applied on LPV matrix to generate low and high frequency bands such as LL, LH, HL and HH. The low frequency LL band is considered for features as the coefficient values are enhanced compared to original image pixel values and also reduction in dimensionality. The statistical measure is applied on HPV to compute mean, median, mode, maximum and standard deviation features. The features of LL band and statistical features are concatenated to obtain final features. The Artificial Neural Network (ANN) is used as classifier to recognize human beings. It is perceived that the performance of the proposed method is enhanced compared with the existing methods.

关键词： Biometrics face recognition discrete wavelet transform statistical measures artificial neural network

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Light field image Compression Using Depth-based CNN in Intra Prediction 44

Light field Image Compression Using Depth-based CNN in Intra...

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44th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhong, Tingting Jin, Xin Li, Lingjun Dai, Qionghai Collaborative Innovation Center for Geospatial Technology Wuhan University Wuhan China

ISBN: (纸本)9781479981311

Recently, light field images have received extensive attention due to their potential applications. Since they take up a huge memory because of its super-high resolution, efficient compression methods are fundamentally required. In this paper, we propose a novel intra prediction mode by using depth-adaptive convolutional neuro network (DCNN). Light field projection finds the imaging response distribution for each object point using the depth estimated from each macropixel in the light field image. The highly correlated imaging responses are used to select the neural network structure. The network structure also adapts to the to-be-encoded block size. Adding the proposed DCNN-based prediction mode into the rate-distortion optimization loop with other 35 intra prediction modes of HEVC, the proposed encoding scheme achieves a significant bit-rate saving compared to representative compression approaches with limited computational complexity increment. statistical data are also provided and analyzed to demonstrate the efficiency of the proposed method.

关键词： Light field image compression intra prediction depth-adaptive convolutional neuro network

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Optimization of the processing of images of pollen grains based on the use of their specific characteristics and geometric features

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AIP conference Proceedings 2022年第1期2637卷

作者： Isroil Jumanov Rustam Safarov 1Samarkand State University University Boulevard 15 Samarkand 1400000 Uzbekistan

methods have been developed to increase the accuracy of processing of images of pollen grains based on mechanisms for extracting statistical, dynamic, texture and specific characteristics, as well as geometric features of micro-objects. A technique is proposed to increase the accuracy of information processing by the metric characteristics of the contour points of the input and reference images. methods have been developed for point and nonlinear verification of the correspondence of the contours of the input and reference objects based on the mechanisms of isolating, segmenting, interpolating, contrasting, extracting specific characteristics of pollen grains such as frequency, cytoplasm, reticular, spore, texture, morphology of the object and other geometric features of raster images. A set of information processing methods with mechanisms for reducing the zero points of the image contour, reducing the size of rasters, scaling, threshold and level control of dynamic parameters during image recognition and classification, adjusting the points of the color and brightness picture parameters, fixing the initial values, segment centroid and identification are proposed and implemented. Implemented a computer complex of micro objects view in C ++ in the parallel computing environment “CUDA”.

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CT image Reconstruction using Fourth-order PDE based AD Regularization 5

CT Image Reconstruction using Fourth-order PDE based AD Regu...

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5th International conference on image Information processing (ICIIP)

作者： Pathak, Yadunath Agarwal, Dheeraj Tiwari, Shailendra Khurana, Manju IIIT Dept Comp Sci & Engn Bhopal 462003 India MANIT Elect & Commun Engn ECE Dept Bhopal 462003 India TIET Comp Sci & Engn Dept CSED Patiala 147004 Punjab India

ISBN: (纸本)9781728108995

One-Step-Late (OSL) statistical iterative algorithm plays an important role in Computed Tomography (CT) image reconstruction. But the fundamental problem related to OSL is the optimum initial value condition, slow convergence, and ill-posed. To resolves these problems, we present a modified OSL algorithm. The issue of optimum initial value condition and slow convergence can handle by integrating the Simultaneous Algebraic Reconstruction Technique (SART) with OSL called as modified OSL (SART+OSL). The output of modified OSL undertakes in Fourth order partial differential equation (PDE) based Anisotropic Diffusion regularization approach to deal with an ill-posed. It is an extended version of the Perona-Malik (P-M) filter. For validation of the proposed model, both simulated and real standard thorax phantoms have been used. Finally, the results were compared with the related state-of-the- art methods. It is observed that the proposed model has many desirable advantages such as noise reduction, minimize the computational cost, as well as accelerate the convergence rate.

关键词： One-Step-Late Fourth order PDE Anisotropic Diffusion ill-posed Simultaneous Algebraic Reconstruction Technique Regularization term

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Joint statistical and Spatial Sparse Representation for Robust image and image-Set Classification

Joint Statistical and Spatial Sparse Representation for Robu...

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IEEE International conference on image processing

作者： Hao Cheng Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recent image classification schemes, by learning deep features from large-scale dataset, have achieved the significantly better results comparing to classic feature-based approaches. However, there are still challenges in practice, such as classifying noisy image-set queries and training over limited-scale dataset. Instead of applying generic deep features, the model-based approaches can be more effective for robust image and image-set classification tasks, as we need various image priors to exploit the inter- and intra-set data variations while prevent over-fitting. In this work, we propose a novel joint statistical and spatial sparse representation, dubbed J3S, to model the image or image-set data, by exploiting both their local patch structures and global Gaussian distribution into Riemannian manifold. To the best of our knowledge, no work to date utilized both global statistics and local patch structures jointly via sparse representation. We propose to solve a co-regularized sparse coding problem based on the J3S model, by coupling the local and global representations using joint sparsity. The learned J3S models are used for robust image and image-set classification. Experiments show that the proposed J3S-based image classification scheme outperforms the popular or state-of-the-art competing methods.

关键词： Manifolds Dictionaries Robustness Encoding Data models Training Task analysis

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Momentum centering and asynchronous update for adaptive gradient methods 21

Momentum centering and asynchronous update for adaptive grad...

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Proceedings of the 35th International conference on Neural Information processing Systems

作者： Juntang Zhuang Yifan Ding Tommy Tang Nicha Dvornek Sekhar Tatikonda James S. Duncan Yale University University of Central Florida University of Illinois at Urbana-Champaign

ISBN: (纸本)9781713845393

We propose ACProp (Asynchronous-centering-Prop), an adaptive optimizer which combines centering of second momentum and asynchronous update (e.g. for t-th update, denominator uses information up to step t - 1, while numerator uses gradient at t-th step). ACProp has both strong theoretical properties and empirical performance. With the example by Reddi et al. (2018), we show that asynchronous optimizers (e.g. AdaShift, ACProp) have weaker convergence condition than synchronous optimizers (e.g. Adam, RMSProp, AdaBelief); within asynchronous optimizers, we show that centering of second momentum further weakens the convergence condition. We demonstrate that ACProp has a convergence rate of $O(\frac{1}{\sqrt{T}})$ for the stochastic non-convex case, which matches the oracle rate and outperforms the $O(\frac{logT}{\sqrt{T}})$ rate of RMSProp and Adam. We validate ACProp in extensive empirical studies: ACProp outperforms both SGD and other adaptive optimizers in image classification with CNN, and outperforms well-tuned adaptive optimizers in the training of various GAN models, reinforcement learning and transformers. To sum up, ACProp has good theoretical properties including weak convergence condition and optimal convergence rate, and strong empirical performance including good generalization like SGD and training stability like Adam.

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The Research Context, Latest Hotspots And Evolution Trend Of Global Robots–Based On Visual Analysis Of Citespace

The Research Context, Latest Hotspots And Evolution Trend Of...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Yu Zhang College of Information & Technology Zhejiang University of Technology Hangzhou China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

Theoretical research on robots and the development of key technologies is one of the current research hotspots, adopting Citespace as the visualization tool, and with the target literatures of the WOS database as the research object to carry out statistical analysis and content analysis on the research history of robots. The system sorts out the spatial and temporal distribution, knowledge groups, subject structures and hotspot fields, latest researches and evolutionary trends of global robotics research, combined with the nation's great demands and the latest development of discipline, sets forth the research hotspots and development trends on current robotics, and draws many objective conclusions based on the facts, as well as several challenge problems that need to be solved in hurry in the basic theory and key technologies of the robots are extracted. Points out the current research foundations and deficiencies in this field, and put out direction proposal of the key points in this field for the next 5 to 10 years, which provides theoretical support and practical guidance for the research direction of future robots.

关键词： robot Citespace latest hotspots research context Robot Visual analytics Key technology Fields robotics statistical methods statistical analysis current research Content analysis

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K-fold cross-validation of multiple-point statistical simulations 4

K-fold cross-validation of multiple-point statistical simula...

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4th EAGE conference on Petroleum Geostatistics

作者： Juda, P. Renard, P. Straubhaar, J. University of Neuchatel

ISBN: (纸本)9789462822962

In reservoir models, the choice of spatial interpolation or stochastic simulation methods for subsurface properties is crucial when dealing with heterogeneous media. Multiple-point statistics (MPS) algorithms allow to simulate complex structures but they are controlled by hyper-parameters whose identification can be tedious. Furthermore, many different geostatistical methods and models are available. In this work, we present an application of K-fold cross-validation for the selection of a spatial simulation method. The proposed technique allows to rank models based on their predictive accuracy and is completely generic: it can handle categorical and continuous variables, as well as compare MPS algorithms to variogram-based, or object based models. It can be used for the selection of any type of parameters, including the choice of the training image. We demonstrate the performance of the method on a synthetic test case used previously for benchmarking training image selection techniques and on a real field application including non-stationarity. © EAGE 2019.

关键词： Benchmarking

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