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3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts

3D volume extraction of densely packed cells in EM data stac...

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ieee symposium on computational intelligence for Multimedia signal and Vision processing, CIMSVP

作者： Huei-Fang Yang Yoonsuck Choe Department of Computer Science and Engineering Texas A and M University College Station TX USA

3D reconstruction on dense nanoscale medical images is a very challenging research topic. The challenge comes from the fact that boundaries of objects on such images are not always very clear due to imperfect staining. This makes the segmentation of dense nanoscale medical images very difficult and thus increases the difficulty in 3D reconstruction. In this paper, we proposed a method based on watershed and an interactive segmentation technique, graph cuts, to extract 3D volumes from dense nanoscale medical images. In our method, images are first segmented by a marker-controlled watershed algorithm. Markers for watershed segmentation algorithm are seed points generated by using distance transform, followed by a new grouping method that clusters seed points that are too close. Regions obtained by watershed transform segmentation algorithms are considered as nodes in a graph. Edges are to connect between the nodes in adjacent image slices. The weight on each edge is defined based on the overlapped area between nodes. User-selected nodes (regions) in an initial image slice serve as hard constraints in the minimization process. A globally optimal 3D volume is obtained by minimizing MAP-MRF energy function via graph cuts. In our application, in order to obtain a complete 3D volume structures including branching, the final 3D volume is the union of two 3D volumes obtained by performing the minimization of MAP-MRF energy function using graph cuts forwards and backwards through the image stack. Experiments are conducted both on synthetic data and on nanoscale image sequences from the serial block face scanning electron microscope (SBF-SEM). The results show that our method can successfully extract 3D volumes.

关键词： Data mining image segmentation Biomedical imaging image reconstruction Clustering algorithms Gray-scale Scanning electron microscopy image generation image resolution Optical noise

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Color image cryptosystem using chaotic maps

Color image cryptosystem using chaotic maps

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ieee symposium on computational intelligence for Multimedia, signal and Vision processing (CIMSIVP)

作者： Sahar Mazloom Amir-Masud Eftekhari-Moghadam Faculty of Electrical Computer and IT Engineering Qazvin Islamic Azad University Qazvin Iran

image encryption is somehow different from text encryption due to some inherent features of image such as bulk data capacity and high correlation among pixels, which are generally difficult to handle by traditional methods. The exceptionally desirable properties of the chaotic maps such as sensitivity to initial conditions and random-like behavior have attracted the attention of cryptographers to develop new encryption algorithms. This paper proposes a new symmetric image cipher based on the widely used confusion-diffusion architecture which utilizes the chaotic 2D Standard map and 1D Logistic map. It is specifically designed for the color images, which are 3D arrays of data streams. We prefer chaotic Standard map to Baker and Cat maps since the key space of the chaotic Standard map is large enough as compared to the Baker and Cat maps, which makes the brute-force attack infeasible. The initial conditions and system parameters of the chaotic maps constitute the secret key of the algorithm. To further enhance the security, the control parameters used in the confusion stage and the keystream employed for diffusion stage are distinct in different rounds and related to the plain-image. These control parameters are generated by a Tent map. For getting higher security and higher complexity, the current scheme employs two kinds of diffusion processes namely the horizontal and vertical diffusions which are completed by mixing the properties of horizontally and vertically adjacent pixels using a Logistic map, respectively. The results of several experiments, including the most important ones like key space analysis, key sensitivity test, statistical analysis, and visual test by histograms of encrypted images, the correlation coefficients of adjacent pixels, and differential analysis, demonstrate the satisfactory security and efficiency of the proposed image encryption scheme for real-time image encryption and transmission.

关键词： Pixel Encryption Chaotic communication Logistics

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Neuro-fuzzy filters based on recursive processing and genetic learning

Neuro-fuzzy filters based on recursive processing and geneti...

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International symposium on image and signal processing and Analysis

作者： F. Russo D.E.E.I University of Trieste Italy

ISBN: (纸本)9539676940

Neuro-fuzzy filters based on genetic learning are a recently introduced class of nonlinear operators that aim at exploiting the powerful paradigms of computational intelligence. These filters adopt fuzzy reasoning to model the noise removal process and then perform an effective noise cancellation without blurring the image details. In this paper, we focus on the latest generation of neuro-fuzzy filters that adopt a multiple-output architecture. These filters are composed of several subnetworks that process different subsets of input data adopting a serial or a parallel approach. Since the filtering action is recursive, even different processing strategies can be combined in the same filtering architecture. As a result, the most appropriate filtering behavior can be learned from a set of training data.

关键词： Filters Filtering Noise cancellation Fuzzy systems computational intelligence Training data Artificial neural networks Genetic algorithms Humans Fuzzy sets

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A Multi-Channel Representation for images on quantum computers using the RGBα color space

A Multi-Channel Representation for images on quantum compute...

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ieee International symposium on Intelligent signal processing

作者： Bo Sun Phuc Q. Le Abdullah M. Iliyasu Fei Yan J. Adrian Garcia Fangyan Dong Kaoru Hirota Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Tokyo Japan

A Multi-Channel Representation for Quantum image (MCRQI) is proposed to facilitate the further image processing tasks based on the Flexible Representation for Quantum image (FRQI). Channel Swapping Operation, One Channel Operation, are proposed as basic image processing operations on MCRQI representation. The simulation experiment results on classical computer show that the channel information of R G B (color) and α (transparency) can be carried easily on quantum computer by employing three qubits to represent the color space, and also indicate that this MCRQI is very flexible to realize some classic-like operations. The MCRQI provides a foundation not only express image in RGBα color space, but also to explore theoretical and practical aspects of image processing on quantum computer.

关键词： image color analysis Quantum computing Logic gates Computers Bismuth Transforms

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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2022 International Applied computational Electromagnetics Society symposium, ACES-China 2022

作者： Chen, Jiansong Zuo, Shikai Lin, Yun Tang, Kai Zou, Jianhang Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning. © 2022 ieee.

关键词： Classification (of information)

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Tutorial CICA-T Computing with intelligence for identification and control of nonlinear systems

Tutorial CICA-T Computing with intelligence for identificati...

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ieee symposium on computational intelligence in Control and Automation, CICA

作者： Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology USA

System characterization and identification are fundamental problems in systems theory and play a major role in the design of controllers. System identification and nonlinear control has been proposed and implemented using intelligent systems such as neural networks, fuzzy logic, reinforcement learning, artificial immune system and many others using inverse models, direct/indirect adaptive, or cloning a linear controller. Adaptive Critic Designs (ACDs) are neural networks capable of optimization over time under conditions of noise and uncertainty. The ACD technique develops optimal control laws using two networks - critic and action. There are merits for each approach adopted will be presented. The primary aim of this tutorial is to provide control and system engineers/researchers from industry/academia, new to the field of computational intelligence with the fundamentals required to benefit from and contribute to the rapidly growing field of computational intelligence and its real world applications, including identification and control of power and energy systems, unmanned vehicle navigation, signal and image processing, and evolvable and adaptive hardware systems.

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Robot navigation using biosonar for natural landmark tracking

Robot navigation using biosonar for natural landmark trackin...

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ieee International symposium on computational intelligence in Robotics and Automation (CIRA)

作者： Maosen Wang Hashem Tamimi A. Zell Computer Science Department University of Tübingen Tubingen Germany

A biosonar based mobile robot navigation system is presented for the natural landmark classification using acoustic image matching. The aim of this approach is to take advantage of the perceived properties of bats' prey and landmark identification mechanisms for mobile robots' tracking of natural landmarks. Recognizing natural landmarks like trees through sequential echolocation and acoustic image analyzing allows mobile robot to update its location in the natural environment. In this work, a working implementation of the biosonar system on a mobile robot is shown. It collects sequential echoes to produce acoustic images through digital signal processing (DSP), then compresses images with discrete cosine transform or pyramid algorithm. Fast normalized cross correlation (FNCC) and kernel principal component analysis (KPCA) are respectively used to make the final classification. Experimental result indicates that a mobile robot can achieve the ability of natural landmark classification only based on biomemetic sonar, the topological congruency of the relational structure with cross correlation in acoustic images is reliable in time domain, while the kernel principle component analysis based classification is robust in frequency domain and demands fewer echolocation for landmark classification.

关键词： Navigation Mobile robots image analysis Digital signal processing Kernel image matching Mechanical factors image recognition image coding Discrete cosine transforms

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On Improved MRI Segmentation Using Hierarchical computational intelligence Techniques and Textural Analysis of the Discrete Wavelet Transform Domain

On Improved MRI Segmentation Using Hierarchical Computationa...

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ieee International symposium on Intelligent signal processing

作者： D. A. Karras Chalkis Institute of Technology Automation Department Hellenic Open University Athens Greece

ISBN: (纸本)1424408296;97

This paper investigates a novel feature extraction approach to MRI segmentation based on identifying the critical image edges by using textural (cooccurrence matrices) analysis of the discrete wavelet transform (DWT) domain. Furthermore, the presented approach is based on formulating the problem as a two-stage unsupervised classification task using a modified Kohonen's self organizing feature map (SOFM) along with independent component analysis (ICA). The main goal of such a research effort is to better identify abrupt textural image changes without increasing the presence of noise in the resulting image. The suggested methodology is based on novel discrete wavelet descriptors involving the discrete k-level 2-D wavelet transform and cooccurrence matrices analysis applied to sliding windows raster scanning the original image. The proposed two-stage classification scheme applied to such textural wavelet descriptors and using a modified vector quantizing self-organizing feature map (SOFM) and ICA analysis is compared with a corresponding two-stage scheme involving PCA analysis and the widely used SOFM, trained with Kohonen's algorithm. The feasibility of this novel two-stage proposed approach is studied by applying it to the edge structure segmentation problem of brain slice MRI images. The promising results presented in the experimental study illustrate a performance favourably compared, also, to that of traditional Sobel edge detectors supported by usual contour tracing methods.

关键词： Discrete wavelet transforms Magnetic resonance imaging computational intelligence Wavelet analysis Wavelet domain Independent component analysis image texture analysis image segmentation image edge detection image analysis

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Selective Bayesian estimation for efficient super-resolution

Selective Bayesian estimation for efficient super-resolution

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ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Z.A. Ivanovski L.J. Karam G.P. Abousleman Department of Electrical Engineering Arizona State University Tempe AZ USA Compression Communications & Intelligence Laboratory General Dynamics C4 Systems Scottsdale AZ USA

In this paper, a new approach to efficient and robust super-resolution is presented. Our method is based on selectively applying a Bayesian MAP estimator to image regions with high spatial activity. The degree of spatial activity is measured using the gradient of the estimated high-resolution image at each iteration. In addition, selective filtering is applied to enhance the visual quality of the estimated high-resolution image. The results obtained via simulation and with real video sequences demonstrate up to a 50% reduction in computational complexity, with improved visual quality, and higher SNR gains for magnification factors of four or more.

关键词： Bayesian methods Strontium Spatial resolution Interpolation Maximum likelihood estimation Video sequences image registration Pixel State estimation Robustness

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Combined image signal processing for CMOS image Sensors

Combined Image Signal Processing for CMOS Image Sensors

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Proceedings of 2006 ieee International symposium on Circuits and Systems,Volume 7

作者： Kimo Kim In-Cheol Park System LSI Division Samsung Electronics Company Limited South Korea Department of Electrical Engineering and Computer Science KAIST South Korea

This paper presents an efficient image signal processing structure for CMOS image sensors to achieve low area and power consumption. Although CMOS image sensors (CISs) have various benefits compared with charge-coupled devices (CCDs), the images obtained from CISs have much lower quality than those from CCDs. To improve the quality of CIS images, it is required to do reproducing and enhancing processings such as color interpolation, white balancing, color correction, gamma correction and color conversion. They are implemented individually in most conventional designs though they have similar functional characteristics. In this proposed structure, the gamma correction block is moved to the front in order to combine several image signal processings into one block. An efficient compensation scheme is also proposed to reduce the errors caused by the moving of the non-linear gamma correction. A prototype CIS image signal processor is implemented in Verilog-HDL and synthesized with 0.18μm standard cell library. Experimental results show that the proposed structure reduces area and power consumption by 23.8% and 31.1%, respectively.

关键词： signal processing CMOS image sensors Color Energy consumption computational intelligence Society Interpolation image converters Error correction Prototypes Hardware design languages

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