Multi-beam scanning electron microscopy (MBSEM), has been developed to reduce the acquisition time by scanning multiple pixels simultaneously. The signal from the 14 x 14 beams is captured on a camera which reads out ...
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ISBN:
(纸本)9781467395557
Multi-beam scanning electron microscopy (MBSEM), has been developed to reduce the acquisition time by scanning multiple pixels simultaneously. The signal from the 14 x 14 beams is captured on a camera which reads out the position and intensity for each beam on the sample. But as we work with multiple beams and pixels we need a powerful technique for image acquisition and imageprocessingalgorithms. We use Field Programmable Gate Arrays (FPGA's), often used as an implementation platform for real time image acquisition and processing applications, because their structure is able to exploit spatial and temporal parallelism. This paper presents a technique for dealing with the various constraints of the camera and efficient mapping for image acquisition and processing operations on FPGA.
The ICCSCM 2017 (The 6th International conference on Computer Science and Computational Mathematics) has aimed to provide a platform to discuss computer science and mathematics related issues including Algebraic Geome...
The ICCSCM 2017 (The 6th International conference on Computer Science and Computational Mathematics) has aimed to provide a platform to discuss computer science and mathematics related issues including Algebraic Geometry, Algebraic Topology, Approximation Theory, Calculus of Variations, Category Theory; Homological Algebra, Coding Theory, Combinatorics, Control Theory, Cryptology, Geometry, Difference and Functional Equations, Discrete Mathematics, Dynamical systems and Ergodic Theory, Field Theory and Polynomials, Fluid Mechanics and Solid Mechanics, Fourier Analysis, Functional Analysis, Functions of a Complex Variable, Fuzzy Mathematics, Game Theory, General Algebraic systems, Graph Theory, Group Theory and Generalizations, imageprocessing, Signal processing and Tomography, Information Fusion, Integral Equations, Lattices, Algebraic Structures, Linear and Multilinear Algebra; Matrix Theory, Mathematical Biology and Other Natural Sciences, Mathematical Economics and Financial Mathematics, Mathematical Physics, Measure Theory and Integration, Neutrosophic Mathematics, Number Theory, Numerical Analysis, Operations Research, Optimization, Operator Theory, Ordinary and Partial Differential Equations, Potential Theory, Real Functions, Rings and Algebras, Statistical Mechanics, Structure Of Matter, Topological Groups, Wavelets and Wavelet Transforms, 3G/4G Network Evolutions, Ad-Hoc, Mobile, Wireless Networks and Mobile Computing, Agent Computing & Multi-Agents systems, All topics related image/Signal processing, Any topics related Computer Networks, Any topics related ISO SC-27 and SC- 17 standards, Any topics related PKI(Public Key Intrastructures), Artifial Intelligences(A.I.) & Pattern/image Recognitions, Authentication/Authorization Issues, Biometric authentication and algorithms, CDMA/GSM Communication Protocols, Combinatorics, Graph Theory, and Analysis of algorithms, Cryptography and Foundation of Computer Security, Data Base(D.B.) Management & Information Retr
Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove th...
Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove the efficiency of vision systems in autonomous robotic devices. However, the use of these systems is limited because of the computational and energy resources available in the robot device. The paper describes the results of applying the original approach for imageprocessing on reconfigurable computing environments by the example of morphological operations over grayscale images. This approach is prospective for realizing complex imageprocessingalgorithms and real-time image analysis in autonomous robotic devices.
Diabetic retinopathy (DR) is one of the most serious complications associated with chronic diabetes. High levels of sugar in blood results in long term damage of many vital organs like eyes. If untreated, diabetic ret...
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ISBN:
(纸本)9781538619599
Diabetic retinopathy (DR) is one of the most serious complications associated with chronic diabetes. High levels of sugar in blood results in long term damage of many vital organs like eyes. If untreated, diabetic retinopathy may even lead to partial or complete vision loss. With the advent of machine learning techniques in the field of imageprocessing, the severity of DR can now be estimated from eye fundus images which aids in timely appropriate treatment of the disease. In this paper we have compared the performance of different machine learning algorithms namely decision trees (bagged trees and boosted trees), weighted k-nearest neighbor (KNN), subspace discriminant analysis, and support vector machine (SVM) for the classification of eye fundus images into two classes. We have implemented binary particle swarm optimization (BPSO) to generate a reduced feature set on which we have applied our classification algorithms.
In the Amazon rainforest, fluvial monitoring involves various challenges, including an exhaustive surveillance due to its large territory and due to the fact that the largest river basin in the world is in this region...
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In the Amazon rainforest, fluvial monitoring involves various challenges, including an exhaustive surveillance due to its large territory and due to the fact that the largest river basin in the world is in this region. To cope with this problem, we propose a method focused on detecting the movement of boats in typical scenes of the Amazonian rivers. We employ digital imageprocessing techniques to detect moving objects in the scene, while machine learning algorithms are used to indicate whether or not the detected object is a boat. Experiment results show that the proposed method achieves 79% of accuracy and 91% of precision rates when all images are considered, as well as a 71% of precision rate when only scenes with boats are analyzed.
Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquistrate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detectio...
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ISBN:
(纸本)9781538620625
Compressed sensing can represent the sparse signal with a small number of measurements compared to Nyquistrate samples. Considering the high-complexity of reconstruction algorithms in CS, recently compressive detection is proposed, which performs detection directly in compressive domain without reconstruction. Different from existing work that generally considers the measurements corrupted by dense noises, this paper studies the compressive detection problem when the measurements are corrupted by both dense noises and sparse errors. The sparse errors exist in many practical systems, such as the ones affected by impulse noise or narrowband interference. We derive the theoretical performance of compressive detection when the sparse error is either deterministic or random. The theoretical results are further verified by simulations.
Since the transmission of the uncompressed image in the context of wireless visual sensor networks (WVSNs) consumes less energy than transmitting the compressed image, developing energy-aware compression algorithms ar...
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ISBN:
(纸本)9781538632703
Since the transmission of the uncompressed image in the context of wireless visual sensor networks (WVSNs) consumes less energy than transmitting the compressed image, developing energy-aware compression algorithms are mandatory to extend the camera node's lifetime and thereby the whole network lifetime. The present paper studies a low-complexity image compression algorithm in the context of WVSNs. This algorithm consists of applying a pruning approach on a DCT approximation transform. The scheme is investigated in terms of computation cycles, processing time, energy consumption and image quality. Experimental works are conducted using the Atmel Atmega128 processor of Mica2 and MicaZ sensor boards. Simulation results show that the studied scheme can exhibit a competitive performance when compared against other algorithms. Furthermore, the scheme can achieve the best tradeoff between energy consumption and image quality.
In the event of a maritime disaster, casualties need to be found and rescued promptly. imageprocessing methods could help to perform automated detection from a UAV. The main current approaches make use of multispectr...
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ISBN:
(纸本)9781509044962
In the event of a maritime disaster, casualties need to be found and rescued promptly. imageprocessing methods could help to perform automated detection from a UAV. The main current approaches make use of multispectral and thermal cameras, which can deal with lightning difficulties but are expensive and could suffer from high noise problems. This paper presents a method combining both color analysis and frequency patterns identification using an inexpensive vision camera (EO camera), and implements it through an adaptive algorithm to deal with a dynamically changing background. The method is tested successfully in different environments.
This paper presents a new method for the segmentation of glandular cavity. Our method is based on the K-means and mathematical morphology. We have segmented the entire glandular cavity and eliminated many of the inter...
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This paper presents a new method for the segmentation of glandular cavity. Our method is based on the K-means and mathematical morphology. We have segmented the entire glandular cavity and eliminated many of the interference factors in the gastric glandular image. An important characteristic of our method is that we combined the K-means with the mathematical morphological processing, and carried out the iterative execution which resulted in a gradual refinement, and the algorithm can run in nearly a linear time. images that are processed using our methods can be more effective in helping doctors diagnose disease.
We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking ar...
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ISBN:
(纸本)9781538639542
We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts. In contrast, we propose an image recovery method free of post-processing, where we utilize a lapped transform (LT) for the sparse representation of the image in order to reduce the blocking artifacts. Specifically, we derive an iterative image reconstruction method, where a small number of adjacent measurement blocks are jointly processed for recovering an image block. For this purpose, we propose a novel sparse Bayesian learning (SBL) algorithm.
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