Robots have become an integral part in industry as well as domestic use. The future of mankind cannot be seen without robots. Artificial Intelligence is core of any autonomous robot, and it should be capable to recogn...
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ISBN:
(纸本)9781538677100
Robots have become an integral part in industry as well as domestic use. The future of mankind cannot be seen without robots. Artificial Intelligence is core of any autonomous robot, and it should be capable to recognize an object or human by the vision provided to it. Representations of an object or human are complex functions and various algorithms are being implemented to analyze these complex functions. This review paper is a survey of different conventional techniques used to detect an object in an image. The paper commences with basics of imageprocessingalgorithms for object detection and summarizes the findings based on the research and experimental results of different researchers.
Cellular Automata (CA) theory is a discrete model that represents the state of each of its cells from a finite set of possible values which evolve in time according to a pre-defined set of transition rules. CA have be...
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ISBN:
(纸本)9781538610466
Cellular Automata (CA) theory is a discrete model that represents the state of each of its cells from a finite set of possible values which evolve in time according to a pre-defined set of transition rules. CA have been applied to a number of imageprocessing tasks such as Convex Hull Detection, image Denoising etc. but mostly under the limitation of restricting the input to binary images. In general, a gray-scale image may be converted to a number of different binary images which are finally recombined after CA operations on each of them individually. We have developed a multinomial regression based weighed summation method to recombine binary images for better performance of CA based imageprocessingalgorithms. The recombination algorithm is tested for the specific case of denoising Salt and Pepper Noise to test against standard benchmark algorithms such as the Median Filter for various images and noise levels. The results indicate several interesting invariances in the application of the CA, such as the particular noise realization and the choice of sub-sampling of pixels to determine recombination weights. Additionally, it appears that simpler algorithms for weight optimization which seek local minima work as effectively as those that seek global minima such as Simulated Annealing.
We propose a GPU-based approach to accelerate filtered backprojection (FBP)-type computed tomography (CT) algorithms by adaptively reconstructing only relevant regions of the object at full resolution. In industrial a...
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We propose a GPU-based approach to accelerate filtered backprojection (FBP)-type computed tomography (CT) algorithms by adaptively reconstructing only relevant regions of the object at full resolution. In industrial applications, the object's insensitivity to radiation as well as lack of inner motion allow for high-resolution scans. The large amounts of recorded data, however, pose serious challenges as the computational cost of CT reconstruction scales quartically with resolution. To ensure real-time reconstruction (i.e. faster processing than projection acquisition) for high-resolution scans, our method skips below-threshold voxels and monotonous regions inside the object. Our approach is able to speed up the reconstruction process by a factor of up to 13 while simultaneously reducing memory requirements by a factor of up to 71.
A new method for personal identification based on geometric invariant moment is presented. Firstly imageprocessing including binary processing and segment of hand silhouette are used, and then translation and scale n...
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A new method for personal identification based on geometric invariant moment is presented. Firstly imageprocessing including binary processing and segment of hand silhouette are used, and then translation and scale normalization algorithms are implemented on the palms and fingers image. After that the geometric moment characteristics of image are extract, and then the feature vectors composed of seven moment invariants is obtained. At last, support vector is achieved by training 100 images data in images database, 15 testing image were selected randomly to verify validity and feasibility of algorithms. Experimental results indicate that the accuracy of hand shape identification is 93%. The new method of extracting hand shape geometric moment as characteristic matrix is easy to realize with characteristic of high utility and accuracy, and solve the problem of translation, rotation and scaling during the image acquisition process without positioning aids, and especially for development and application of the portable embedded devices.
Hyperspectral image classification is one of the most important techniques for analyzing hyperspectral image that have hundreds of spectrum luminance values. For this classification, supervised learning methods are wi...
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ISBN:
(纸本)9781728102153
Hyperspectral image classification is one of the most important techniques for analyzing hyperspectral image that have hundreds of spectrum luminance values. For this classification, supervised learning methods are widely used, but in general, they have a trade-off between their accuracy and computational complexity. In this paper, we propose an FPGA implementation of hyperspectral image classification based on a composite kernel method. Because of the large size of hyperspectral images, the data mapping becomes the most critical issue for achieving higher processing speed. Two data mapping approaches are discussed, and one of them that is most suitable for our target images is implemented on an FPGA. Its processing speed for 145×145 pixel images is fast enough for real-time processing, and its accuracy is comparable with other classification algorithms.
Photoacoustic tomography is a quickly growing imaging method that can provide images of high spatial resolution and high contrast at a limited depths. Medical photoacoustic processing characteristics two main componen...
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ISBN:
(纸本)9781538650431;9781538662175
Photoacoustic tomography is a quickly growing imaging method that can provide images of high spatial resolution and high contrast at a limited depths. Medical photoacoustic processing characteristics two main components: A transducer is required to transmit laser pulses and acquire the reflected ultrasound signals and a back-end processing system that will generate the final reconstructed image. In this paper, we introduce an implementation of the receive part of proposed embedded system and briefly discuss reconstruction algorithms which are used in medical imaging systems. Furthermore, an intellectual property core (IP-core), which can be controlled and configured by a user application on Zynq-7000 System-On-Chip (SoC) via AXI-Lite Interface, that can receive multichannel digitized raw signals from Analog-Front-End (AFE) device via Low Voltage Differential Signal (LVDS), is proposed for photoacoustic imaging systems. Besides, block diagram of the system, the hardware design flow and the proposed IP-core are fully described in this paper. In order to effortlessly test and evaluate a wide variety of ultrasonic signal processing applications, 16 channel system is implemented and demonstrated by using TI AFE5816 Evaluation module (EVM) based on AFE5816 device and Xilinx ZC702 Evaluation Kit based on Zynq-7000 SoC. Apart from working on hardware, we review and commented on the proposed 3-Dimensional photoacoustic image reconstruction algorithm.
In this work we propose a fully end-to-end approach for multi-spectral image registration and fusion. Our fusion method combines images from different spectral channels into a single fused image using approaches for l...
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In this work we propose a fully end-to-end approach for multi-spectral image registration and fusion. Our fusion method combines images from different spectral channels into a single fused image using approaches for low and high frequency signals. A prerequisite of fusion is the geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel might prove inaccurate on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment allowing us to further fuse the images. It is experimentally shown to produce a high quality of multi-spectral image registration and fusion under challenging scenarios.
In bridge health monitoring, the detection and localization of surface defects are highly important for health condition evaluation. Due to the limitation of manual detection, it is easier to measure those defects in ...
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ISBN:
(纸本)9781538670774;9781538670767
In bridge health monitoring, the detection and localization of surface defects are highly important for health condition evaluation. Due to the limitation of manual detection, it is easier to measure those defects in a more automatic way. Machine learning is a hot topic in the recent decade, and the contribution of Artificial Neural Network (ANN) is especially remarkable, which is the most widely used models of machine learning in the image-processing field. In this paper, we will discuss two ANN-based algorithms (Back propagation (BP) and Self-Organizing Maps (SOM)) and their applications for the recognition of surface defect on images taken from bridges. Moreover, a combined network algorithm with BP and SOM is designed in order to improve the performance in crack image segmentation, and analysis over this network is carried out specifically.
This paper is devoted to present technique of the use of imageprocessing for lab-on-a-chip techniques. algorithms and methods for cell detecting, obtaining their parameters and multiparametric cell tracking in lab-on...
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ISBN:
(纸本)9788363578121
This paper is devoted to present technique of the use of imageprocessing for lab-on-a-chip techniques. algorithms and methods for cell detecting, obtaining their parameters and multiparametric cell tracking in lab-on-a-chip were presented and discussed from the point of real-time detection.
A recent and fascinating interest in computational symmetry for computer vision and computer graphics applications has led to a remarkable realization of new symmetry detection algorithms. Such a concern is culminated...
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ISBN:
(纸本)9781509063444
A recent and fascinating interest in computational symmetry for computer vision and computer graphics applications has led to a remarkable realization of new symmetry detection algorithms. Such a concern is culminated in a symmetry detection competition as a workshop affiliated with the 2011 and 2013 CVPR conferences. In this paper, we propose a method based on the computation of the symmetry level associated to each pixel. Such a value is determined through the energy balance of the even/odd decomposition of a patch with respect to a central axis (which is equivalent to estimate the middle point of a row-wise convolution). Peaks localization along the perpendicular direction of each angle allows to identify possible symmetry axes. The evaluation of a feature based on gradient information allows to establish a classification confidence for each detected axis. By adopting the aforementioned rigorous validation framework, the proposed method indicates significant performance increase.
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