Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detec...
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ISBN:
(纸本)9781467389112
Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application of warning the driver when pedestrians are in front of the car. For such applications, it is not required to detect each pedestrian independently, but to generate an alarm when necessary. In this paper we explore techniques to boost the accuracy of recent channel-based algorithms in this application: algorithmic refinements as well as the inclusion of an LWIR image channel. We use the KAIST dataset which is constructed from image-pairs of both the visual and the LWIR spectrum, in day and night conditions. We study the influence of techniques that have shown success in literature.
In this paper, we introduce a novel edge directed image detail preserving impulse noise removal algorithm. Our approach is based on local image directionality features. Local edge features are analysed on both a downs...
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In this paper, we introduce a novel edge directed image detail preserving impulse noise removal algorithm. Our approach is based on local image directionality features. Local edge features are analysed on both a downsampled version of the image and the noisy image, and both soft an sharp edges are considered for selective noise removal. Experimental results on a set of standard images show our technique to be effective in removing salt-and-pepper noise even at high noise levels, to yield good image quality and to outperform a number of other noise removal techniques.
Recently, Automatic Number Plate Recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Ch...
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Recently, Automatic Number Plate Recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to provide better recognition rate, High Definition (HD) cameras have started to be used. However, most known techniques for standard definition are not suitable for real-time HD imageprocessing due to the computationally intensive cost of localizing the number plate. In this paper, algorithms to implement the three main stages of a high definition ANPR system for Qatari number plates are presented. The algorithms have been tested using MATLAB and two databases as a proof of concept. Implementation results have shown that the system is able to process one HD image in 61 ms, with an accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR.
In this work, we present a parallel implementation of the Singular Value Decomposition (SVD) method on Graphics processing Units (GPUs) using CUDA programming model. Our approach is based on an iterative parallel vers...
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In this work, we present a parallel implementation of the Singular Value Decomposition (SVD) method on Graphics processing Units (GPUs) using CUDA programming model. Our approach is based on an iterative parallel version of the QR factorization by means Givens plane rotations using the Sameh and Kuck scheme. The parallel algorithm is driven by an outer loop executed on the CPU. Therefore, threads and blocks configuration is organized in order to use the shared memory and avoid multiple accesses to global memory. However, the main kernel provides coalesced accesses to global memory using contiguous indices. As case study, we consider the application of the SVD in the Overcomplete Local Principal Component Analysis (OLPCA) algorithm for the Diffusion Weighted Imaging (DWI) denoising process. Our results show significant improvements in terms of performances with respect to the CPU version that encourage its usability for this expensive application.
The paper describes a problem of detection small moving objects. A size of these objects is only one or few pixels. Relevant mathematical apparatus which are usually used for processing of dynamic pictures are include...
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The paper describes a problem of detection small moving objects. A size of these objects is only one or few pixels. Relevant mathematical apparatus which are usually used for processing of dynamic pictures are included. There was proposed experimental module for standalone locator for real time operation. This module is consisted from two main parts. First part is used for imaging of scene, especially small moving objects. We use four camera systems. The Second part comprises a development board based on Raspberry PI 2. We use GNU/Linux system on the board. There were also tested methods useful for the essence of this project. On behalf of the tests was developed an algorithm to detect small particle in video sequence. C and python language routines were build including essence algorithms. This algorithm consists of: images are captured one by one from all 4 cameras 640 £ 480; RGB to Gray transformation; For each image are all shapes detected; unnecessary shapes are removed; images are filtered with additional filters; Noise free images are then compared for movement detection. images with movement are stored on SD card. Additional information: Devices captures and stores all flying objects. Data are accessible through LAN port. We use OpenCv for image analysis. Device is used for sky movement detection of small objects, birds. After detection, it can create acoustic signal to scare them away. This minimizes bird impact in Agriculture and it prevents bird-plane collisions on airports. This paper describes in more detail not only the entire detection algorithm, but also the results of the test algorithm.
Missile-borne synthetic aperture radar (SAR) imaging system is built up according to actual working principle, it uses the input data and embedded algorithms to simulate echo, then generate SAR image. Relevant algorit...
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ISBN:
(纸本)9781509007691
Missile-borne synthetic aperture radar (SAR) imaging system is built up according to actual working principle, it uses the input data and embedded algorithms to simulate echo, then generate SAR image. Relevant algorithms is analyzed, a SAR echo simulation method based on graphic processing unit (GPU) acceleration is presented to satisfy the request of real-time. Simulation platform realized by MATLAB GUI turns out to be reliable and interactive, it can meet the demand for missile-borne SAR system test and development, and has some practical value.
image quality assessment is a very important and challenging task for many imageprocessing applications. The task of quality assessment of an image can be done either with the help of a reference image of the same sc...
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image quality assessment is a very important and challenging task for many imageprocessing applications. The task of quality assessment of an image can be done either with the help of a reference image of the same scene or blindly without any reference image. No-reference image quality assessment algorithms specific to a particular type of distortion are very popular for different imageprocessing applications. Color quantization is a technique to reduce the number of unique colors in the image, but excessive color quantization can reduce the visual quality of images. In this paper, we propose a no-reference image quality measure specific to quality assessment color quantized images and color quantized images with dither. The results are validated using a subset of the standard TID2013 image quality dataset for validating it in accordance with the human visual system.
In this paper, we present an automated algorithm for detection of blood vessels in 2D-thermographic images for breast cancer screening. Vessel extraction from breast thermal images help in the classification of malign...
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In this paper, we present an automated algorithm for detection of blood vessels in 2D-thermographic images for breast cancer screening. Vessel extraction from breast thermal images help in the classification of malignancy as cancer causes increased blood flow at warmer temperatures, additional vessel formation and tortuosity of vessels feeding the cancerous growth. The proposed algorithm uses three enhanced images to detect possible vessel regions based on their intensity and shape. The final vessel detection combines these three outputs. The algorithm does not depend on the variation of pixel intensity in the images but only depends on the relative variation unlike many standard algorithms. On a dataset of over 40 subjects with high-resolution thermographic images, we are able to extract the vessels accurately with elimination of diffused heat regions. Future studies would involve extracting features from the detected vessels and using these features for classification of malignancy.
The method of joining images to make a panorama is known as image stitching. It is an enthusiastic research area in imageprocessing and computer vision but still a challenging problem for panoramic images. A good num...
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ISBN:
(纸本)9781509057702
The method of joining images to make a panorama is known as image stitching. It is an enthusiastic research area in imageprocessing and computer vision but still a challenging problem for panoramic images. A good number of researches had been carried out to develop different algorithms for image stitching in the last few years. image stitching approaches is classified mainly in two groups: direct and feature based. Direct techniques evaluate pixel intensities of the input images and feature-based methods resolve an association among the images based on the extracted features of inputted images. A detail study on the state-of-the-art of feature-based image stitching approaches is presented in this paper. We have shown the performance of some of the feature-based image stitching approaches using images from Yale Database. In addition, we briefly discussed the challenges and possible future work of image stitching.
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