Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based onmultisensor to decrease rear accidents at night. Then, we use image enhancement...
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Low visibility is one of the reasons for rear accident at night. In this paper, we propose a method to detect the leading vehicle based onmultisensor to decrease rear accidents at night. Then, we use image enhancement algorithm to improve the human vision. First, by millimeter wave radar to get the world coordinate of the preceding vehicles and establish the transformation of the relationship between the world coordinate and image pixels coordinate, we can convert the world coordinates of the radar target to image coordinate in order to form the region of interesting image. And then, by using the imageprocessing method, we can reduce interference from the outside environment. Depending on D-S evidence theory, we can achieve a general value of reliability to test vehicles of interest. The experimental results show that the method can effectively eliminate the influence of illumination condition at night, accurately detect leading vehicles, and determine their location and accurate positioning. In order to improve nighttime driving, the driver shortage vision, reduce rear-end accident. Enhancing nighttime color image by three algorithms, a comparative study and evaluation by three algorithms are presented. The evaluation demonstrates that results after image enhancement satisfy the human visual habits.
This paper presents a study on the exploitation of visual information from two points of view radically different. Computer vision is a branch of artificial intelligence that focuses on the extraction of useful inform...
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ISBN:
(纸本)9781509016457
This paper presents a study on the exploitation of visual information from two points of view radically different. Computer vision is a branch of artificial intelligence that focuses on the extraction of useful information in an image. image matching is a fundamental aspect of many problems in computer vision. Several algorithms have been developed for this purpose. Based on this research, this paper present all the previous work reviewed.
In this paper, imageprocessingalgorithms designed in Zynq SoC using the Vivado HLS tool are presented and compared with hand-coded designs. In Vivado HLS, the designer has the opportunity to employ libraries similar...
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ISBN:
(纸本)9781509045655
In this paper, imageprocessingalgorithms designed in Zynq SoC using the Vivado HLS tool are presented and compared with hand-coded designs. In Vivado HLS, the designer has the opportunity to employ libraries similar to OpenCV, a library that is well-known and wide used by software designers. The algorithms are compared in terms of area resources in two conditions: using the libraries and not using the libraries. The case studies are Data Binning, a Step Row Filter and a Sobel Filter. These algorithms have been selected because they are very common in the field of imageprocessing and they have high computational complexity. The main benefit of the Vivado HLS tool is the reduction in time-to-market. On the other hand, when a software designer hand-codes the design, the use of imageprocessing libraries similar to OpenCV helps to reduce development time even further because software designers are familiar with them. However, using these kinds of libraries significantly increases the necessary FPGA resources.
Recent developments in image and video processing employed in multimedia and communication systems require fast 2-D Discrete Cosine Transforms (DCT). The DCT is widely employed in image compression for its high power ...
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Recent developments in image and video processing employed in multimedia and communication systems require fast 2-D Discrete Cosine Transforms (DCT). The DCT is widely employed in image compression for its high power compaction property. Approximate DCT transforms have been developed to proceed faster than the original DCT while maintaining comparative levels of power compaction. This paper introduces a multiplierless efficient and low complexity 8-point approximate DCT. A flow diagram is provided for the fast implementation of the proposed transform. Only 17 additions are required for both forward and backward transformations. A fast and efficient Graphics processing Unit (GPU) implementation for the proposed transform is provided. Performance evaluation shows that the proposed transform outperforms other approximate DO' transforms in JPEG-like image compression. (C) 2016 Elsevier Inc. All rights reserved.
The well-known low-complexity JPEG and the newer JPEG-XR systems are based on block-based transform and simple transform-domain coefficient prediction algorithms. Higher complexity image compression algorithms, obtain...
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The well-known low-complexity JPEG and the newer JPEG-XR systems are based on block-based transform and simple transform-domain coefficient prediction algorithms. Higher complexity image compression algorithms, obtainable from intra-frame coding tools of video coders H.264 or HEVC, are based on multiple block-based spatial-domain prediction modes and transforms. This paper explores an alternative low-complexity image compression approach based on a single spatial-domain prediction mode and transform, which are designed based on a global image model. In our experiments, the proposed single-mode approach uses an average 20.5 % lower bit-rate than a standard low-complexity single-mode image coder that uses only conventional DC spatial prediction and 2-D DCT. It also does not suffer from blocking effects at low bit-rates.
Remote sensing systems equipped with multispectral and hyperspectral sensors are able to capture images of the surface of the Earth at different wavelengths. In these systems, hyperspectral sensors typically provide i...
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Remote sensing systems equipped with multispectral and hyperspectral sensors are able to capture images of the surface of the Earth at different wavelengths. In these systems, hyperspectral sensors typically provide images with a high spectral resolution but a reduced spatial resolution, while on the contrary, multispectral sensors are able to produce images with a rich spatial resolution but a poor spectral resolution. Due to this reason, different fusion algorithms have been proposed during the last years in order to obtain remotely sensed images with enriched spatial and spectral resolutions by wisely combining the data acquired for the same scene by multispectral and hyperspectral sensors. However, the algorithms so far proposed that are able to obtain fused images with a good spatial and spectral quality require a formidable amount of computationally complex operations that cannot be executed in parallel, which clearly prevent the utilization of these algorithms in applications under real-time constraints in which high-performance parallel-based computing systems are normally required for accelerating the overall process. On the other hand, there are other state-of-the-art algorithms that are capable of fusing these images with a lower computational effort but at the cost of decreasing the quality of the resultant fused image. In this paper, a new algorithm named computationally efficient algorithm for fusing multispectral and hyperspectral images (CoEf-MHI) is proposed in order to obtain a high-quality image from hyperspectral and multispectral images of the same scene with a low computational effort. The proposed CoEf-MHI algorithm is based on incorporating the spatial details of the multispectral image into the hyperspectral image, without introducing spectral distortions. To achieve this goal, the CoEf-MHI algorithm first spatially upsamples, by means of a bilinear interpolation, the input hyperspectral image to the spatial resolution of the input multispec
The large volume of data and computational complexity of algorithms limit the application of hyperspectral image classification to real-time operations. This work addresses the use of different parallel processing tec...
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The large volume of data and computational complexity of algorithms limit the application of hyperspectral image classification to real-time operations. This work addresses the use of different parallel processing techniques to speed up the Markov random field (MRF)-based method to perform spectral-spatial classification of hyperspectral imagery. The Metropolis relaxation labelling approach is modified to take advantage of multi-core central processing units (CPUs) and to adapt it to massively parallel processingsystems like graphics processing units (GPUs). The experiments on different hyperspectral data sets revealed that the implementation approach has a huge impact on the execution time of the algorithm. The results demonstrated that the modified MRF algorithm produced classification accuracy similar to conventional methods with greatly improved computational performance. With modern multi-core CPUs, good computational speed-up can be achieved even without additional hardware support. The CPU-GPU hybrid framework rendered the otherwise computationally expensive approach suitable for time-constrained applications.
We present a novel method for single-image super-resolution (SR). In natural images, spatial edges usually have smooth contours. From this observation, we derive a fast edge-preserving natural image prior using our pr...
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Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the...
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Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems. (C) 2016 Optical Society of America
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