Detecting Significant Changes in Image Sequences

2017 ◽  
pp. 379-408
Author(s):  
Sergii Mashtalir ◽  
Olena Mikhnova
Keyword(s):  
Video Processing ◽  
Image Features ◽  
Spatial Image ◽  
Image And Video Processing ◽  
Novel Approach ◽  
Image Partition ◽  
Video Scene ◽  
High Level ◽  
Point Detection

In this chapter the authors propose an overview on contemporary artificial intelligence techniques designed for change detection in image and video sequences. A variety of image features have been analyzed for content presentation at a low level. In attempt towards high-level interpretation by a machine, a novel approach to image comparison has been proposed and described in detail. It utilizes techniques of salient point detection, video scene identification, spatial image segmentation, feature extraction and analysis. Metrics implemented for image partition matching enhance performance and quality of the results, which has been proved by several estimations. The review on estimation measures is also given along with references to publicly available test datasets. Conclusion is provided in relation to trends of future development in image and video processing.

Detecting Significant Changes in Image Sequences

2018 ◽  
pp. 80-109
Author(s):  
Sergii Mashtalir ◽  
Olena Mikhnova
Keyword(s):  
Video Processing ◽  
Image Features ◽  
Spatial Image ◽  
Image And Video Processing ◽  
Novel Approach ◽  
Image Partition ◽  
Video Scene ◽  
High Level ◽  
Point Detection

In this chapter the authors propose an overview on contemporary artificial intelligence techniques designed for change detection in image and video sequences. A variety of image features have been analyzed for content presentation at a low level. In attempt towards high-level interpretation by a machine, a novel approach to image comparison has been proposed and described in detail. It utilizes techniques of salient point detection, video scene identification, spatial image segmentation, feature extraction and analysis. Metrics implemented for image partition matching enhance performance and quality of the results, which has been proved by several estimations. The review on estimation measures is also given along with references to publicly available test datasets. Conclusion is provided in relation to trends of future development in image and video processing.

Accelerating Sobel Edge Detection Using Compressor Cells Over FPGAs

2018 ◽  
pp. 1133-1154
Author(s):  
Ahmed Abouelfarag ◽  
Marwa Ali Elshenawy ◽  
Esraa Alaaeldin Khattab
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Video Processing ◽  
High Level Synthesis ◽  
Computational Time ◽  
The Novel ◽  
Image And Video Processing ◽  
Sobel Edge Detection ◽  
High Level ◽  
Time Image

Recently, computer vision is playing an important role in many essential human-computer interactive applications, these applications are subject to a “real-time” constraint, and therefore it requires a fast and reliable computational system. Edge Detection is the most used approach for segmenting images based on changes in intensity. There are various kernels used to perform edge detection, such as: Sobel, Robert, and Prewitt, upon which, the most commonly used is Sobel. In this research a novel type of operator cells that perform addition is introduced to achieve computational acceleration. The novel operator cells have been employed in the chosen FPGA Zedboard which is well-suited for real-time image and video processing. Accelerating the Sobel edge detection technique is exploited using different tools such as the High-Level Synthesis tools provided by Vivado. This enhancement shows a significant improvement as it decreases the computational time by 26% compared to the conventional adder cells.

Accelerating Sobel Edge Detection Using Compressor Cells Over FPGAs

2017 ◽  
pp. 1-21
Author(s):  
Ahmed Abouelfarag ◽  
Marwa Ali Elshenawy ◽  
Esraa Alaaeldin Khattab
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Video Processing ◽  
High Level Synthesis ◽  
Computational Time ◽  
The Novel ◽  
Image And Video Processing ◽  
Sobel Edge Detection ◽  
High Level ◽  
Time Image

Recently, computer vision is playing an important role in many essential human-computer interactive applications, these applications are subject to a “real-time” constraint, and therefore it requires a fast and reliable computational system. Edge Detection is the most used approach for segmenting images based on changes in intensity. There are various kernels used to perform edge detection, such as: Sobel, Robert, and Prewitt, upon which, the most commonly used is Sobel. In this research a novel type of operator cells that perform addition is introduced to achieve computational acceleration. The novel operator cells have been employed in the chosen FPGA Zedboard which is well-suited for real-time image and video processing. Accelerating the Sobel edge detection technique is exploited using different tools such as the High-Level Synthesis tools provided by Vivado. This enhancement shows a significant improvement as it decreases the computational time by 26% compared to the conventional adder cells.

A virtual e-bespoke men's shirt based on new body measurements and method of pattern drafting

2020 ◽  
Vol 90 (19-20) ◽  
pp. 2223-2244
Author(s):  
Jiaqi Yan ◽  
Victor E Kuzmichev
Keyword(s):  
New Technologies ◽  
Apparel Industry ◽  
Morphological Features ◽  
Evaluation Procedure ◽  
Body Measurements ◽  
The Novel ◽  
Novel Approach ◽  
The One ◽  
High Level

Customization is prevailing in the apparel industry with increasing requirements from consumers and the popularization of new technologies. This study aimed to establish the novel approach of applying existing and new body measurements to customize the pattern block of a men's shirt, to enrich the anthropometric database, and to develop the fit evaluation procedure. New body measurements were extracted from 156 scanned male mesh bodies in accordance with the morphological features and developing method of pattern block sketching. Owing to these new body measurements, the customized shirt with assured high-level fit can be obtained by generating original patterns as bespoke, on the one hand, and by transforming ready-to-wear patterns, on the other hand. The first way is e-bespoke tailoring that utilizes the developed schedule of body morphological features, improved shirt pattern of desired style (body fit, slim fit, regular fit, and comfort fit), and virtual try-on software CLO 3D. The proposed method of virtual e-bespoke design allows readily completing a well-fitted and balanced men's shirt, which will contribute to the efficiency of customization and quality of end-products for the apparel industry.

scholarly journals Discrimination of Sidewalk Surface Condition Based on Image Textures and Meteorological Information

2007 ◽  
Vol 11 (5) ◽  
pp. 491-501 ◽  
Author(s):  
Handri Santoso ◽  
◽  
Kazuo Nakamura ◽  
Keyword(s):  
Neural Network ◽  
Video Processing ◽  
Surface Condition ◽  
Texture Feature ◽  
Image Features ◽  
Image And Video Processing ◽  
Front End ◽  
Heavy Snow ◽  
Background Image ◽  
Snow Fall

Slippery roads, especially during and after a heavy snow fall, may lead to accidents causing injuries and fatalities to vulnerable person such as the aged. In this context, it is important to keep pedestrian aware of sidewalk condition. This paper aims at proposing detection of several sidewalk conditions under different environment circumstance. At the front end, image and video processing is perrformed to separate background and foreground images. Background image features are extracted using several texture feature generators. In this study, factor analysis methods are employed to examine the pattern of correlations among variables, and to reduce data dimensionality. Finally, Artificial Neural Network is employed to discriminate sidewalk surface condition, i.e., dry, wet, or snow.

scholarly journals LIME-Based Data Selection Method for SAR Images Generation Using GAN

2022 ◽  
Vol 14 (1) ◽  
pp. 204
Author(s):  
Mingzhe Zhu ◽  
Bo Zang ◽  
Linlin Ding ◽  
Tao Lei ◽  
Zhenpeng Feng ◽  
...  
Keyword(s):  
Video Processing ◽  
Generative Adversarial Networks ◽  
Correct Prediction ◽  
Positive Contribution ◽  
Sar Image ◽  
Sar Images ◽  
Adversarial Networks ◽  
Image And Video Processing ◽  
Interpretable Model

Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.

Real-Time Image and Video Processing Using High-Level Synthesis (HLS)

2017 ◽  
pp. 390-408
Author(s):  
Murad Qasaimeh ◽  
Ehab Najeh Salahat
Keyword(s):  
Video Processing ◽  
High Performance ◽  
Low Cost ◽  
Optimization Techniques ◽  
High Level Synthesis ◽  
Image And Video Processing ◽  
Digital Hardware ◽  
Processing Algorithms ◽  
Computationally Intensive ◽  
High Level

Implementing high-performance, low-cost hardware accelerators for the computationally intensive image and video processing algorithms has attracted a lot of attention in the last 20 years. Most of the recent research efforts were trying to figure out new design automation methods to fill the gap between the ability of realizing efficient accelerators in hardware and the tight performance requirements of the complex image processing algorithms. High-Level synthesis (HLS) is a new method to automate the design process by transforming high-level algorithmic description into digital hardware while satisfying the design constraints. This chapter focuses on evaluating the suitability of using HLS as a new tool to accelerate the most demanding image and video processing algorithms in hardware. It discusses the gained benefits and current limitations, the recent academic and commercial tools, the compiler's optimization techniques and four case studies.

Real-Time Image and Video Processing Using High-Level Synthesis (HLS)

2018 ◽  
pp. 1004-1022
Author(s):  
Murad Qasaimeh ◽  
Ehab Najeh Salahat
Keyword(s):  
Video Processing ◽  
High Performance ◽  
Low Cost ◽  
Optimization Techniques ◽  
High Level Synthesis ◽  
Image And Video Processing ◽  
Digital Hardware ◽  
Processing Algorithms ◽  
Computationally Intensive ◽  
High Level

Implementing high-performance, low-cost hardware accelerators for the computationally intensive image and video processing algorithms has attracted a lot of attention in the last 20 years. Most of the recent research efforts were trying to figure out new design automation methods to fill the gap between the ability of realizing efficient accelerators in hardware and the tight performance requirements of the complex image processing algorithms. High-Level synthesis (HLS) is a new method to automate the design process by transforming high-level algorithmic description into digital hardware while satisfying the design constraints. This chapter focuses on evaluating the suitability of using HLS as a new tool to accelerate the most demanding image and video processing algorithms in hardware. It discusses the gained benefits and current limitations, the recent academic and commercial tools, the compiler's optimization techniques and four case studies.

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