Line and part detection is an important vicinity of virtual imageprocessing. It can be used for a ramification of functions, consisting of object recognition in photographs. Machine studying is a technique that may b...
Line and part detection is an important vicinity of virtual imageprocessing. It can be used for a ramification of functions, consisting of object recognition in photographs. Machine studying is a technique that may be used to improve the accuracy and performance of line and area detection. The basic approach for line and side detection is to first convert the photo into a one-dimensional signal. This sign can then be processed with sign processing.techniques which include Fourier remodel or discrete cosine transform. Later on, algorithms may be used to detect neighborhood functions within the signal inclusive of modifications in depth or neighborhood maximums or minimums. This is used to become aware of edges and line obstacles. The use of system studying for line and part detection calls for the definition of the perfect learning undertaking. In most cases, this assignment involves the category of different line and edge segments as both a line and an aspect. That is usually executed by computing various feature vectors for each section after which using an algorithm to classify each phase as both a line and a part. One technique to characteristic extraction is to use a convolutional neural community (CNN) structure, that's a form of device mastering algorithm that can mechanically extract important capabilities from an image. The output of the CNN can then be used as enter to a classification.
image reconstruction is critical in medical imaging, where accurate data restoration is essential for precise analysis and diagnosis. This research proposes an innovative architectural framework for medical image reco...
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With the development of Synthetic Aperture Radar (SAR), there is a growing demand for rapid SAR imageprocessing. However, traditional Graphics processing.Unit (GPU) based processing.faces challenges in meeting real-t...
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ISBN:
(纸本)9798350360332;9798350360325
With the development of Synthetic Aperture Radar (SAR), there is a growing demand for rapid SAR imageprocessing. However, traditional Graphics processing.Unit (GPU) based processing.faces challenges in meeting real-time application requirements, especially in scenarios like maritime search and rescue, due to time delays caused by input/output data transmission through the Peripheral Component Interconnect Express (PCIe) bus and high power consumption. To address this issue, our research proposes a real-time SAR ship detection system based on a lightweight Field-Programmable Gate Array (FPGA). The system utilizes a trainable pseudo-color synthesis network for SAR image preprocessing.and employs a generic convolutional architecture to convert the YOLO v5 model into FPGA-executable hardware language. Experimental results indicate that the FPGA achieves a processing.time of 68.9 milliseconds, significantly outperforming the GPU (234.7 milliseconds), without compromising detection accuracy. This research enhances SAR image detection speed, with implications for deploying object detection. algorithms on FPGAs.
This paper develops a digital watermarking algorithm using an informed watermark retrieval architecture. The developed method uses the fractional Fourier transform to embed the watermark in the space-frequency domain ...
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This paper develops a digital watermarking algorithm using an informed watermark retrieval architecture. The developed method uses the fractional Fourier transform to embed the watermark in the space-frequency domain and extracts the watermark using blind source separation techniques. The watermark embedding is further enhanced using a heuristic algorithm to increase the strength of the watermarking system. We use genetic algorithm to find the optimal fractional domain by minimizing the coefficient of RMSE between the input image and the watermarked image. The algorithm's performance against various common attacks, e.g., JPEG compression and Gaussian noise, is presented to estimate the algorithm's robustness.
In high-precision image segmentation tasks, even slight deviations in the segmentation results can bring about significant consequences, especially in certain application areas such as medical imaging and remote sensi...
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In high-precision image segmentation tasks, even slight deviations in the segmentation results can bring about significant consequences, especially in certain application areas such as medical imaging and remote sensing image classification. The precision of segmentation has become the main factor limiting its development. Researchers typically refine image segmentation algorithms to enhance accuracy, but it is challenging for any improvement strategy to be effectively applied to images of different objects and scenes. To address this issue, we propose a two-step refinement method for image segmentation, comprising region expansion and minor contour adjustments. First, we design an adaptive gradient thresholding module to provide gradient-based constraints for the refinement process. Next, the region expansion module iteratively refines each segmented region based on colour differences and gradient thresholds. Finally, the minor contour adjustments module leverages local strong gradient features to refine the contour positions further. This method integrates region-level and pixel-level information to refine various image segmentation results. This method was applied to the BSDS500, Cells, and WHU Building datasets. The results demonstrate that the refined closed contours align more closely with the ground truth, with the most notable improvement observed at contour inflection points (corner points). Among the results, the Cells dataset showed the most significant improvement in segmentation accuracy, with the F-score increasing from 87.51% to 89.73% and IoU from 86.83% to 88.40%.
Design for Embedded imageprocessing.on FPGAs Bridge the gap between software and hardware with this foundational design reference Field-programmable gate arrays (FPGAs) are integrated circuits designed so that config...
ISBN:
(数字)9781119819813;9781119819806
ISBN:
(纸本)9781119819790
Design for Embedded imageprocessing.on FPGAs Bridge the gap between software and hardware with this foundational design reference Field-programmable gate arrays (FPGAs) are integrated circuits designed so that configuration can take place. Circuits of this kind play an integral role in processing.images, with FPGAs increasingly embedded in digital cameras and other devices that produce visual data outputs for subsequent realization and compression. These uses of FPGAs require specific design processes designed to mediate smoothly between hardware and processing.algorithm. Design for Embedded imageprocessing.on FPGAs provides a comprehensive overview of these processes and their applications in embedded imageprocessing. Beginning with an overview of imageprocessing.and its core principles, this book discusses specific design and computation techniques, with a smooth progression from the foundations of the field to its advanced principles. Readers of the second edition of Design for Embedded imageprocessing.on FPGAs will also find:
Detailed discussion of imageprocessing.techniques including point operations, histogram operations, linear transformations, and more
New chapters covering Deep Learning algorithms and image and Video Coding
Example applications throughout to ground principles and demonstrate techniques
Design for Embedded imageprocessing.on FPGAs is ideal for engineers and academics working in the field of imageprocessing. as well as graduate students studying Embedded Systems Engineering, imageprocessing.digital Design, and related fields.
In this paper, we propose an advanced scripting approach using Python and R for satellite imageprocessing.and modelling terrain in Cote d'Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM di...
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In this paper, we propose an advanced scripting approach using Python and R for satellite imageprocessing.and modelling terrain in Cote d'Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and 'raster' and 'terra' packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Cote d'Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Cote d'Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing.include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Cote d'Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite imageprocessing.
Digitized methodologies in the recent era contribute to various fields of automation that used to hold different interests and meanings of human life. Buildings with historical significance, cultural values, and belie...
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Digitized methodologies in the recent era contribute to various fields of automation that used to hold different interests and meanings of human life. Buildings with historical significance, cultural values, and beliefs are becoming an interdisciplinary field of interest, engaging more computer scientists nowadays. Such structures need more attention towards reconstructing their values using a flavor of computerized tools instead of brickwork directly. Due to the wear of time, the tiles and engravings of most of the historical monuments are on the verge of ruin, endangering significant historical values. In this survey, we rebuild the values by delving deep into the device and methodologies by providing a comprehensive understanding of emerging fields and some experimental decisions. We discuss heritage restoration from some essential papers on 3D reconstruction, image inpainting, IoT-based methods, genetic algorithms, and imageprocessing. The survey explains Machine Learning, Deep Learning, and Computer Vision-based methods for various restoration tasks in the related field. We divide this into certain parts contributing to different fields that restore cultural heritage. Moreover, we infer that the techniques will be faster, cheaper, and more beneficial to the context of image reconstruction in the near future.
The Internet of Things is transforming our interactions with the world. CMOS image Sensors are crucial components in multimedia applications for the Internet of Things. They allow IoT devices to perceive, interpret, a...
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The Internet of Things is transforming our interactions with the world. CMOS image Sensors are crucial components in multimedia applications for the Internet of Things. They allow IoT devices to perceive, interpret, and respond to the physical world, improving efficiency, safety, and convenience across various industries. The images captured by these sensors are sent to an external digital device for processing. This data flow can result in high power consumption, limit the frame rate, and restrict the parallelization of algorithm data. This paper introduces a new CMOS Smart Imaging Sensor architecture that performs kernel-based spatial filtering at the pixel level, overcoming the drawbacks above. The design proposal enables spatial filtering using a single kernel, two different kernels on the same image, or two cascaded kernels. The circuit calculates the absolute value of the single convolution in parallel at each pixel using simple arithmetic operations within a neighborhood during photocurrent integration. This process is repeated when the filter requires more than one kernel. We designed a 128 x 128-pixel imager in a 0.35 mu m CMOS process and validated it through post-layout simulations. According to these simulations, the kernel-based spatial filter circuit is integrated into the imager, which processes images at frame rates ranging from 746 to 752 fps. Our CMOS implementation outperforms state-of-the-art circuits in denoising, with the best result: a Mean Squared Error of 0.90, Peak Signal Noise Ratio of 48.55 dB, and Structural Similarity Index Metric of 0.99 configured as the mean filter. Additionally, it demonstrates comparable performance in edge detection to state-of-the-art circuits. The best results were achieved with a Mean Squared Error of 4.68, a Peak Signal to Noise Ratio of 41.42 dB, and a Structural Similarity Index Metric of 0.97, using the y-direction Robert filter. Our proposed circuit demonstrated good edge-location accuracy, with a value of
Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical ha...
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Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms.
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