Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears ...
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ISBN:
(数字)9798350377972
ISBN:
(纸本)9798350377989
Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with imageprocessing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines imageprocessing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve
Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This ...
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Interferometric synthetic aperture radar (inSAR) phase image is a key for the digital elevation model of earth mapping. To achieve this three-dimensional reconstruction, phase unwrapping process must be performed whit...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
Interferometric synthetic aperture radar (inSAR) phase image is a key for the digital elevation model of earth mapping. To achieve this three-dimensional reconstruction, phase unwrapping process must be performed whitch is used to calculate the accurate elevations from the wrapped phase map. For the noise-free images, this process is just a simple integration of the wrapped gradient. But in reality, there is no phase image without noise, therefore the phase unwrapping have to be adaptive with a strong immunity to noise. Several adaptive algorithms have been proposed in such area where Goldstein’s branch-cut and Flynn’s quality-guided are the widely known in the path-following category, they are the most used methods and all other propositions are just enhancements or hybridizations. In this paper, we analyze the performance of each one and provide the substantial difference between them. Both algorithms are implemented using simulated and real inSAR data of different patterns and are analyzed under several relevant criteria.
Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest imageprocessing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.
With the development of computer graphics and imageprocessing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study...
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ISBN:
(数字)9798331509828
ISBN:
(纸本)9798331509835
With the development of computer graphics and imageprocessing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study proposes a new and efficient image stitching algorithm to solve the image stitching artifacts caused by perspective changes and exposure differences in multi-camera systems, and expands it to the field of virtual images. First, as a pre-processing part, the model analyzes the image through nonlinear filtering and mesh deformation optimization to ensure image quality. Subsequently, the model presents a new image stitching algorithm based on the image matching method of minimizing projection deviation and thin plate spline theory. This algorithm further proposes an improved EIEOWF algorithm based on circular weighted fusion to achieve the task of smooth transition stitching of images. Finally, the stitching technology was extended to virtual scene generation, and OpenGL technology was used to achieve real-time 3D texture mapping. The test part is verified through experiments on the UDIS-D data set. The results show that the average splicing accuracy of the proposed algorithm in 100 sets of tests is 95%, with a standard deviation of 2%, which is significantly better than the existing comparison algorithm.
During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring ...
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ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring the earth's surface at different scales; therefore these images are becoming a very relevant opportunity of investigating. The numerical analyses of imageprocessing are based on the artificial intelligence (AI) tool rather than the traditional methods. The environmental indicators from these images offer very important statistics, the carried out work of dynamic phenomena, the observation event and interpretation of evolving circumstances like: atmospheric conditions, crises and natural disasters taking the opportunity to be studied, several problems come out of deforestation and monitoring of water resources (water bodies). The purpose of this study is focused on drawing out details from the database used and then to give an offer for a texture analysis strategy. Firstly, using specific development and then developing the existing software in the second hand. This work is to establish an algorithm for the detection of region of interest (water body) which is based on the theory of fuzzy logic and the hypothesis of Fuzzy C-Means.
With the rapid advancement of the Internet of Things (IoT) technology, Wireless Sensor Networks (WSN) have become a fundamental component of contemporary society. Nevertheless, the surge in complex application scenari...
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ISBN:
(数字)9798331535087
ISBN:
(纸本)9798331535094
With the rapid advancement of the Internet of Things (IoT) technology, Wireless Sensor Networks (WSN) have become a fundamental component of contemporary society. Nevertheless, the surge in complex application scenarios coupled with an exponential increase in data volumes has rendered the traditional cloud computing processing model insufficient. The edge computing-based deep learning framework can effectively handle these large-scale image datasets without compromising privacy and security, facilitating intelligent analysis and decision-making. This study,through experimental design, we developed a deep learning model grounded in edge computing, tailored for addressing specific challenges in image data processing within wireless sensor networks, such as image recognition, classification, and analysis. For concrete metrics, consider a scenario where edge computing-based deep learning models were deployed for real-time traffic flow analysis. These models, by processing data locally, can reduce data transmission times by up to 70%, enhancing response times from seconds to milliseconds. Furthermore, in privacy-sensitive applications such as wildlife monitoring, edge computing ensures data is processed on-site, reducing the risk of sensitive data exposure by over 80%. Lastly, in terms of energy consumption, edge devices operating on optimized deep learning algorithms can achieve a reduction of up to 40% compared to traditional cloud-based processing methods, underlining the significant efficiency and performance gains achievable with this technology integration.
The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehi...
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ISBN:
(数字)9798331524227
ISBN:
(纸本)9798331524234
The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced imageprocessing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.
Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researcher...
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ISBN:
(数字)9798350355611
ISBN:
(纸本)9798350355628
Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researchers because of the rising need for quality produce. Thus, the findings highlighted in this work show the progress and current approaches and methodologies designed for the design and implementation of automation systems for grading onions. This work outlines various methods such as imageprocessing, gas sensor technology, Near Infra-Red (NIR) spectroscopy, X-ray imaging, Laser Doppler Vibrometry (LDV), and some artificial intelligence approaches that have been used in improving onion sorting systems. In addition, it reveals the drawbacks of these technologies, such as the realization of high imageprocessing, the availability of small data sets, and the stability of the sortation systems. Based on the findings of this review, some recommendations for future research are suggested, such as the integration of multi-sensor systems and the enhancement of the efficiency of the automated onion grading systems using different types of algorithms.
Different from binary computation, stochastic computation (SC), as a new paradigm, uses stochastic bit stream (SBS) to encode data. By simplifying computing elements, the circuit area can be greatly reduced. SBS can b...
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ISBN:
(纸本)9781665450737
Different from binary computation, stochastic computation (SC), as a new paradigm, uses stochastic bit stream (SBS) to encode data. By simplifying computing elements, the circuit area can be greatly reduced. SBS can be generated by a stochastic number generator (SNG) with a variety of formats. In this work, we use unipolar (UP) and bipolar (BP) formats to optimize the traditional SC subtractor, which is named the UP-to-BP Subtractor (UBS). A new cross format coding (CFC) method is proposed for stochastic computing, which combines the UP and BP format, and is applied to Sobel edge detection in imageprocessingalgorithms. The fault tolerance and detection efficacy of the proposed CFC method and conventional binary computing are compared in this paper. By using the CFC method, the detected F-Score is improved by 0.15(23%). If the F-score remains unchanged, the processing speed can be about 10 times faster.
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