Vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innov...
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Vision based runway identification using 'marked or unmarked terrain' image sequences captured from a fixed wing unmanned aerial vehicle through onboard stereovision sensor is presented in this paper. An innovative convolutional neural netwok (CNN) based YOLO-V8 object detection algorithm is used to detect the runway during approach segment of UAV. This deep learning algorithm detects the region of interest in real time and in a computationally efficient manner. The captured unknown road segment or runway image frames are processed and examined for width, length, level and smoothness aspects to qualify as a suitable runway for UAV landings. Also, it is ensured that there are no obstacles, patches or holes on the detected road or runway. Runway start and end threshold lines and regions, touchdown point and runway edge lines are considered as the region of interest. imageprocessingalgorithms are applied on the captured runway or road images to detect strong features in the region of interest. Feature detector based imageprocessing algorithm with stereo vision constraint is used to establish the relation between unmanned aerial vehicle's center of gravity and detected runway feature points imageprocessingalgorithms like hough line detection, RANSAC, Oriented FAST and Rotated BRIEF (ORB), median filters, morphological methods are applied to extract terrain features. Based on the detected runway orientation and position with respect to UAV position. An automatic landing manoeuvre is performed by UAV autopilot to land the UAV on intended touchdown point on runway computed through detected feature points.
With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing perfor...
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The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. While previous research has acknowledge...
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This research paper presents a website that leverages the power of the image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using image GPT's...
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Early detection and accurate prediction of liver disease play a crucial role in improving patient outcomes and reducing the burden on healthcare systems. Segmenting the liver and its tumors using computed tomography (...
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Early detection and accurate prediction of liver disease play a crucial role in improving patient outcomes and reducing the burden on healthcare systems. Segmenting the liver and its tumors using computed tomography (CT) images is an essential undertaking for the diagnosis and treatment of liver illnesses. Because of the uneven distribution, hazy boundaries, varied densities, forms, and sizes of lesions, segmenting the liver and associated tumor is a challenging task. Up until this point, our primary focus has been on developing deep learning algorithms that can separate the liver and its tumor from CT scan pictures of the abdomen, saving time and effort when diagnosing liver illnesses. A deep learning-based automatic segmentation method is presented that uses the improved densenet121 model to segment the liver and its tumor. In this model imageprocessing is used for the accurate automated segmentation of tumors, the proposed method demonstrates the ability to accurately segment the liver as well, as indicated by the confusion matrix obtained when comparing to the previous work on liver and tumor segmentation. The Densenet121 architecture serves as the foundation for the algorithm employed here, we introduced an autonomous technique to segment the liver from CT scans and lesions from the segmented liver region, based on semantic segmentation convolutional neural networks ( CNNs). For liver and tumor segmentations, respectively, the suggested system achieves an accuracy of 95.31% to 95.39%.
This paper delves into an innovative image recognition algorithm that merges deep learning techniques with Generative Adversarial Networks (GANs) and offers a comparative analysis against traditional image recognition...
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Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the r...
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This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical over...
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ISBN:
(纸本)9798350391558;9798350379990
This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signal processing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.
Recently, machine learning algorithms have been widely used in the fields of imageprocessing, network security and natural language processing, etc., profoundly affecting human life. However, machine learning algorit...
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The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Sinc...
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ISBN:
(纸本)9798400704123
The strong temporal consistency of surveillance video enables compelling compression performance with traditional methods, but downstream vision applications operate on decoded image frames with a high data rate. Since it is not straightforward for applications to extract information on temporal redundancy from the compressed video representations, we propose a novel system which conveys temporal redundancy within a sparse decompressed representation. We leverage a video representation framework called AD Delta ER to transcode framed videos to sparse, asynchronous intensity samples. We introduce mechanisms for content adaptation, lossy compression, and asynchronous forms of classical vision algorithms. We evaluate our system on the VIRAT surveillance video dataset, and we show a median 43.7% speed improvement in FAST feature detection compared to OpenCV. We run the same algorithm as OpenCV, but only process pixels that receive new asynchronous events, rather than process every pixel in an image frame. Our work paves the way for upcoming neuromorphic sensors and is amenable to future applications with spiking neural networks.
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