The proceedings contain 20 papers. The topics discussed include: automated detection of common IED components on resource constrained computing devices;closed-loop active object recognition with constrained illuminati...
ISBN:
(纸本)9781510650800
The proceedings contain 20 papers. The topics discussed include: automated detection of common IED components on resource constrained computing devices;closed-loop active object recognition with constrained illumination power;deeplearning techniques to identify and classify COVID-19 abnormalities on chest x-ray images;deeplearning architecture search for real-timeimage denoising;self-supervised learning in medical imaging: anomaly detection in MRI using autoencoders;benchmarking the MAX78000 artificial intelligence microcontroller for deeplearning applications;high efficiency sensing in realtime;a local real-time bar detector based on the multiscale radon transform;object detection on resource-constrained platforms using a configurable ensemble of detectors;comparison of onboard processors for rapid target identification in unmanned aircraft systems;and toward a hardware implementation of lidar-based real-time insect detection.
Experiments on public datasets suggest that this method certifies its effectiveness, reaches human-level performance, and outperforms current state-of-the-art methods with 92.8% on the extended Cohn-Kanade (CK+) and 8...
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Experiments on public datasets suggest that this method certifies its effectiveness, reaches human-level performance, and outperforms current state-of-the-art methods with 92.8% on the extended Cohn-Kanade (CK+) and 87.0% on FERPLUS.
“A locally-processed light-weight deep neural network for detecting colorectal polyps in wireless capsule endoscopes” propose a light-weight DNN model that has the potential of running locally in the WCE [2].
[...]only images indicating potential diseases are transmitted, saving energy on data transmission.
Background subtraction is a substantially important video processing task that aims at separating the foreground from a video in order to make the post-processing tasks efficient.
[...]several different techniques have been proposed for this task but most of them cannot perform well for the videos having variations in both the foreground and the background.
“Background subtraction in videos using LRMF and CWM algorithm,” a novel background subtraction technique is proposed that aims at progressively fitting a particular subspace for the background that is obtained from L1-low rank matrix regularization using the cyclic weighted median algorithm and a certain distribution of a mixture of Gaussian noise for the foreground [3].
In the rapidly evolving sphere of infrastructure management, early detection of road damage stands paramount for ensuring both safety and longevity. This research introduces an innovative technique for real-time road ...
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With the continuous advancement of deeplearning and imageprocessing technologies, consumer emotion recognition has emerged as a significant area of research in advertising and marketing. Emotional responses from con...
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With the continuous advancement of deeplearning and imageprocessing technologies, consumer emotion recognition has emerged as a significant area of research in advertising and marketing. Emotional responses from consumers playAa crucial role in optimizing advertising effectiveness and marketing strategies. Among these, micro-expressions- subtle and involuntary facial movements-offer rich emotional cues that can enhance understanding of consumer sentiment. However, existing studies predominantly focus on conventional facial expressions or single-dimensional emotion classification, lacking indepth exploration and accurate detection of micro-expressions. Additionally, current approaches often overlook individual differences and the dynamic nature of emotional changes, resulting in limited accuracy and real-time performance. Effectively leveraging deeplearning and imageprocessing for precise emotion recognition thus presents a critical challenge in modern advertising. Traditional methods-based on facial expressions, speech, or physiological signals-face various limitations in practical applications. Facial expression-based models are sensitive to individual variations and rely heavily on the quality of facial feature extraction. Although speech and physiological signal-based techniques can offer valuable emotional insights, constraints in data acquisition and processing hinder their effectiveness in recognizing complex emotional states. This study aims to enhance the precision and real-time capability of consumer emotion recognition by utilizing deeplearning and imageprocessing techniques. The key research contributions include: (1) proposing an improved preprocessing method for micro-expression images to enhance emotional feature extraction;(2) designing a deeplearning model tailored for micro-expression recognition to optimize emotion classification accuracy;and (3) developing adaptive advertising strategies based on emotion recognition results to maximize adve
Due to the rapidly increasing number of vehicles and urbanization, the use of parking spaces on the streets has increased significantly. Many studies have been carried out on the determination of parking spaces by usi...
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Due to the rapidly increasing number of vehicles and urbanization, the use of parking spaces on the streets has increased significantly. Many studies have been carried out on the determination of parking spaces by using the lines in the parking areas. However, the usage areas of this method are very limited since these lines are not found in every parking area. In this research, a unique study has been presented to determine the empty and occupied parking spaces in the parking area by processing the images from the cameras located at high points on the streets with depth calculation, perspective transformation and certain imageprocessing techniques within the framework of specific features. Empty and full parking lots were determined by utilizing perspective transformation and depth measurement techniques, and the data obtained were transferred to the real-time Database environment. In addition to determining the parking spaces, the study also aims to inform users through the mobile application and to prevent traffic congestion, extra fuel consumption, waste of time and air pollution caused by fuel consumption.
Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using imageprocessing to improve operational performance that refers to any activity that ...
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Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using imageprocessing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deeplearning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deeplearning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in realtime, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site.
image segmentation in total knee arthroplasty is crucial for precise preoperative planning and accurate implant positioning, leading to improved surgical outcomes and patient satisfaction. The biggest challenges of im...
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Breakwater construction in Indonesia still relies on divers to direct the placement of rock armour units, which is risky and time-constrained. This research aims to replace the diver's task with a deeplearning-ba...
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ISBN:
(数字)9798350391992
ISBN:
(纸本)9798350392005
Breakwater construction in Indonesia still relies on divers to direct the placement of rock armour units, which is risky and time-constrained. This research aims to replace the diver's task with a deeplearning-based vision system using YOLO-based deeplearning models. The system utilizes image pre-processing technology by applying histogram equalization (HE) techniques to improve image quality before the detection process. This research evaluates the performance of the YOLO-based deeplearning models in detecting armour units in real-time with a focus on various environmental conditions, which are clear and murky water. The analysis reveals clear water consistently supports higher average frame rates (FPS) compared to murky water, maintaining efficient frame processing across all models. In murky water, histogram equalization significantly enhances detection accuracy from 60% to 80% for YOLOv4-tiny and YOLOv7-tiny, demonstrating its effectiveness in challenging conditions. Notably, accuracy remains at 100% for all models in clear water, underscoring their robust performance under optimal visibility conditions.
Label-free molecular imaging based on Raman scattering is attractive for medical imaging applications. The long exposure time of Raman imaging is the most significant barrier for medical applications. Here, we will pr...
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ISBN:
(纸本)9781510647862;9781510647855
Label-free molecular imaging based on Raman scattering is attractive for medical imaging applications. The long exposure time of Raman imaging is the most significant barrier for medical applications. Here, we will present the improvement of imaging speed using deep-learning-based segmentation for coherent Raman endoscopic imaging. We used 3,600 nerve images obtained with coherent anti-Stokes Raman scattering endoscopy for the training of U-Net architecture. We investigated the shortest available exposure time relationship between the exposure time of the input images and the quality of the output images. As a result, the imaging speed accelerated to 37.5 images/min from 0.68 images/min when the segmentation quality satisfies the criterion required for medical imaging.
In the rapidly evolving sphere of infrastructure management, early detection of road damage stands paramount for ensuring both safety and longevity. This research introduces an innovative technique for real-time road ...
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In the rapidly evolving sphere of infrastructure management, early detection of road damage stands paramount for ensuring both safety and longevity. This research introduces an innovative technique for real-time road damage detection by leveraging the Mask R-CNN (Region-based Convolutional Neural Networks) approach. The primary objective was to discern varied forms of damages – from cracks to potholes, ensuring timely interventions and repairs. Utilizing a robust dataset comprising images of multiple road surfaces under different environmental conditions, the Mask R-CNN model was trained exhaustively. Results reveal a commendable accuracy rate, with the model distinguishing between minor aberrations and significant damages adeptly. A distinctive feature was the model's capability to operate in real-time, aiding in instant damage reporting. Furthermore, a comparative analysis with existing methods demonstrated a marked improvement in terms of both detection speed and precision. The findings suggest promising implications for urban planning and road maintenance. The integration of such an approach can revolutionize the manner in which road monitoring is traditionally undertaken, potentially resulting in substantial economic savings and enhanced safety measures.
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