Background Subtraction Based on Principal Motion for a Freely Moving Camera

Ma, Yingnan; Dong, Guanfang; Zhao, Chenqiu; Basu, Anup; Wu, Zhengjiang

doi:10.1007/978-3-030-54407-2_6

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Another method was raised by this paper [13], naming Robust Principal Components Analysis (RPCA) which was used to subtract the background from a freely moving camera. After optical flow was extracted, they implemented motion decomposition using RPCA.…”

Section: Related Work a Review Of Image Processing Based Methodologymentioning

confidence: 99%

Feature matching in Ultrasound images

Zhu¹,

Wang²

2020

Preprint

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Feature matching is an important technique to identify a single object in different images. It helps machine to construct recognition of a specific object from multiple perspectives. For years, feature matching has been commonly used in various computer vision applications, like traffic surveillance, self-driving, and other systems. With the arise of Computer-Aided Diagnosis(CAD), the need of feature matching technique also emerges in medical imaging field. In this paper, we present a deep learning based method specially to ultrasound images. It will be examined against existing methods that have outstanding results on regular images. As the ultrasound images is different to regular images in many fields like texture, noise type, and dimension, traditional methods will be evaluated and optimized to be applied to ultrasound images.

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Section: Related Work a Review Of Image Processing Based Methodologymentioning

confidence: 99%

Feature matching in Ultrasound images

Zhu¹,

Wang²

2020

Preprint

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“…However, dealing with a moving camera is more challenging. This method [21] first used the Robust Principal Component Analysis (RPCA) to extract background motion, assuming the background scenes motion can be described as a low-rank matrix, then segment the frames into sub-pixels to enhance the accuracy of converting optical flow into magnitude and angel of motion, thus improve the results.…”

Section: A Traditional Methodsmentioning

confidence: 99%

An Overview Of 3D Object Detection

Wang¹,

Ye²

2020

Preprint

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Point cloud 3D object detection has recently received major attention and becomes an active research topic in 3D computer vision community. However, recognizing 3D objects in LiDAR (Light Detection and Ranging) is still a challenge due to the complexity of point clouds. Objects such as pedestrians, cyclists, or traffic cones are usually represented by quite sparse points, which makes the detection quite complex using only point cloud. In this project, we propose a framework that uses both RGB and point cloud data to perform multiclass object recognition. We use existing 2D detection models to localize the region of interest (ROI) on the RGB image, followed by a pixel mapping strategy in the point cloud, and finally, lift the initial 2D bounding box to 3D space. We use the recently released nuScenes dataset-a large-scale dataset contains many data formats-to training and evaluate our proposed architecture.

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“…Despite the abovementioned difficulties, noise removal is crucial in the field of computer vision, such as for image segmentation [6]. Specifically, organ segmentation can help doctors quickly reconstruct information about human tissues [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Feature-Guided CNN for Denoising Images From Portable Ultrasound Devices

Dong

Basu

2021

IEEE Access

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As a non-invasive medical imaging scanning device, ultrasound has greatly increased the efficiency and accuracy of medical diagnosis. In recent years, portable ultrasound is being more widely used for its convenience and lower cost. Patients and physicians can receive the scanned images on their mobile phones at any time via a wireless network with low latency. However, it is difficult for portable ultrasound devices to capture images with the same quality as standard hospital ultrasound image acquisition systems. Usually, the images captured by portable ultrasound equipment have considerable noise. This noise undoubtedly affects the diagnosis of the physician. It is imperative to develop methods to remove the noise while preserving important information in the image. For this reason, we propose a novel denoising neural network model, called Feature-guided Denoising Convolutional Neural Network (FDCNN), to remove noise while retaining important feature information. In order to achieve high-quality denoising results, we employ a hierarchical denoising framework driven by a feature masking layer for medical images. Furthermore, we propose a feature extraction algorithm based on Explainable Artificial Intelligence (XAI) for medical images. Experimental results show that our medical image feature extraction method outperforms previous methods. Combined with the new denoising neural network architecture, portable ultrasound devices can now achieve better diagnostic performance.

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Background Subtraction Based on Principal Motion for a Freely Moving Camera

Cited by 5 publications

References 20 publications

Feature matching in Ultrasound images

Feature matching in Ultrasound images

An Overview Of 3D Object Detection

Feature-Guided CNN for Denoising Images From Portable Ultrasound Devices

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