Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection, environmentally friendly construction, research, and numerous other domains. I...
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ISBN:
(纸本)9783031546044;9783031546051
Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection, environmentally friendly construction, research, and numerous other domains. In order to develop an efficient and robust airborne dust monitoring algorithm, various challenges have to be overcome. Airborne dust may be opaque as well translucent, can vary heavily in density, and its boundaries are fuzzy. Also, dust may be hard to distinguish from other atmospheric phenomena such as fog or clouds. To cover the demand for a performant and reliable approach for monitoring airborne dust, we propose DustNet, a dust density estimation neural network. DustNet exploits attention and convolutional-based feature pyramid structures to combine features from multiple resolution and semantic levels. Furthermore, DustNet utilizes highly aggregated global information features as an adaptive kernel to enrich high-resolution features. In addition to the fusion of local and global features, we also present multiple approaches for the fusion of temporal features from consecutive images. In order to validate our approach, we compare results achieved by our DustNet with those results achieved by methods originating from the crowd-counting and the monocular depth estimation domains on an airborne dust density dataset. Our DustNet outperforms the other approaches and achieves a 2.5% higher accuracy in localizing dust and a 14.4% lower mean absolute error than the second-best approach.
Snapshot testing is a form of software testing that is focused on visual components by highlighting any code changes when compared to a previously stored state. This quick and simple method of testing is growing popul...
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ISBN:
(纸本)9798350311846
Snapshot testing is a form of software testing that is focused on visual components by highlighting any code changes when compared to a previously stored state. This quick and simple method of testing is growing popular among the industry with companies such as Spotify and Robinhood. Despite its growing popularity, snapshot testing is barely explored in academia. In this paper, we use GitHub API to collect a dataset of 686 repositories tagged with Jest, a popular testing framework capable of snapshot testing. From those repositories, we found 4,604 snapshot files and 11,367 test files. The top-10 repositories represent 20% of all snapshot files in the dataset, even though it is only 3% of the size. We acknowledge that improvements can be made in the dataset but due to the lack of data on snapshot testing, we believe the current dataset is useful in helping researchers to study this topic.
Background and objective: Automated detection and quantification of carotid artery stenosis is a crucial task in establishing a computer-aided diagnostic system for brain diseases. Digital subtraction angiography (DSA...
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Background and objective: Automated detection and quantification of carotid artery stenosis is a crucial task in establishing a computer-aided diagnostic system for brain diseases. Digital subtraction angiography (DSA) is known as the "gold standard" for carotid stenosis diagnosis. It is commonly used to identify carotid artery stenosis and measure morphological indices of the stenosis. However, using deep learning to detect stenosis based on DSA images and further quantitatively predicting the morphological indices remain a challenge due the absence of prior work. In this paper, we propose a quantitative method for predicting morphological indices of carotid stenosis. Methods: Our method adopts a two-stage pipeline, first locating regions suitable for predicting morphological indices by object detection model, and then using a regression model to predict indices. A novel Carotid Artery Stenosis Matching (CASMatching) strategy is introduced into the object detection to model the matching relationship between a stenosis and multiple normal vessel segments. The proposed Match-ness branch predicts a Match-ness score for each normal vessel segment to indicate the degree of matching to the stenosis. A novel Direction Distance-IoU (2DIoU) loss based on the Distance-IoU loss is proposed to make the model focused more on the bounding box regression in the direction of vessel extension. After detection, the normal vessel segment with the highest Match-ness score and the stenosis are intercepted from the original image, then fed into a regression model to predict morphological indices and calculate the degree of stenosis. Results: Our method is trained and evaluated on a dataset collected from three different manufacturers' monoplane X-ray systems. The results show that the proposed components in the object detector substantially improve the detection performance of normal vascular segments. For the prediction of morphological indices, our model achieves Mean Absolute Error o
We propose a novel hierarchical sliding slice regression which in a coarse-to-fine manner represents global circular target space with a number of ordinally localized and overlapping subspaces. Our method is particula...
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We propose a novel hierarchical sliding slice regression which in a coarse-to-fine manner represents global circular target space with a number of ordinally localized and overlapping subspaces. Our method is particularly suitable for visual regression problems where the regression target is circular (e.g., car viewing angle) and visual similarity inconsistent over the target space (e.g., repetitive appearance). A good application example is the camera-based car viewing angle estimation problem, where visual similarity of different views is highly inconsistent-front and back views and left and right side views are pairwise similar, but appear at the far ends of the circular view angle space. In practice, the problem is even more complicated due to large visual variation of objects (e.g., different car models). We perform extensive experiments on the Lausanne Federal of Institute of Technology Multi-view Car and KITTI Data Sets as well as the Technische Universitat Darmstadt Multi-view Pedestrians Data Set and achieve superior performance as compared to the state-of-the-art algorithms.
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