- Read more about Automatic Defect Segmentation by Unsupervised Anomaly Learning
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This paper addresses the problem of defect segmentation in semiconductor manufacturing. The input of our segmentation is a scanning-electron-microscopy (SEM) image of the candidate defect region. We train a U-net shape network to segment defects using a dataset of clean background images. The samples of the training phase are produced automatically such that no manual labeling is required. To enrich the dataset of clean background samples, we apply defect implant augmentation. To that end, we apply a copy-and-paste of a random image patch in the clean specimen.
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When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minutes) is essential not to miss the cause of flooding in local regions. In this paper, we study optical flow-based interpolation of globally available weather radar images from satellites.
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- Read more about Fine-Grained Dynamic Loss for Accurate Single-Image Super-Resolution
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- Read more about LEARNING TO FUSE HETEROGENEOUS FEATURES FOR LOW-LIGHT IMAGE ENHANCEMENT
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In this paper, an approach to self-calibrate an outward-looking camera from camera images is presented. Ego lane boundaries are detected in the image frame. A straight line is fitted to each detected boundary. Vanishing point in image space is computed as the intersection of the fitted straight lines. A closed-form solution is obtained for camera pitch and yaw angles using vanishing points coordinates.
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- Read more about BALANCED STRIPE-WISE PRUNING IN THE FILTER
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- Read more about STATISTICAL, SPECTRAL AND GRAPH REPRESENTATIONS FOR VIDEO-BASED FACIAL EXPRESSION RECOGNITION IN CHILDREN
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- Read more about STATISTICAL, SPECTRAL AND GRAPH REPRESENTATIONS FOR VIDEO-BASED FACIAL EXPRESSION RECOGNITION IN CHILDREN
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- Read more about STATISTICAL, SPECTRAL AND GRAPH REPRESENTATIONS FOR VIDEO-BASED FACIAL EXPRESSION RECOGNITION IN CHILDREN
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- Read more about Extracting and Distilling Direction-adaptive Knowledge for Lightweight Object Ddetection in Remote Sensing Images
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Recently, some lightweight convolutional neural network (CNN) models have been proposed for airborne or spaceborne remote sensing object detection (RSOD) tasks. However, these lightweight detectors suffer from performance degradation due to the compromise of limited computing resources on embedded devices. In order to narrow this performance gap, a direction-adaptive knowledge extraction and distillation (DKED) method is proposed.
Poster_Paper8521.pdf
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