- Read more about A deep neural network for oil spill semantic segmentation in SAR images
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Oil spills pose a major threat of the oceanic and coastal environments, hence, an automatic detection and a continuous monitoring system comprises an appealing option for minimizing the response time of relevant operations. Numerous efforts have been conducted towards such solutions by exploiting a variety of sensing systems such as satellite Synthetic Aperture Radar (SAR) which can identify oil spills over sea surfaces in any environmental conditions and operational time. Such approaches include the use of artificial neural networks which effectively identify the polluted areas.
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In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods.
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This paper proposes a novel approach for colorizing near infrared (NIR) images using a S-shape network (SNet). The proposed approach is based on the usage of an encoder-decoder architecture followed with a secondary assistant network. The encoder-decoder consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. The assistant network is a shallow
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- Read more about DEPTH ESTIMATION NETWORK FOR DUAL DEFOCUSED IMAGES WITH DIFFERENT DEPTH-OF-FIELD
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In this work, we propose an algorithm to estimate the depth map of a scene using defocused images. In particular, the depth map is estimated using two defocused images with different depth-of-field for the same scene. Similar to the approach of the general depth from defocus (DFD), the proposed algorithm obtains the depth information from the
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- Read more about Class-specific Coders for Hyper-spectral Image Classification
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In this paper, we introduce the paradigm of class specific
coders (CSC) for classification of hyperspectral images
(HSI). Apparently, CSC are defined as a set of distinct
encoder-decoder (henceforth called a coder) networks where
a given coder is trained on the samples of a particular class.
In contrast to auto-encoders (AE) which learn an identity
mapping of data in an unsupervised fashion, the CSC model,
on the other hand, learns re-constructive mappings for all
possible pairs of training samples for each class in separate
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- Read more about IMAGE STITCHING FOR DUAL FISHEYE CAMERAS
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Panoramic photography creates stunning immersive visual experiences for viewers. In this paper, we investigate how to seamlessly stitch a pair of images captured by two uncalibrated, back-to-back, 195-degree fisheye cameras to generate a surround view of a 3D scene. It is a challenging task because the two camera centers are displaced and because the common region is the most distorted area. To enhance the robustness of feature matching and hence the quality of stitching, we propose a novel technique that projects the image rectilinearly onto an equirectangular plane.
Poster.pdf
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- Read more about S3D: Stacking Segmental P3D for Action Quality Assessment
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Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.
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- Read more about DENSE BYNET: RESIDUAL DENSE NETWORK FOR IMAGE SUPER RESOLUTION
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This paper proposes a method, Dense ByNet, for single image super-resolution based on a convolutional neural network (CNN). The main innovation is a new architecture that combines several CNN design choices. Using a residual network as a basis, it introduces dense connections inside residual blocks, significantly reducing the number of parameters. Second, we apply dilation convolutions to increase the spatial context. Lastly, we propose modifications to the activation and cost functions.
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- Read more about SuperCut: Superpixel Based Foreground Extraction With Loose Bounding Boxes in One Cutting
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Interactive image segmentation that uses a bounding box containing the foreground has gained great popularity because of its convenience. However, its performance is often degraded when the bounding box is not tight enough or covers large background regions. To solve this problem, this paper proposes a novel segmentation algorithm called ``SuperCut". This algorithm provides robust segmentation in one cut even with loose bounding boxes.
Poster.pdf
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