2,054 research outputs found
Deep Contrast Learning for Salient Object Detection
Salient object detection has recently witnessed substantial progress due to
powerful features extracted using deep convolutional neural networks (CNNs).
However, existing CNN-based methods operate at the patch level instead of the
pixel level. Resulting saliency maps are typically blurry, especially near the
boundary of salient objects. Furthermore, image patches are treated as
independent samples even when they are overlapping, giving rise to significant
redundancy in computation and storage. In this CVPR 2016 paper, we propose an
end-to-end deep contrast network to overcome the aforementioned limitations.
Our deep network consists of two complementary components, a pixel-level fully
convolutional stream and a segment-wise spatial pooling stream. The first
stream directly produces a saliency map with pixel-level accuracy from an input
image. The second stream extracts segment-wise features very efficiently, and
better models saliency discontinuities along object boundaries. Finally, a
fully connected CRF model can be optionally incorporated to improve spatial
coherence and contour localization in the fused result from these two streams.
Experimental results demonstrate that our deep model significantly improves the
state of the art.Comment: To appear in CVPR 201
Video Deinterlacing using Control Grid Interpolation Frameworks
abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics.Dissertation/ThesisM.S. Electrical Engineering 201
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