Search results for: Jiasheng Hao

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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Jiasheng Hao"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Jiasheng Hao</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Visual Search Based Indoor Localization in Low Light via RGB-D Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Yali%20Zheng">Yali Zheng</a>, <a href="https://publications.waset.org/search?q=Peipei%20Luo"> Peipei Luo</a>, <a href="https://publications.waset.org/search?q=Shinan%20Chen"> Shinan Chen</a>, <a href="https://publications.waset.org/search?q=Jiasheng%20Hao"> Jiasheng Hao</a>, <a href="https://publications.waset.org/search?q=Hong%20Cheng"> Hong Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Indoor%20navigation" title="Indoor navigation">Indoor navigation</a>, <a href="https://publications.waset.org/search?q=low%20light" title=" low light"> low light</a>, <a href="https://publications.waset.org/search?q=RGB-D%20camera" title=" RGB-D camera"> RGB-D camera</a>, <a href="https://publications.waset.org/search?q=vision%0D%0Abased." title=" vision based."> vision based.</a> </p> <a href="https://publications.waset.org/10006850/visual-search-based-indoor-localization-in-low-light-via-rgb-d-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006850/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006850/bibtex" target="_blank" rel="nofollow" class="btn 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Search results for: Jiasheng Hao