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themes/hugo-academic-master/exampleSite/content/publication/_index.md

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+title = "Publications"
+date = "2017-01-01T00:00:00Z"
+math = false
+highlight = false
+
+# Optional featured image (relative to `static/img/` folder).
+[header]
+image = ""
+caption = ""
+
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themes/hugo-academic-master/exampleSite/content/publication/clothing-search.md

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+abstract = "We present a mobile visual clothing search system whereby a smart phone user can either choose a social networking photo or take a new photo of a person wearing clothing of interest and search for similar clothing in a retail database. From the query image, the person is detected, clothing is segmented, and clothing features are extracted and quantized. The information is sent from the phone client to a server, where the feature vector of the query image is used to retrieve similar clothing products from online databases. The phone's GPS location is used to re-rank results by retail store location. State of the art work focuses primarily on the recognition of a diverse range of clothing offline and pays little attention to practical applications. Evaluated on a challenging dataset, the system is relatively fast and achieves promising results."
+abstract_short = "A mobile visual clothing search system is presented whereby a smart phone user can either choose a social networking image or capture a new photo of a person wearing clothing of interest and search for similar clothing in a large cloud-based ecommerce database. The phone's GPS location is used to re-rank results by retail store location, to inform the user of local stores where similar clothing items can be tried on."
+authors = ["GA Cushen", "MS Nixon"]
+date = "2013-07-01"
+image_preview = ""
+math = true
+publication_types = ["1"]
+publication = "In *International Conference on Multimedia and Expo Workshops (ICMEW)*, IEEE."
+publication_short = "In *ICMEW*"
+selected = true
+title = "Mobile visual clothing search"
+url_code = "#"
+url_dataset = "#"
+url_pdf = "http://eprints.soton.ac.uk/352095/1/Cushen-IMV2013.pdf"
+url_project = "project/deep-learning/"
+url_slides = "#"
+url_video = "#"
+
+[[url_custom]]
+name = "Custom Link"
+url = "http://www.example.org"
+
+# Optional featured image (relative to `static/img/` folder).
+[header]
+image = "headers/bubbles-wide.jpg"
+caption = "My caption :smile:"
+
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+
+More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code.

themes/hugo-academic-master/exampleSite/content/publication/person-re-identification.md

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+abstract = "Person re-identification is a critical security task for recognizing a person across spatially disjoint sensors. Previous work can be computationally intensive and is mainly based on low-level cues extracted from RGB data and implemented on a PC for a fixed sensor network (such as traditional CCTV). We present a practical and efficient framework for mobile devices (such as smart phones and robots) where high-level semantic soft biometrics are extracted from RGB and depth data. By combining these cues, our approach attempts to provide robustness to noise, illumination, and minor variations in clothing. This mobile approach may be particularly useful for the identification of persons in areas ill-served by fixed sensors or for tasks where the sensor position and direction need to dynamically adapt to a target. Results on the BIWI dataset are preliminary but encouraging. Further evaluation and demonstration of the system will be available on our website."
+abstract_short = ""
+authors = ["GA Cushen"]
+date = "2015-09-01"
+image_preview = ""
+math = true
+publication_types = ["2"]
+publication = "In *Signal Image Technology & Internet Systems (SITIS)*, IEEE."
+publication_short = ""
+selected = false
+title = "A Person Re-Identification System For Mobile Devices"
+url_code = ""
+url_dataset = ""
+url_pdf = "http://arxiv.org/pdf/1512.04133v1"
+url_project = "project/deep-learning/"
+url_slides = ""
+url_video = ""
+
++++
+
+More detail can easily be written here using *Markdown* and $\rm \LaTeX$ math code.