{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:21:45Z","timestamp":1760955705843},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,3,15]],"date-time":"2017-03-15T00:00:00Z","timestamp":1489536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2017,3,15]],"date-time":"2017-03-15T00:00:00Z","timestamp":1489536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["IPSJ T Comput Vis Appl"],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper proposes a deep learning-based efficient and compact solution for road scene segmentation problem, named deep residual coalesced convolutional network (RCC-Net). Initially, the RCC-Net performs dimensionality reduction to compress and extract relevant features, from which it is subsequently delivered to the encoder. The encoder adopts the residual network style for efficient model size. In the core of each residual network, three different convolutional layers are simultaneously coalesced for obtaining broader information. The decoder is then altered to upsample the encoder for pixel-wise mapping from the input images to the segmented output. Experimental results reveal the efficacy of the proposed network over the state-of-the-art methods and its capability to be deployed in an average system.<\/jats:p>","DOI":"10.1186\/s41074-017-0020-9","type":"journal-article","created":{"date-parts":[[2017,3,15]],"date-time":"2017-03-15T12:04:25Z","timestamp":1489579465000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep residual coalesced convolutional network for efficient semantic road segmentation"],"prefix":"10.1186","volume":"9","author":[{"given":"Igi","family":"Ardiyanto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teguh Bharata","family":"Adji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,15]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Yang Y, Li Z, Zhang L, Murphy C, Hoeve JV, Jiang H (2012) Local label descriptor for example based semantic image labeling In: Proc. of European Converence on Computer Vision (ECCV), 361\u2013375.","DOI":"10.1007\/978-3-642-33786-4_27"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Sturgess P, Alahari K, Ladicky L, H.S.Torr P (2009) Combining appearance and structure from motion features for road scene understanding In: Proc. of British Machine Vision Conferenve (BMVC).","DOI":"10.5244\/C.23.62"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Ladicky L, Sturgess P, Alahari K, Russell C, Torr PHS (2010) What, where and how many? 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