{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T06:12:39Z","timestamp":1648879959288},"reference-count":6,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2004,6]]},"abstract":"<jats:p> A retina has a space-variant sampling mechanism and an orientation-sensitive mechanism. The space-variant sampling mechanism of the retina is called Retinotopic Sampling (RS). With these mechanisms, the object-detection is formulated as finding an appropriate coordinate transformation from a coordinate system on the input image to the retina. The appropriate coordinate transformation is found using maximum likelihood method. By using the model based on RS, we formulate a kernel function as an analytical function of the information on the input image, the position and the size of the object in the input image. Then the object-detection is realised as a gradient decent method for a discriminant function trained by Support Vector Machine (SVM). This detection mechanism realises faster detection than exploring a visual scene in raster-like fashion. The discriminant function outperforms results of SVMs using a kernel function using intensities of all pixels (based on independently published results), in face detection experiments over test images in the MIT-CBCL face database. <\/jats:p>","DOI":"10.1142\/s1469026804001185","type":"journal-article","created":{"date-parts":[[2004,9,17]],"date-time":"2004-09-17T10:44:54Z","timestamp":1095417894000},"page":"115-130","source":"Crossref","is-referenced-by-count":0,"title":["A NON-PARAMETRIC TRAINABLE OBJECT-DETECTION MODEL USING A CONCEPT OF RETINOTOPIC SAMPLING"],"prefix":"10.1142","volume":"04","author":[{"given":"HIROTAKA","family":"NIITSUMA","sequence":"first","affiliation":[{"name":"Kwansei Gakuin University, 2-1 Gakuen Sanda 669-1337, Japan"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf3","author":"Collobert R.","journal-title":"JMLR"},{"key":"rf4","author":"Dietterich T. G.","journal-title":"NIPS"},{"key":"rf6","author":"Itti L.","journal-title":"Syst. J. Electron. Imaging"},{"key":"rf7","first-page":"487","volume":"11","author":"Jaakkola T. S.","journal-title":"NIPS"},{"key":"rf15","author":"Smeraldi F.","journal-title":"Pattern, Recognition Lett."},{"key":"rf16","author":"Tao H.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026804001185","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T00:27:05Z","timestamp":1565137625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026804001185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,6]]},"references-count":6,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2011,11,20]]},"published-print":{"date-parts":[[2004,6]]}},"alternative-id":["10.1142\/S1469026804001185"],"URL":"https:\/\/doi.org\/10.1142\/s1469026804001185","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004,6]]}}}