PPT-Pertemuan-2: IMAGE PROCESSING

KK Komputasi dan Kecerdasan Buatan Teknik Komputer Universitas Komputer IndonesiaUNIKOM John Adler Wednesday September 19 2012 1 TK 37404 Pengolahan Citra Bahan. Overview. Images. Pixel Filters. Neighborhood Filters. Dithering. Image as a Function. We can think of an . image . as a function, . f. , . f:. . R. 2. . . . R. f . (. x, y. ). . gives the . intensity. Processing & Machine Vision. By:. Dr. Rajeev Srivastava. Associate Professor, CSE, IIT(BHU), Varanasi. Fundamentals of . Digital Image Processing. • Applications of image processing. • What's an image?. Jayanthi. . Sivaswamy. . and. . Suryakanth. V . Gangashetty. 2. Broad Areas of . Signal . Processing . Activities. Audio, speech and language . Medical image processing . Computer vision (image and video). r. s. t. u. v. w. x. y. z. Origin. x. y. Image f (x, y). e. processed. = . v. *e + . . r. *a + . s. *b + . t. *c + . . u. *d + . w. *f + . . x. *g + . y. *h + . z. *. i. Filter. Simple 3*3. Neighbourhood. Islamic Economics. Gunadarma. University. 2. nd. . Meeting. Prepared by: . Izzani. . Ulfi. Assessment. Kehadiran. : 15%. Tugas. 1 : 15 % . dikumpul. . pertemuan. . ketiga. Tugas. 2 : 15 % . dikumpul. 1. Image. Rich info. from visual data. Examples of images around us. natural photographic images; . . artistic and engineering drawings. scientific images (satellite, medical, etc.). “Motion pictures” => video. Eric Rees, Clemens Kaminski, Miklos Erdelyi, Dan Metcalf, Alex Knight. Laser Analytics Group. , . University of Cambridge . & . Biotechnology Group. , . National Physical Laboratory. Contents. Introduction. Dosen. : Muhammad Fachrie, M.Cs.. 1. Prakt. BasDat - Pertemuan 1. Biodata Dosen. Nama lengkap : Muhammad Fachrie. No. HP : 081373478938. Email : fachrie.sukses@gmail.com. Pendidikan : S1 T. Informatika, Telkom University. Mohammadhossein . Behgam. Agenda. Need for parallelism. Challenges. Image processing algorithms. Data handling & Load Balancing. Communication cost & performance. What is the problem?. Image Processing applications can be very computationally demanding due to:. . Pertemuan. 01 - 14. Ratna. . Cahaya. – M. . Rizaldi. – . Zamzami. A.. 12.02. 2014. Kehadiran. Hadir. . tepat. . waktu. di . kelas. . . Toleransi. . maksimal. 15 . menit. . . Jika. . Andrew Schoen. Center for Healthy Minds at the Waisman Center. University of Wisconsin - Madison. What do we do, . What this looks like:. Waisman. Center. Center for Healthy Minds / MIDUS Study. Wide variety of assessments. Divya Spandana . Marneni. Agenda. What is Big Data. Big Data and image processing. Why to analyze big images. Complexity involved in processing. Hadoop Image processing framework. Image Retrieval in big data. The relevant features for the examination task are enhanced. The irrelevant features for the examination task are removed/reduced. Here the input and output image are both digital image in color or gray scale.. Mor . soffer. Instructors: prof. Naftali . Lazarovitch. , prof. . Ofer. . Hadar. Schedule. Introduction and motivation. Our method. Data collection . Data description. Proposed method. Results and conclusions.