PPT-CS448f: Image Processing For Photography and Vision

Graph Cuts Seam Carving Video Make images smaller by removing seams Seam connected path of pixels from top to bottom or left edge to right edge Dont want to remove important stuff. fiuedu Abstract Image Processing Algorithms are the basis for Image Computer Analysis and Machine Vision Employing a theoretical foundation Image Algebra and powerful development tools Visual C Visual Fortran Visual Basic and Visual Java highleve Sharpening. Sharpening. Boost detail in an image without introducing noise or artifacts. Undo blur. due to lens aberrations. slight misfocus. Recall Denoising. Input. =. Signal. +. Noise. Denoising. How goes the assignment?. The course so . far…. We have a fair idea what image processing code looks like. We know how to treat an image as a continuous function. We know how to warp images. Tone Mapping. So far. So far. Tone Mapping. Some Images have too much dynamic range to display on a slide:. (belgium.hdr). Recall Sharpening. Input. =. Coarse +. Fine. Tone Mapping. Input. Blending and Pyramids. Blending. We’ve aligned our images. What now?. Averaging. Weighted averaging. min/max/median. Noise . reduction by Averaging. 2 Shots. 4 Shots. 8 Shots. 16 Shots. Noise Reduction by Averaging. Lecture 1. Today:. Introductions. Course Format. Course Flavor. Low-level basic image processing. High-level algorithms from . Siggraph. Introductions and Course Format. http://cs448f.stanford.edu/. Some Background Qs. Tone Mapping. So far. So far. Tone Mapping. Some Images have too much dynamic range to display on a slide:. (belgium.hdr). Recall Sharpening. Input. =. Coarse +. Fine. Tone Mapping. Input. Hel-Or . . toky@idc.ac.il . Image Processing. Spring 2010. 2. Administration. Pre-requisites / prior knowledge. Course Home Page:. http://. www1.idc.ac.il/toky/ImageProc-10. “What’s new” . Lecture slides and handouts . Tone Mapping. So far. So far. Tone Mapping. Some Images have too much dynamic range to display on a slide:. (belgium.hdr). Recall Sharpening. Input. =. Coarse . Fine. Tone Mapping. Input. Sharpening. Sharpening. Boost detail in an image without introducing noise or artifacts. Undo blur. due to lens aberrations. slight misfocus. Recall Denoising. Input. =. Signal. . Noise. Blending and Pyramids. Blending. We’ve aligned our images. What now?. Averaging. Weighted averaging. min/max/median. Noise . reduction by Averaging. 2 Shots. 4 Shots. 8 Shots. 16 Shots. Noise Reduction by Averaging. Fast Filtering. Problems in Computer Vision. Computer Vision in One Slide. 1) Extract some features from some images . 2) Use these to formulate some . (hopefully linear) constraints. 3) Solve a . system of . Fast Filtering Continued. Filtering by . Resampling. This looks like we just zoomed a small image. Can we filter by . downsampling. then . upsampling. ?. Filtering by Resampling. Filtering by Resampling. Part I (Overview and Examples). 1. /56. 9/19/19. 9/19/19. IP is fundamental to both computer graphics and computer vision . Has its own publications and conferences. IEEE Transactions on Image Processing (TIP).