This module extends the ideas of the Discrete Fourier Transform (DFT) into two-dimensions, which is necessary for any image processing.

Final results for astronomical image processing using Weiner filtering and weighted averaging.

Astronomical Image Deconvolution: Image Processing with Weiner Filter. The complete image processing technique.

This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data.

A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors and processing. In this course we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks. If novel cameras can be designed to sample light in radically new ways, then rich and useful forms of visual information may be recorded — beyond those present in traditional photographs. Furthermore, if computational process can be made aware of these novel imaging models, them the scene can be analyzed in higher dimensions and novel aesthetic renderings of the visual information can be synthesized.We will discuss and play with thermal cameras, multi-spectral cameras, high-speed, and 3D range-sensing cameras and camera arrays. We will learn about opportunities in scientific and medical imaging, mobile-phone based photography, camera for HCI and sensors mimicking animal eyes. We will learn about the complete camera pipeline. In several hands-on projects we will build physical imaging prototypes and understand how each stage of the imaging process can be manipulated.

Foundations subject in modern software development techniques for engineering and information technology. Covers the design and development of component-based software (using C# and .NET); data structures and algorithms for modeling, analysis, and visualization; basic problem-solving techniques; web services; and the management and maintenance of software. Includes a treatment of topics such as sorting and searching algorithms; and numerical simulation techniques. Foundation for in-depth exploration of image processing, computational geometry, finite element methods, network methods and e-business applications.

" This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment. Additional Faculty Div Bolar Dr. Bradford Dickerson Dr. John Gabrieli Dr. Doug Greve Dr. Karl Helmer Dr. Dara Manoach Dr. Jason Mitchell Dr. Christopher Moore Dr. Vitaly Napadow Dr. Jon Polimeni Dr. Sonia Pujol Dr. Bruce Rosen Dr. Mert Sabuncu Dr. David Salat Dr. Robert Savoy Dr. David Somers Dr. A. Gregory Sorensen Dr. Christina Triantafyllou Dr. Wim Vanduffel Dr. Mark Vangel Dr. Lawrence Wald Dr. Susan Whitfield-Gabrieli Dr. Anastasia Yendiki "

The Image Composite Explorer is designed to be an easy first step into the realm of Earth system science, image processing, data analysis, and satellite remote sensing via your Web browser. Click to read About ICE and the rationale for its design; for an in-depth tutorial, read the ICE Users Guide; or jump right in to the Channel Islands example if you prefer to learn using a hands-on approach. A Teacher’s Guide is available for educators who wish to use ICE in their classrooms.

Sparse approximation, defined as the practice of representing a given signal as a summation of elements from a dictionary of elementary signals, has traditionally only involved one basis - the canonical basis in which we perceive the world, the Fourier basis that is the foundation of the frequency domain, or the dct basis that is behind the modern JPEG image format. However, recent thought has suggested that more accurate, faster methods for sparse approximation may instead be derived from a "combinational" basis, ie, a basis that consists of two or more bases concatenated onto each other. This resultant basis is often called an "overcomplete" or "redundant" basis, as there are always more vectors in the basis than the magnitude of the dimension of the space they span. Since they are redundant in this effect, the immediate problem would seem to be that there are then an infinite number of representations for any vector, or signal, in a space. Modern theory suggests that there are ideal algorithms for determining these transformations, in terms of number of computations and sparsity of the resultant representation; the two most prevalent being Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP).

This module introduces image processing, 2D convolution, 2D sampling and 2D FTs.

A project by Rice University students done in Fall 2004 for ELEC 301. An investigation of signal processing techniques applied to iris recognition.

This site provides a number of Landsat 7 scene subsets as LAN files that are intended for use with Purdue University's MultiSpec software. Users also have the option of downloading the Landsat images as TIFF files in four different band combinations. Links are included to download Multi-Spec, a MultiSpec tutorial, and an introduction to remote-sensing PowerPoint presentation with detailed notes.

Principles of operation, algorithms, applications, and limitations of optical detection, storage, processing, transmission and display devices and systems. Topics: review of basic properties of electromagnetic waves; holography; spatial light modulator and display devices; thermal and quantum photodetectors; optical storage media such as disks and 3-D holographic materials; fiberoptic communication systems; optical interconnection device technologies; coherent and incoherent light processors based on Fourier optics, Acousto-optics, and optoelectronic neural networks; role of optics in next-generation computers; applications to image processing, pattern recognition, radar systems and adaptive optics; limitations of optical processors.

This is an Elec 301 end-of-year project. It involves the optimization of fingerprint recognition through different procedures such as convolution and matched filtering. Also included are the classification of different fingerprints according to their composition of lines, arches, and/or swirls. This classification is used in conjunction with the aforementioned procedures and other techniques such as deblurring.

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Lezione introduttiva sulla utilizzazione del linguaggio e ambiente Processing per l'insegnamento dell'elaborazione di media e dell'interaction design.

Introductory lecture on the use of the "processing" language and environment for teaching media processing and interaction design.