Using Image Processing Techniques to Estimate the Air Quality

The color of the sky varies depending on the composition of the air. The particles lingering in the atmosphere scatter the components of the light based on their wavelength in relation to particle size. Thus, the color of the sky depends on what wavelengths are scattered while the light travels through our atmosphere. By analyzing the color of the sky using computer image processing, it is possible to determine the quality of the air in an area. It is possible to classify pictures of the sky into clean or polluted air and have a computer estimate the category any other picture belongs to

Digital picture processing and psychophysics: a study of brightness perception

technical reportA computer driven display system was used to study brightness contrast phenomena, in a project motivated by research in digital picture processing. The modeling approach was that of Stockham and Davidson: the visual system is modeled as the cascade of a linear system (eye optics) and a multiplicative homomorphic system?that is, a logarithmic transformation (retinal receptors), followed by a linear system (neural interaction). In order to test the linearity of neural interaction, smooth stimulus patterns were utilized, containing only a few sinusoidal components within the low frequency band, and exhibiting classical brightness contrast effects (Mach bands, simultaneous brightness contrast, Hermann grid effect). Data were collected from brightness matching experiments with these smooth patterns. The data were verified in preliminary experiments on similar patterns digitally processed by the inverse of the model, in order to obtain cancellation of the brightness contrast effects. The experimental results showed to be in agreement with Davidson's data, obtained by a fundamentally different method. This new experimental approach indicated that the hypothesis of linearity of neural interaction is justified for smooth patterns. Further studies suggested that intensity edges and contours cause strong departure from linearity. Some steps were also taken toward extending the homomorphic model for color contrast phenomena. Conclusions are drawn about the implications of these experiments in the fields of computer image processing and visual psychophysics. The advantages of computer techniques in visual experiments are presented; the applications of the homomorphic model of brightness perception to digital picture processing are reviewed, and the implications of the experimental findings are discussed

Perancangan Object Tracking Robot Berbasis Image Processing Menggunakan Raspberry Pi

Object tracking is a technique in computer vision field that used to tracking on a moving object. Object tracking is done by image processing techniques through a combination of complex algorithm. This process gives the computer ability to know the movement of particular object. This study designs a robot that can follow the movement of a ball with diameter is 6.5 cm with particular color. Image processing in this study is convertion of RGB to HSV color space, color filtering, edge detector, and circle hough transform. Image processing and motion control of robot using Raspberry Pi 2 Model B mini computer and Raspberry Pi camera. Language programming that use is python with OpenCV library. OpenCV library is used to do all things related for image processing. The mechanical system using a pan tilt as camera driver to get flexibility in following the movement of ball. Robot’s body move using a pair of DC motors. The test that have been done is influence the resolution of picture on the camera motion system, robot visibility, color recognition, ball’s shape recognition, the speed when tracking, and minimum light intensity. The result show that robot can tracking a ball using the best image resolution in 320x240 pixel, it has a maximum visibility is 113 cm, maximum speed of object follow the movement is 22.6 cm/sec and it can recognize ball in minimum light intensity is 21.0 lux

Laser Conoscopy Study of Optical Anomalies in Uniaxial Crystals

New experimental possibilities of detecting fine optical anomalies in uniaxial crystals are demonstrated on a level with numerical estimation of refractive index and mechanical stress variations that cause distortions of the optical indicatrix. New possibilities are due to the use of an exact equation for isochromes derived for uniaxial crystals without mathematical simplifications commonly used by other authors. It allows one to calculate and graphically reproduce the theoretical form of isochromes of any orders in the conoscopic picture of an ideal crystal with known principal refractive indices, the thickness and orientation of the crystal surfaces, and also the wavelength of the radiation and the parameters of the optical circuit. A computer comparison of the theoretical image with an experimental conoscopic picture of a real crystal, fixed by a color digital camera on a semitransparent screen, is performed. The data on the variations of refractive indices and mechanical stresses in the crystal are retrieved from the mathematical processing of differences in the conoscopic images. The applications of the proposed method for the analysis of optical homogeneity of paratellurite and lithium niobate single crystals are presented. Keywords: method of conoscopy, isochromes, piezo-optic effec

Automating the construction of scene classifiers for content-based video retrieval

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering

Digital Image Processing

In recent years, digital images and digital image processing have become part of everyday life. This growth has been primarily fueled by advances in digital computers and the advent and growth of the Internet. Furthermore, commercially available digital cameras, scanners, and other equipment for acquiring, storing, and displaying digital imagery have become very inexpensive and increasingly powerful. An excellent treatment of digital images and digital image processing can be found in Ref. [1]. A digital image is simply a two-dimensional array of finite-precision numerical values called picture elements (or pixels). Thus a digital image is a spatially discrete (or discrete-space) signal. In visible grayscale images, for example, each pixel represents the intensity of a corresponding region in the scene. The grayscale values must be quantized into a finite precision format. Typical resolutions include 8 bit (256 gray levels), 12 bit (4096 gray levels), and 16 bit (65536 gray levels). Color visible images are most frequently represented by tristimulus values. These are the quantities of red, green, and blue light required, in the additive color system, to produce the desired color. Thus a so-called “RGB” color image can be thought of as a set of three “grayscale” images — the first representing the red component, the second the green, and the third the blue. Digital images can also be nonvisible in nature. This means that the physical quantity represented by the pixel values is something other than visible light intensity or color. These include radar cross-sections of an object, temperature profile (infrared imaging), X-ray images, gravitation field, etc. In general, any two-dimensional array information can be the basis for a digital image. As in the case of any digital data, the advantage of this representation is in the ability to manipulate the pixel values using a digital computer or digital hardware. This offers great power and flexibility. Furthermore, digital images can be stored and transmitted far more reliably than their analog counterparts. Error protection coding of digital imagery, for example, allows for virtually error-free transmission

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio