COMPUTER VISION TECHNIQUES
Computer vision is the science that develops the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analysed from an observed image, image set, or image sequence from computations made by special-purpose or general purpose computers (notes). The main goal of computer vision application is to produce automated recognition systems that can equivalent or eventually better than human performance. Computer vision can be used to allow new relations techniques and tie both the physical and virtual worlds. There are some techniques of computer vision that are used to produce the better quality of computer vision application.
Image Processing
Image processing is a technique in which the data from an image are digitized and various mathematical operations are applied to the data, generally with a digital computer, in order to create an enhanced image that is more useful or pleasing to a human observer, or to perform some of the interpretation and recognition tasks usually performed by human.
Image processing is separated into two levels: the lower-level processing and the higher-level processing. The lower –level processing takes image pixels as input and perform task such as image enhancement, feature extraction and image segmentation. The higher-level processing takes the output from the lower-level processing as an input and generates output related to the system. Example of tasks accomplished in the higher-level processing includes vehicle tracking (T., 1998).
In general, image processing operations can be categorized into four types (Y., 2010) which is:
1. Pixel operations: The output at a pixel depends only on the input at that pixel, independ...
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... and calculate the M medians of squared differences between corresponding points and transformed points. Then select the affine parameters for which the median of squared difference is the minimum.
According to the above procedure, the probability p that at least one data set in the background and their corresponding points are obtained is derived in following equation.
〖p(ε,q,M)=1-(1-((1-ε)q)〗^3) ͫ
Where ε(<0.5) is the ratio of the moving object regions to whole image and q is the probability that corresponding points are correctly find. This method will give an accurate and reliable model.
REFERENCES
Behard, A., Shahrokni, A., & Motanedi, S, A. (2002). A robust vision-based moving target detection and tracking system.
Kaukorata, T., & Smed, J. (2006). Role of pattern recognition platform.
T., L. C. (1998). Image processing and pattern recognition. Elsevier.
One image type is 2D computer graphics which are a computer based generation of digital images. 2D computer graphics are mostly used for typography, cartography, technical drawings, and advertising. These graphics were created in the 1950s and based off of vector graphics. 2D Models combine geometric models, digital images, text, and mathematical functions. Components of 2D graphics can be modified and manipulated by two-dimensional transformations like translation, rotation, and scaling. Complex 2D models can be built by combining simple objects (Peterson).
The flow of the operation starts from capturing the image, after which is the process image for detection of the borders as well as the golf balls. Using image processing base on the RGB values or the white and non-white image, the system is capable of distinguishing whether the object is a golf ball or not. The logic of the system is created using neural networks. The logic is programmed in such a way that the robot is able to determine how far the golf ball is and whether the object is actually a golf ball or not. The dimples of the golf ball are also one of the considerations they use in creating the logic of the system. The person is able to set the boundaries for the robot to move around. This is done by putting a boundary using blue or red tapes for the robot to sense. The border detection and avoidance is used to prevent the robot from going out of the prescribed area.
In this project, issues regarding the Hough Transform for line detection are considered. The first several sections deal with theory regarding the Hough transform, then the final section discusses an implementation of the Hough transform for line detection and gives resulted images. The program, images, and figures for this project are implemented using the Matlab.
...omated detection of lines and points in the images and the use of smart markers in reference video recordings.
In this section, the results of the research are presented. For each task carried out, the most important information obtained is presented.
...Variational Image Thresholding N. Ray, B.N. Saha Alberta Univ., Edmonton Proceedings / ICIP ... International Conference on Image Processing 01/2007; 6:VI - 37 - VI - 40. DOI:10.1109/ICIP.2007.4379515 ISBN: 978-1-4244-1437-6 In proceeding of: Image Processing, 2007. ICIP 2007. IEEE International Conference on, Volume: 6
...n the frequency space, it is required to enhance image transforming operations. Comaparing increment at this time might be expressed as:
the propose of image block motion Extraction is to use 2D image of foreground and background which extract the predominant lines and vanishing point of the background base on geometry method and motion, the background depth map is considered. The depth of the object have used the motion extraction to obtain the motion depth map and then divided according to the color of the object to give the object region segmentation depth map.
This approach includes two processes, training and classification (Chelali, Djeradi & Dejradi, 2009). In the training process, a subspace will be established by using the training samples, and then the training faces will be projected onto the same subspace. In the classification process, the input face image will be measured by Euclidean Distance to the subspace, and a decision will be made, either accept or reject.
Computer graphic is one essential part of the information technology (IT) field. There are three key elements that support the computer graphics such as data base, graphics algorithm and programming language (Yuwaldi, 2000). The use of computer graphics is beneficial in terms of fast drawing, better designing of a product, no repetition of the drawing work and quickly and efficiently transformation for object manipulation operations. Examples of graphical object manipulation are the scaling, translation, rotation and reflection (Riza and Yuwaldi, 2002). Object manipulation in computer graphics is also known as geometric transformation.
...ting the disparity map was based on belief propagation and mean shift segmentation [19]. The disparity map and the reference image (JI_L) are segmented into some objects. The objects and the average disparity of these objects are denoted by O_(JI_L)^i & d_(JI_L)^i, respectively, i = 1,2,…,m. If d_(JI_L)^i is in [D_b,D_f ], O_(JI_L)^i is regarded as the main content, O_(JI_L)^i∈O_maipart.If d_(JI_L)^iis not in [D_b,D_f ], O_(JI_L)^i is regarded as the background, O_(JI_L)^i∈O_background. That is,
Correlation- based method: It uses richer gray scale information. It overcome problems of above method, it can work with bad quality data. But it has some of its own problems like localization of points.
An image are two dimensional function T (p, q), where x and y are spatial (plane) coordinates (p, q). This coordinates are called gray level of the image. T, p and q are all finite and discrete quantities, the image is called a digital image [1].
The first characteristics of a digital image are its mode of color. A digital image may have any one of the three modes: grayscale, binary and color. A grayscale image is an image in which each pixel contains a single color i.e. shades of gray color. A binary image is an image in which each pixel contains two possible values i.e. either black or white. A color image is an image in which each pixel uses different color.
The final step is called rendering. During rendering, the computer calculates the effect of light, color, and texture on the model's surface. For a film or video, the computer will produce a two-dimensional digital picture of the characters for each frame of the animation. The computer artist usually adjusts many visual effects, such as camera focus and transparency, during the rendering phase.