Computer vision is a discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images in terms of the properties of the structures present in the scene. It combines the knowledge from computer science, electrical engineering, mathematics, physiology, biology and cognitive science in order to understand and simulate the operation of the human vision system. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract data from the images. As a technological discipline, computer vision seeks to apply its theories and model to the construction of computer vision systems.
Computer vision applications include biomedical, video analysis, scientific, surveillance, graphics, entertainment, games, document understanding, environment exploration and industrial. Computer vision systems have been designed that can inspect machine parts, control robots or autonomous vehicles, detect and recognise human faces, reconstruct large objects or portions from multiple photographs track suspicious objects or people in the videos, retrieve images from large database according to content, and more.
Each of the application areas described above employ a range of computer vision task, processing or measurement problems which can be solved using variety of methods. One of the fundamental problems in computer vision that takes place in many image processing applications is of image matching. Images of one scene may be taken from different viewpoints or may suffer transformations such as rotation, noise etc. So it is likely that two images of the same scene will be different. The task of finding similarity correspondences between two images of the same scene or object has ...
... middle of paper ...
...he database is scored and the top ten highest scoring logo images in the database are pairwise matched using ratio test and Random Sample Consensus (RANSAC). The database image with largest consensus set is matched with the input image. The algorithm proved to be noise-resilient, scale-invariant and rotation invariant up to +/- 10 degree.
In this report, the concepts of Speeded Up Robust Features algorithm, hierarchical K-means clustering, Term Frequency- Inverse Document Frequency weights and Random Sample Consensus are reviewed first and then the algorithm implemented in this project is discussed. In the experimental results, the accuracy of the algorithm is shown for images with noisy background, different scale size and inclined images. Last section of this report, concludes the proposed approach and refers to the future extensions of this project.
In this image, a sewage worker is seen cleaning the drainage system, with his bear hands, without the use of either any equipment’s or protection. On the first glace, the image depicts the idea of health risk, because the man is exposed to such contaminants, which for him is work. He is looking up from a dirty drain, covered in filth, which shows that he is clearly used as the subject of this image, whom we are engaged to more as he is making eye contact with its viewers. This picture only includes one person into the frame, as the other man’s face isn’t available to see in this picture, which is man that is holding the bucket. Holding a bucket either emphasise the idea that he is helping the sewage worker, either to get the dirt out or to put the dirt in the drainage system.
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.
...ge flow and pattern types, are prominent enough to align fingerprints directly. Nilsson [26] detected the core point by complex filters applied to the orientation field in multiple resolution scales, and the translation and rotation parameters are simply computed by comparing the coordinates and orientation of the two core points. Jain [27] predefined four types of kernel curves:first is arch, second is left loop ,third is right loop and fourth is whorl, each with several subclasses respectively. These kernel curves were fitted with the image, and then used for alignment. Yager [28] proposed a two stage optimization alignment combined both global and local features. It first aligned two fingerprints by orientation field, curvature maps and ridge frequency maps, and then optimized by minutiae. The alignment using global features is fast but not robust, because the
A common characteristic of most of the images is that the neighboring pixels are highly correlated and therefore contain superfluous information. I...
...omated detection of lines and points in the images and the use of smart markers in reference video recordings.
...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
...a, M. Pietikainen and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE Trans. PAMI, vol.24, no.7, pp.971-987, Jul 2002.
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.
In this process, user’s faces are acquired in order to extract out the facial features from cluttered background. In Robust Real-time Object Detection (P.Viola ,2002), the authors used AdaBoost algorithm to detect the frontal view of faces rapidly. The system able to detect the face from background quickly and compute the face features in a short time. However, the frontal view of faces cannot always guarantee appear in the environment, so some of the researchers have considered used side view plus frontal view to detect the faces. Besides that, this algorithm will fail to detect those faces with more than 10 degree rotation.
The way that each individual interprets, retrieves, and responds to the information in the world that surrounds you is known as perception. It is a personal way of creating opinions about others and ourselves in everyday life and being able to recognize it under various conditions. Each person’s perceptions are used as a kind of filter that every piece of information has to pass through before it determines the effect that it has or will have on the person from the stimulus. It is convincing to believe that we create multiple perceptions about different situations and objects each day. Perceptions reflect our opinions in many ways. The quality of a person’s perceptions is very important and can affect the response that is given through different situations. Perception is often deceived as reality. “Through perception, people process information inputs into responses involving feelings and action.” (Schermerhorn, et al.; p. 3). Perception can be influenced by a person’s personality, values, or experiences which, in turn, can play little role in reality. People make sense of the world that they perceive because the visual system makes practical explanations of the information that the eyes pick up.
Image intensification is the process of converting x-ray into visible light. “Early fluoroscopic procedures produced visual images of low intensity, which required the radiologist's eyes to be dark adapted and restricted image recording. In the late 1940s, with the rapid developments in electronics and borrowing the ideas from vacuum tube technology, scientists invented the x-ray image intensifier, which considerably brightened fluoroscopic images” (Wang & Blackburn, 2000, np). We will explore the image-intensification tube, the various gain parameters associated with the tube, and the magnification mode of the image intensifier.
2. Face recognition: Face recognition is based on both the shape and location of the eyes, eyebrows, nose, lips and chin. It is non intrusive method and very popular also. Facial recognition is carried out in two ways ...
Oftentimes, a person can look at an image and draw a conclusion about it, only to find out later that he or she was incorrect. This phenomenon is due to what is called an optical illusion, in which an image is perceived incorrectly to be something else. This leads to the questions, why do optical illusions occur, and what can be done about them?
Today, artificial intelligence techniques have found application area in many areas. The most often techniques which are investigated and used, are:
There is a wide spectrum of applications, from different security systems for crime prevention and investigation to commercial and private use. For example, doors that open automatically have existed for a long time. To save energy, if a smart camera is used instead of a simple motion detector, the camera can choose to open the door if a person is approaching or leave it closed if a person is just walking by the door. One of the most sophisticated tools for smart cameras is a method called facial recognition.