Computer rotoscoping with the aid of color recognition

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1980.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.Includes bibliographical references (leaves 40-42).Rotoscoping is explored as a computer animation technique. The optical videodisc serves as the image storage and input source. Image processing and tablet painting routines are applied to digitized frames. "Color recognition", the exploitation of digital color information, enables the tracking of objects, from frame to frame, based on their color. This system allows for semi-automatic, selective processing of images.by Rebecca Allen.M.S

Using Color in Machine Vision Systems for Wood Processing

Color information, already shown to be valuable in distinguishing wood surface features, should prove especially useful for future applications of machine vision in the wood products industry. This review provides investigators interested in such applications with the information necessary for understanding the benefits-and associated difficulties-of using color. Various standard color-measurement systems ("color spaces") are discussed. No one system has been completely successful, at least partly because simple physical measurements are difficult to correlate with a human observer's complex perception of color. Color video camera systems, designed with human viewers in mind, have the potential for machine vision applications, but certain system "features" (white balance, gamma or contour correction) could cause problems. Future applications, including detecting and classifying hard-to-identify defects and matching colors of wood components, will require careful choice of lighting geometry and source, camera system, and color space for the purpose at hand

Specular reflection removal and bloodless vessel segmentation for 3-D heart model reconstruction from single view images

Three Dimensional (3D) human heart model is attracting attention for its role in medical images for education and clinical purposes. Analysing 2D images to obtain meaningful information requires a certain level of expertise. Moreover, it is time consuming and requires special devices to obtain aforementioned images. In contrary, a 3D model conveys much more information. 3D human heart model reconstruction from medical imaging devices requires several input images, while reconstruction from a single view image is challenging due to the colour property of the heart image, light reflections, and its featureless surface. Lights and illumination condition of the operating room cause specular reflections on the wet heart surface that result in noises forming of the reconstruction process. Image-based technique is used for the proposed human heart surface reconstruction. It is important the reflection is eliminated to allow for proper 3D reconstruction and avoid imperfect final output. Specular reflections detection and correction process examine the surface properties. This was implemented as a first step to detect reflections using the standard deviation of RGB colour channel and the maximum value of blue channel to establish colour, devoid of specularities. The result shows the accurate and efficient performance of the specularities removing process with 88.7% similarity with the ground truth. Realistic 3D heart model reconstruction was developed based on extraction of pixel information from digital images to allow novice surgeons to reduce the time for cardiac surgery training and enhancing their perception of the Operating Theatre (OT). Cardiac medical imaging devices such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) images, or Echocardiography provide cardiac information. However,these images from medical modalities are not adequate, to precisely simulate the real environment and to be used in the training simulator for cardiac surgery. The propose method exploits and develops techniques based on analysing real coloured images taken during cardiac surgery in order to obtain meaningful information of the heart anatomical structures. Another issue is the different human heart surface vessels. The most important vessel region is the bloodless, lack of blood, vessels. Surgeon faces some difficulties in locating the bloodless vessel region during surgery. The thesis suggests a technique of identifying the vessels’ Region of Interest (ROI) to avoid surgical injuries by examining an enhanced input image. The proposed method locates vessels’ ROI by using Decorrelation Stretch technique. This Decorrelation Stretch can clearly enhance the heart’s surface image. Through this enhancement, the surgeon become enables effectively identifying the vessels ROI to perform the surgery from textured and coloured surface images. In addition, after enhancement and segmentation of the vessels ROI, a 3D reconstruction of this ROI takes place and then visualize it over the 3D heart model. Experiments for each phase in the research framework were qualitatively and quantitatively evaluated. Two hundred and thirteen real human heart images are the dataset collected during cardiac surgery using a digital camera. The experimental results of the proposed methods were compared with manual hand-labelling ground truth data. The cost reduction of false positive and false negative of specular detection and correction processes of the proposed method was less than 24% compared to other methods. In addition, the efficient results of Root Mean Square Error (RMSE) to measure the correctness of the z-axis values to reconstruction of the 3D model accurately compared to other method. Finally, the 94.42% accuracy rate of the proposed vessels segmentation method using RGB colour space achieved is comparable to other colour spaces. Experimental results show that there is significant efficiency and robustness compared to existing state of the art methods

Computational mechanisms for colour and lightness constancy

Attributes of colour images have been found which allow colour and lightness constancy to be computed without prior knowledge of the illumination, even in complex scenes with three -dimensional objects and multiple light sources of different colours. The ratio of surface reflectance colour can be immediately determined between any two image points, however distant. It is possible to determine the number of spectrally independent light sources, and to isolate the effect of each. Reflectance edges across which the illumination remains constant can be correctly identified.In a scene illuminated by multiple distant point sources of distinguishalbe colours, the spatial angle between the sources and their brightness ratios can be computed from the image alone. If there are three or more sources then reflectance constancy is immediately possible without use of additional knowledge.The results are an extension of Edwin Land's Retinex algorithm. They account for previously unexplained data such as Gilchrist's veiling luminances and his single- colour rooms.The validity of the algorithms has been demonstrated by implementing them in a series of computer programs. The computational methods do not follow the edge or region finding paradigms of previous vision mechanisms. Although the new reflectance constancy cues occur in all normal scenes, it is likely that human vision makes use of only some of them.In a colour image all the pixels of a single surface colour lie in a single structure in flux space. The dimension of the structure equals the number of illumination colours. The reflectance ratio between two regions is determined by the transformation between their structures. Parallel tracing of edge pairs in their respective structures identifies an edge of constant illumination, and gives the lightness ratio of each such edge. Enhanced noise reduction techniques for colour pictures follow from the natural constraints on the flux structures