For a freehand line image drawn onto a PC screen where a user-selected reference image, e.g., a color photograph, as a model is faintly displayed with low contrast, we proposed a method for automatic coloring with a constrained Delaunay triangulation that divides the image into small triangles. Using a prototype system based on the proposed method, users can complete impressive pictures by only drawing lines. Our coloring method begins with the triangulation for the set of sampling points on the drawn lines, followed by sampling of color in each triangle on the reference image, smoothing of color among neighboring triangles, and painting of each triangle with the smoothed color. The result of the triangulation is modified such that it satisfies a constraint where its divided lines should not cross over the drawn lines not to mix colors beyond the drawn line. Our prototype system can display the coloring result of the current drawings immediately for convenience. So, the user can check the effect of a newly drawn line on coloring at any time. As the result of the coloring depends on how the user draws freehand lines, it can be seen as an art work with the individuality of each user’s drawings.
KEYWORDS: Image segmentation, Feature extraction, Optical character recognition, Visual information processing, Electronic imaging, Current controlled current source, Image filtering, Sensors, Binary data, Time metrology
A method for extracting onomatopoeia characters from comic images was developed based on stroke width feature of characters, since they nearly have a constant stroke width in a number of cases. An image was segmented with a constrained Delaunay triangulation. Connected component grouping was performed based on the triangles generated by the constrained Delaunay triangulation. Stroke width calculation of the connected components was conducted based on the altitude of the triangles generated with the constrained Delaunay triangulation. The experimental results proved the effectiveness of the proposed method.
Detecting lane markings on roads from in-vehicle camera images is very important because it is one of the fundamental
tasks for autonomous running technology and safety driving support system. There are several lane markings detection
methods using the width information, but most of these are considered to be insufficient for oblique markings. So, the
primary intent of this paper is to propose a detecting lane markings method robust to orientation of markings. In this
work, we focus on the width of lane markings standardized by road act in Japan, and propose a method for detecting
white lane markings by extracting white regions with constant predefined width from bird's-eye road images after
segmentation such as categorical color area one. The proposed method is based on the constrained Delaunay
triangulation. The proposed method has a merit that can be measure an exact width for oblique markings on the bird's-eye
images because it can be obtained perpendicular width for edge. The effectiveness of the proposed method was
shown by experimental results for 187 actual road images taken from an in-vehicle camera.
Humans can put different colors together and categorize them as "red", "yellow", or "orange" etc. This is called
categorical color perception. We applied this property of human color vision to area segmentation for road images in
order to compensate color tone change of road images depending on light condition on a road. Basic map of categorical
colors is constructed in the L*a*b* space based on the color naming experiment. Area segmentation was done by
assigning one of the 14 categorical colors to each pixel. Results were successful, even without any noise reduction
technique, A shifted database of categorical colors for images with orangish tone is also prepared by trial and error.
Pseudo-color-constancy is successfully obtained for the images of orangish tones using the shifted database. To deal with
the lightness change depending on the change of sunlight along the time of day, an appropriate value was added to the
lightness of each pixel of the original image. Satisfiable area segmentation was obtained in this case, too. This method
indicates the possibility of implementation of color constancy property for color image processing of road scene.
Volume intersection (VI) is a successful technique for reconstructing 3-D shapes from 2-D images (silhouettes) of multiple views. It consists of intersecting the cones formed by back-projecting each silhouette. The 3-D shapes reconstructed by VI are called visual hull (VH). In this paper we propose a fast method obtaining the VH. The
method attempts to reduce the computational cost by using a run representation for 3-D objects called SPXY table that is previously proposed by us. It makes cones by back-projecting the 2-D silhouettes to the 3-D space through the centers of the lens and intersects them keeping the run representation. To intersect the cones of multiple views keeping the run representation, we must align the direction of runs representing the cones. To align them we use the method of
swapping two axes of a run-represented object at the time cost of O(n) where n is a number of runs, which is also previously proposed by us. The results of experiments using VRML objects such as human bodies show that the proposed method can reconstruct a 3-D object in less than 0.17 s at the resolution of 220 × 220 × 220 voxels from a set of silhouettes of 8 viewpoints on a single CPU.
Volume intersection is one of the simplest techniques for reconstructing 3D shapes from 2D silhouettes. 3D shapes can be reconstructed from multiple view images by back-projecting them from the corresponding viewpoints and intersecting the resulting solid cones. The camera position and orientation (extrinsic camera parameters) of each viewpoint with respect to the object are needed to accomplish reconstruction. However, even a little variation in the
camera parameters makes the reconstructed 3D shape smaller than that with the exact parameters. The problem of optimizing camera parameters dealt with in this paper is determining good approximations from multiple silhouette images and imprecise camera parameters. This paper examines attempts to optimize camera parameters by reconstructing a 3D shape via the method of volume intersection. Reprojecting the reconstructed 3D shape to image
planes, the camera parameters are determined by finding the projected silhouette images that result in minimal loss of area when compared to the original silhouette images. For relatively large displacement of camera parameters we propose a method repeating the optimization using dilated silhouettes which gradually shrink to original ones. Results of experiment show the effect of it.
This report presents a technique of decomposing an arbitrary binary image into the union of rectangles so that the number of rectangles becomes as small as possible. This decomposition is referred as adaptive rectangular decomposition. Decomposing a binary image into objects with a same basic shape but with different sizes is familiar as morphological skeleton decomposition. To implement adaptive rectangular decomposition, we generalize the discrete version of morphological skeleton decomposition by replacing a sequences of disks {nB}, n equals 0,1, (DOT)(DOT)(DOT) with a structuring element sequence {Bn}, where Bn equals Bn-1 (direct sum) Gn-1 and Gn is called a generator. A good selection of each generator in a generator sequence {Gn} makes a compact representation of a given binary image. In adaptive rectangular decomposition, we restrict each generator Gn by one of only two objects; the vertical 2-pixel line V and the horizontal 2-pixel line H. The adaptive rectangular decomposition algorithm selects the best sequence {Gn} using dynamic programming (DP) technique. In some experiments, we compared adaptive rectangular decomposition with other types of decomposition in the viewpoint of the time cost of morphological operations by decomposed structuring elements (decomposed binary images). Experimental results show that the time cost of the operations by the structuring elements represented by adaptive rectangular decompositions is smaller than the case of other types of decompositions.
In order to efficiently perform morphological binary operations by relatively large structuring elements, we propose to decompose each structuring element into squares with 2 X 2 pixels by the quadtree approach. There are two types of decomposition--the dilation decomposition and the union decomposition. The first type decomposition is very efficient, but it is not necessarily always possible. The decomposition of second type is available for any structuring element, but the time cost of computation is proportional to the area of the structuring element. The quadtree decomposition proposed here is the combination of these two types of decomposition, and exists for any structuring element. When the Minkowski addition A (direct sum) B or the Minkowski subtraction A - B is computed, the number of times of the union/intersection of translations of the binary image is about the number of leaves of the quadtree representation of the structuring element B, which is roughly proportional to the square root of the area of B. In this paper, an algorithm for quadtree decomposition is described, and experimental results of this decomposition for some structuring elements are shown.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.