L. J. Guibas and C. Tomasi, \Image retrieval and robot vision research at Stanford," in ARPA Image Understanding Workshop, pp. 101-108, 1996. Home/Search Document Details and Download
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Lower-level and Higher-level Approaches to Content-based.. - Iqbal, Aggarwal (2000) (Correct)
....State of Texas Higher Education Coordinating Board, Advanced Research Project 97 ARP 275. is to use the lower level analysis module to increase the capability of the higher level analysis module, for queries where the structure exhibited by the manmade objects is important. Many research groups [5, 6, 7, 8] are actively pursuing content based indexing, storage and retrieval of images. Some systems have already been built that provide content based image retrieval [9, 10, 11] These systems require user interaction, which emphasizes shape, color, and texture features to build queries. The histogram ....
L. J. Guibas and C. Tomasi, \Image retrieval and robot vision research at Stanford," in ARPA Image Understanding Workshop, pp. 101-108, 1996.
Intelligent Retrieval Of Digital Images From Large.. - Agouris, Stefanidis.. (1998) (1 citation) (Correct)
....can subsequently narrow or broaden his her search according to the previously obtained results and the purpose of the search. 1 INTRODUCTION Intelligent image retrieval from large databases is one of the novel applications which are receiving increased attention in the computer vision community [2,8,9,10,14,15]. Some prototype systems have also been reported, including Chabot [14] IBM s QBIC [5] VisualSeek [18] ImageRover [16] and PicHunter [3] The common trend in these efforts is that they focus in generaluse multimedia type image databases. Such a database includes for example images of sunsets, ....
Guibas L.J. & C. Tomasi, "Image Retrieval and Robot Vision Research at Stanford" in Proceedings 1996 ARPA Image Understanding Workshop, Vol. 1, pp. 101-108, 1996.
Content-based 3D Neuroradiologic Image Indexing and.. - Liu, Rothfus, Kanade (1998) (Correct)
....caused by excessive liquid ffl texture: the texture of the lesion ffl size: the dimension of the lesion ffl age: some visual features vary with patient s age. Figure 5 is a sketch of a flow chart for constructing an image index. The basic idea is to build a set of feature detectors or demons [10] which can capture the values of relevant salient visual features in the input image. Some initial design and experiments on building some of the important feature detectors are described below. 3D Symmetry Plane Detector: Since normal human brains present an approximate bilateral symmetry, which ....
L.J. Guibas and C. Tomasi. Image retrieval and robot vision research at stanford. In The Proceedings of the Image Understanding Workshop, pages 101--108. ARPA, Morgan Kaufmann Publishers, Inc., 1996.
Shape-based Image Retrieval Using Geometric Hashing - Cohen, Guibas (1997) (6 citations) Self-citation (Guibas) (Correct)
....a given query feature The most similar database images are ones that have many features which are close to query features. We apply our approach to an example database of 500 chinese character bitmaps. 1 Introduction The function of a content based image retrieval system [ Niblack et al. 1993, Guibas and Tomasi, 1996 ] is typically to find database images that look similar to a given query image or drawing. Database and query images are usually summarized by their color, shape, and texture content. Here we use the term images in a very broad sense that includes any type of graphical information. Examples of ....
....starts with an illustration of the image. The illustration curves are projected onto a basis of basic shapes such as line segments, corners, circular arcs, etc. More precisely, we record what basic shapes fit where in the illustration curves. This strategy was first suggested in [ Cohen and Guibas, 1996 ] The projection step is discussed further in section 2. An invariant set of geometric primitives (a.k.a. basic shapes) is then derived from the projected illustration using geometric hashing ( Lamdan and Wolfson, 1988 ] The invariance is with respect to a transformation of the projected ....
Leonidas J. Guibas and Carlo Tomasi. Image retrieval and robot vision research at Stanford. In Proceedings of the ARPA Image Understanding Workshop, pages 101--108, February 1996.
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