Jain, R. Kasturi, R. and Schunck B. (1995). Machine Vision. McGraw-Hill, New York.  Home/Search Document Not in Database Summary Related Articles Check |
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In Proceedings of Workshop on Models versus Exemplars in.. - Combining Models And (2001) (Correct)
....and of the non Lambertian component of surface reflection. Our statistical model is explained in detail in [17] but for completeness we will describe it here. 2.1. Augmented Lambertian equation At the heart of our method is the following equation, which is the standard Lambertian equation [11] augmented with an additive term. This is done because the standard Lambertian equation does not handle shadows nor specular reflections, which occur naturally in face images. The augmented model is then: 99 9999 9999 9 (1) which says that at pixel position , the pixel intensity, ....
R. Jain, Kasturi R., and B. Schunck. Machine Vision. McGraw Hill, 1995.
Panoramic Stereo Vision and Depth-Assisted Edge Detection - Economopoulos, Martakos (Correct)
....Both systems were developed independently. 1.1. Motivation Edge detection is one of the most broadly used operations in the analysis of images. There has been a plethora of algorithms presented within this context and several different approaches to the subject have been well documented [4, 6, 10, 13, 21]. In the vast majority of cases, edge detection has been considered to be an early step in the process of analysis. This has allowed researchers to effectively utilize edge data to guide higher level operations. In addition to its utilization in other areas, edge detection has also been used in ....
R. Jain, R. Kasturi, and B. G. Schunck. Machine Vision. McGraw-Hill, Singapore, 1995.
Temporal Segmentation of Video Objects for Hierarchical .. - Fu, Ekin, Tekalp.. (2002) (Correct)
....a video is outlined in Fig. 3. The first step is to detect or select occurrences of the objects of interest in the video. For certain applications, a precompiled set of object models can be used to automatically detect the occurrences of objects by model based or appearance based object detection [10]. Alternatively, if the video is chroma keyed and or encoded using an object oriented scheme such as MPEG 4, then this information is contained in the alpha plane of each object. Another option is to employ user interaction, where the object of interest is manually identified in the first fxame ....
R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision, Prentice Hall, 1995.
Restoration Of Images Scanned From Thick Bound Documents - Zhang, Tan (2001) (2 citations) (Correct)
....the height of the word bounding box respectly, and the orientation of the hands is decided by the orientation of the word. Our system detects Figure 3. Enhanced binarization result for Figure1. Figure 4. Words indicated by their bounding boxes. the word orientation by adopting a Hough transform [6]. It takes the centers of the bounding boxes of the connected components that belongs to a word as the points in the image space of Hough transform, and finds the line that most of centers lie on. A text line is found by clustering all the words whose hands of bounding boxes touch each other. ....
R. Jain, R.Kasturi, and B.G.Chamzas. "Machine Vision", McGRAW-HILL International editions, pp. 218-223, 1995. h h/2 Orientation of the word
A Review of Vessel Extraction Techniques and Algorithms - Kirbas, Quek (2000) (5 citations) (Correct)
....in the segmentation process. 2.4 Region Growing Approaches Region growing technique segments image pixels that are belong to an object into regions. Segmentation is performed based on some predefined criteria. Two important segmentation criteria are value similarity and spatial proximity [42]. Two pixels can be grouped together if they have the same intensity characteristics or if they are close to each other. It is assumed that pixels that are closed to each other and have similar intensity values are likely to belong to the same object. The simplest form of the segmentation can be ....
....followed by dilation and used to eliminate small structures. Two algorithms that are used in medical image segmentation and related to mathematical morphology are top hat transformation and watershed transformation [64] A good introduction to morphological operators can be found in [65] and [42]. Figueiredo and Leitao [66] describe their nonsmoothing approach in estimating vessel contours in angiograms. Their technique has two key features. First, it does not smooth the image to avoid the distortions introduced by smoothing. Second, it does not assume a constant background which makes ....
R. Jain, R. Kasturi, and B.G. Schunck, Machine Vision, McGH, 1995.
Recovery of Distorted Document Images from Bound Volumes - Zheng Zhang Chew (2001) (1 citation) (Correct)
....the right side of the left box hand, the center of the word bounding box, and the mid point of the left side of the right box hand, as shown in figure 5. The orientation of the hands is decided by the orientation of the word. Our system detects the word orientation by adopting a Linear Regression [6]. It takes the coordinates, i x and i y , of the centers of all the connected components that belong to a word as inputs of the Linear Regression, and finds the equation c x m y = of a line that best fits all these centers, where m and c are computed as follows: ....
R. Jain, R.Kasturi, and B.G.Chamzas. "Machine Vision", McGRAW-HILL International editions, pp. 506-507, 1995.
Parallel Computing With Generalized Cellular Automata - Maniatty, Szymanski, Caraco (1998) (Correct)
....image (represented as a two dimensional array of pixels) to an output image, using a mapping function called a convolution. Convolution, in this context, is similar to computing a weighted average of the neighborhood about each input pixel to select the value of each pixel in the output image [22, 24]. For each cell at every time step of the simulation, the right hand side of equation (3.1) is evaluated. This computation takes a significant portion of the total execution time of the simula tion. Equation (3.1) is an example of a reduction computed simultaneously over many overlapping ....
R. JAIN, R. KASTURI, AND B. G. SCHUNK, Machine Vision, McGraw-Hill, New York, NY, 1995.
The Actuated Workbench: Computer-Controlled Actuation .. - Pangaro.. (2002) (1 citation) (Correct)
....with IR filter (right) Puck tracking is accomplished by detecting bright regions within the image. We use the image histogram to compute a threshold value on startup, and the threshold is used to divide the grayscale image into zeros and ones. We then employ standard blob analysis techniques [9] to determine the longest horizontal segments. We can track multiple pucks simultaneously in real time using an association method [1] to distinguish the pucks between frames. In every frame, we associate each observed location with the closest puck location in the previous frame. This association ....
Jain, R. et al. Machine Vision. McGraw-Hill, 1995.
Probabilistic Semantic Video Indexing - Naphade, Kozintsev, Huang (Correct)
....in color and motion separated by strong edges. Large dominant regions are labeled manually. Each region is then processed to extract features characterizing the color (3 channel histogram [3] texture (statistical properties of the Gray level Co occurrence matrices at 4 di#erent orientations [6]) structure (edge direction histogram [7] motion (a#ne motion parameters) and shape (moment invariants [8] Details about the extracted features can be found in [9] For sites we use color, texture and structural features (84 elements) and for objects and events we use all features (98 ....
R. Jain, R. Kasturi, and B. Schunck, Machine Vision. MIT Press and McGraw-Hill, 1995.
A Self-initializing Eyebrow Tracker for Binary Switch Emulation - Lombardi, Betke (2002) (Correct)
....except for blinking. During this process, thresholded image di erencing occurs, generating a MEI. In the MEI, there should be two large regions of motion energy where the blinking took place, and scattered noise caused by slight motion of edges in the scene. Subsequently, the image is opened [17] to remove the noise. After removal of the noise, the two remaining regions yield the locations of the user s eyes in the scene. This method is discussed in greater detail in [15] Begin Execution Run Initialization Were the eyes Found Copy Templates Get Next Frame Found eyes and ....
R. Jain, R. Kasturi, and B. Schunk. Machine Vision. McGraw Hill, 1995.
Panoramic Stereo Vision and Depth-Assisted Edge Detection - Economopoulos.. (Correct)
....Both systems were developed independently. 1.1. Motivation Edge detection is one of the most broadly used operations in the analysis of images. There has been a plethora of algorithms presented within this context and several different approaches to the subject have been well documented [4, 6, 10, 13, 21]. In the vast majority of cases, edge detection has been considered to be an early step in the process of analysis. This has allowed researchers to effectively utilize edge data to guide higher level operations. In addition to its utilization in other areas, edge detection has also been used in ....
R. Jain, R. Kasturi, and B. G. Schunck. Machine Vision. McGraw-Hill, Singapore, 1995.
Visual Panel: Virtual Mouse, Keyboard and 3D Controller.. - Zhang, Wu, Shan, Shafer (2001) (4 citations) (Correct)
....# ### ##, where # is the average gradient and # is the average intensity. The distribution of # is assumed to be a Gaussian, i.e. # # #### # ###. More richer modeling of the appearance is under investigation. 4. 2 Automatic Detection We have developed a simple technique based on Hough transform [8] to automatically detect a quadrangle in an image. Take the image shown in Fig. 3a as an example. A Sobel edge operator is first applied, and the resulting edges are shown in Fig. 3b. We then build a 2D Hough space for lines. A line is represented by ### ##, and a point ### ## on the line ....
R. Jain, R. Kasturi, and B.G. Schunck. Machine Vision. McGraw-Hill, New York, 1995.
A Logical Account of Perception Incorporating Feedback and.. - Shanahan (2002) (Correct)
....a conventional image understanding system. The knowledge inherent in Axioms (1) to (4) is analogous to a 3D model in a conventional machine vision system, and the abductive interpretation of the sensor data corresponds to the conventional process of matching a model to the data from a given scene [Jain, et al. 1995, Chapter 15] However, under the present scheme, it s possible to augment this knowledge with declarative sentences, such as all the red blocks are shorter than all the blue blocks or one of the green blocks is hidden . Combined with a suitable inference mechanism, such knowledge can be used ....
R.Jain, R.Kasturi, and B.G.Schunk, Machine Vision, McGraw-Hill, 1995.
Writer Identification from Non-uniformly Skewed Handwriting Images - Tan (1998) (3 citations) (Correct)
....to uniformly skewed lines in typed document images. One of the most popular skew estimation techniques is based on the projection profile of the typed documents. Normalisation Feature Extraction Identification British Machine Vision Conference 480 The horizontal vertical projection profile [8] is the histogram of the number of black pixels along horizontal vertical scan lines. For a script document with horizontal text lines, the horizontal projection profile has peaks at text lines positions and troughs at positions in between successive text lines. To determine the skew of a ....
R. Jain, R. Kasturi, B. G. Schunck, "Machine Vision", McGraw-Hill, Inc., 1995.
Vision-Based Mobile Robot Localization with Simple.. - Baczyk, Kasinski.. (2003) (Correct)
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Jain, R. Kasturi, R. and Schunck B. (1995). Machine Vision. McGraw-Hill, New York.
CMPack: A Complete Software System for Autonomous - Legged Soccer Robots (Correct)
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R. Jain, R. Kasturi, and B. Schunk. Machine Vision. McGraw-Hill, New York, 1995.
Amodal volume completion: 3D visual completion - Breckon, Fisher (2005) (Correct)
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Human Perception-based Color Segmentation Using Fuzzy Logic - Lior Shamir Department (Correct)
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A Binary Color Vision Framework for Content-based - Image Indexing Qiu (2002) (Correct)
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R. Jain, R. Kasturi, B. G. Schunck. Machine Vision. McGraw-Hill,Inc., New York, 1995
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Three-Dimensional Reconstruction of the.. - Douxchamps.. (2000) (Correct)
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Change Detection in Color Images - Fisher (1999) (Correct)
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R. Jain, R. Kasturi, B. G. Schunck. Machine Vision. McGraw-Hill, 1995.
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Environment Learning For Indoor Mobile Robots - Cetto (2003) (Correct)
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A Survey of Computer Vision Education and Text Resources - Bruce Maxwell Department (2000) (3 citations) (Correct)
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R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision, McGraw-Hill, New York, NY, 1995.
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Noise-Resistant Affine Skeletons of Planar Curves - Betelu, Sapiro, Tannenbaum.. (Correct)
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Shape Priors in Medical Image Analysis: Extensions of the .. - Author Albert Montillo (Correct)
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Unknown - Virginia Polytechnic Institute (Correct)
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