M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....this approach is in the way it could help to handle increasingly complex models, by helping in the comparison between heterogenous families of models, and in the explicit use of prior knowledge. We introduce the principle and report an application in which lidar images are segmented in quadtrees [9] and the resulting cells are classified. 2 Model Selection Constructive models allow us to compare between very heterogenous alternatives. A constructive model leads to a description of the data, which has a length in bits. This length is weakly dependent on the language in which the description ....

Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. PWS Publishing, second edition, 1999.

....neighboring pixels are in the same region. Different T1 and T2 will produce slightly different fusion results. In our experiment, we choose T1 and T2 as a function of the mean and variance of pixel values in the edge and source images. Further discussion on how to select T1 and T2 can be found in [15]. Finally, we get a labeled image in which each different value represents a different region, zero corresponds to edges. 2.3. FUSION Information on salient features of an object is partially captured by the magnitude of high frequency wavelet coefficients that corresponding to that object. ....

Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, pp. 164-176, Chapman & Hall Computing, 1993.

....etc. because he proclaimed, that this di#erence in the intensity is preserved in both the images, while the pixel intensity can be modified very easily (due to various geometric distortions, image sampling, etc. The features are typically extracted using various points of interest detectors [10, 42, 62, 26]. The matching is performed only in between the derived features. Consequently the disparity is directly evaluated only in the feature positions. That is why the results are very sparse and for binocular stereo matching purposes inconvenient. In order to obtain dense disparity maps it is ....

Milan Sonka, Vaclav Hlavac, and Roger D. Boyle. Image Processing, Analysis and Machine Vision. PWS, Boston, USA, second edition, 1998.

....region, or, conversely, a light and a dark region. When a part is cleanly segmented from any background information, an edge detection algorithm can easily label all points representing its boundary points. When using the connected component algorithm for segmentation, a boundary tracing algorithm [10] is used to extract the edge pixels representing the boundary points on the region of interest. Figure 3. Path of Raster Scan x z 5.3. Line or Curve Fitting A Hough transform (described in [11] is applied to the extracted edge points of a step block to fit the points into straight lines. The ....

M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision, pp 129 - 134, Chapman & Hall, London, 1993.

....image processing operators. These are very rough representations of the continuous kernels. They are however often sufficient for smoothing, edge detection or gradient estimation. Typical representatives are mean or Gaussian smoothing operators and edge detectors like Sobel or Laplacian operators [18]. Non linear filters are generally filters that do not fit into the category mentioned above, i.e. the filtering cannot be expressed as convolution. Typical examples are dilation, erosion, and median filters used for volume analysis. In this paper we focus on a subcategory of non linear filters, ....

....filters are filters proposed by Nagao et al. 13] The filter computes mean and dispersion for 9 different operator masks. The output value is the mean of the mask with the smallest dispersion. Similar filters based on such edge preserving constraints are also called rotated mask smoothing filters [18]. 3 HARDWARE BASED NON LINEAR Our filtering methods using programmable hardware are in spirit to previous approaches [12] However, exploiting the latest PC graphics hardware features allows to increase the computational efficiency and performance compared to the previous approaches. In this ....

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M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis and Machine Vision. PWS Publishing, 1995.

....is divided into 16 equal parts. In the case of VisTex benchmark, images are first converted into Portable Greyscale format and then divided into four parts each. In the next step, texture features are extracted using five different texture extraction methods. The methods used are auto correlation [178,199], co occurrence matrices [86,176,178] edge frequency [178,199] Laws[178,126] and primitive run length [46,176,199] After the feature extraction phase, the features extracted from each method are classified individually and also features from all the five methods are combined as a set and then ....

....images are first converted into Portable Greyscale format and then divided into four parts each. In the next step, texture features are extracted using five different texture extraction methods. The methods used are auto correlation [178,199] co occurrence matrices [86,176,178] edge frequency [178,199], Laws[178,126] and primitive run length [46,176,199] After the feature extraction phase, the features extracted from each method are classified individually and also features from all the five methods are combined as a set and then classified. For classification, linear classifier and nearest ....

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998.

....into major categories such as edge based, region based, Markov Random Field based (MRF) Deformable Models, and Hybrid techniques. Edge and Region based techniques: In the first group, image edges must be detected and grouped into contours or surfaces which depict the boundaries of objects [26]. Can didate edges can be extracted by thresholding the gradi ent or Laplacian magnitude computed through filters [4] so as to form a continuous, one pixel wide contour. Despite the difficulty in computing the connected contour, many edge based techniques have been successfully employed for ....

M. Sonka et al. Image Processing, Analysis and Machine Vision. PWS Publishing, 1999.

....are then separated by detecting the anterior and posterior junctions. Finally, we optionally smooth the lung boundary along the mediastinum. There are several distinctions between our method and previous work. First, instead of using a fixed threshold value, we use an optimal thresholding method [15] to automatically choose a threshold value that reflects the gray scale characteristics of a specific dataset. Second, we use an efficient method to find the anterior and posterior junction lines between the right and left lungs. Finally, to obtain more consistent results across time and to leave ....

....is described in detail next. A. Lung Extraction The goal of the lung extraction step is to separate the voxels corresponding to lung tissue from the voxels corresponding to the surrounding anatomy. Rather than using a fixed threshold to segment the lungs, we instead use optimal thresholding [15] to automatically select a segmentation threshold for the image volume. Connectivity and topological analysis are used to further refine regions that represent the extracted lungs. 1) Threshold Selection: Optimal thresholding is an automatic threshold selection method that allows us to ....

M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision. Pacific Grove, CA: PWS, 1999.

....in the same direction that have the same gray level. Each primitive is defined by its gray level, length and direction. Five statistical features defining the characteristics of these primitives are used as our features. The detailed algorithms for these methods are presented by Sonka et al. [16]. 3. Experimental Details and Results In this paper we present the experimental details of Meatex and Vistex separately. The texture feature sets have been derived from the above discussed methods. In addition, we also generate a combined feature set that contains all features from the five ....

M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS publishing, San Francisco, 1999.

....can also allow a major step in the direction of image comprehension# this is important because a semantic understanding of pictures could be useful for applications involving the extraction and manipulation of objects in a scene. Conceptually, there are two major approaches to image understanding [8]: ffl Image based methods detect and segment image regions that may correspond to real objects or to portions of object, and eventually match them in a database. These methods are also called bottom up methods because they proceed from the raster image to describe it by regions. ffl On the other ....

M. Sonka, V. Hlavac,andR.Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, London, 1 edition, 1983.

....in the same direction that have the same grey level. Each primitive is defined by its grey level, length and direction. Five statistical features defining the characteristics of these primitives are used as our features. The detailed algorithms for these methods are presented by Sonka et al. [20] and Pratt[14] In addition to the above well known approaches to texture classification, we consider three new approaches including binary stack method, texture operators and texture spectrum. Chen et al. 2] introduce the use of binary stacks for texture analysis. For a total of L grey levels, ....

M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision , PWS publishing, San Francisco, 1999.

....Neural network, Omnidirectional, Axis crossover vector, Ground plane. 1. Introduction Object classification is a common requirement of computer vision and target based tracking systems. Different techniques exist for estimating an object s position and location within an image [1, 2, 3], which can generally be divided into Marr s low and high level categories [4] or combined active vision techniques. Low level data based vision techniques are only able to identify the shape of the object. Higher level model based techniques are able to estimate what type of object ....

.... active vision techniques which combine both low and high level techniques are subject to the same pitfalls, although the process is often less computationally intensive due to heuristic information gathered from the low level technique which decreases the high level process search space [3, 2]. In this paper we present a computationally cheap, and reasonably accurate technique for detecting and tracking moving objects, and for determining whether or not those objects are human. The technique involves three stages. Firstly an active contour model [5] is used to detect and track an ....

Sonka M., Hlavac V. & Boyle R., Image Processing, Analysis and Machine Vision, Chapman & Hall, 1994.

....of human shape in isolated static images taken from a motion sequence; in this study we discuss an individual s shape deformation during motion. The method discussed is part of a larger system designed to track moving pedestrians, a problem that has been the subject of much research [4, 5, 6]. We show that the periodic nature of human walking is clearly discernible from the deformation pattern, and that individual humans have a specific temporal pattern. The paper is divided into 6 sections. Section 2 discusses the use of active contour models for detecting walking humans. Section 3 ....

. Sonka M., Hlavac V. & Boyle R. [1994]. Image processing, analysis and machine vision. Chapman & Hall.

....of three when a call to a gaussian distributed random number generator is made. This is not a serious problem when the applet is running on its own. 4.5 Noise Reduction We implemented four noise reduction algorithms, which also evaluated standard local neighbor operations. ffl Mean smoothing [5] by a 3x3 or 5x5 convolution kernel. ffl A median [13] filter replaces a pixel by the median value of its neighborhood. ffl Gaussian smoothing [16] implemented by using discrete 3x3 and 5x5 convolution kernels. ffl The Mean Median [9, 10] button: Pixel values in a 3x3 neighborhood excluding ....

V. Hlavac, M. Sonka and R. Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall, Cambridge University Press, 1993.

....event queue, thus simulating, for application purposes, the presence of an ordinary mouse. Graylevel VisualGlove is essentially based on the use of a stable, robust pattern matching algorithm applied to an edgeness image obtained in real time from each frame. Thanks to the use of a Kalman Tracker [13] the position, orientation and size of the fingertips are predicted in each frame, thus drastically reducing the search area for pattern matching. A scheme of the algorithm is given in Fig. 3. Init: repeat Check for the presence of both fingertips in the predefined area until Both patterns ....

....i # P i = AP i 1A T BB T Riccati eqn. K i = # P i 1G T i # G i # P i G T R i # 1 Kalman Gain # X i = # X i K i # Z i G i # X i # Assimilation P i = I K i G i ) # P i Figure 6: Prediction Assimilation algorithm The performance of the Kalman controller [13] described above is closely related to the hypothesis that both the noise vectors and the status vector have a Gaussian distribution. At this stage we will not address this issue, since the performance of the Kalman tracker is reasonable for our purposes; several di#erent solutions do, however, ....

M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.

....for determining regional boundaries[14] These edges are overlaid on the original and using a simple point andclick mechanism with the mouse, the different regions are manually labelled. For each region, its texture is extracted using five separate texture methods including autocorrelation[10,13] (99 features) co occurrence matrices[1,7] 20 features 14 Haralick s features and six others including homogeneity, entropy, mean grey level, standard deviation of grey level, kurtosis and skewness) edge frequency[13] 50 features) Law s[8] 125 features from 25 masks) and run length[6,13] ....

.... is extracted using five separate texture methods including autocorrelation[10,13] 99 features) co occurrence matrices[1,7] 20 features 14 Haralick s features and six others including homogeneity, entropy, mean grey level, standard deviation of grey level, kurtosis and skewness) edge frequency[13] (50 features) Law s[8] 125 features from 25 masks) and run length[6,13] 5 statistical features based on run lengths) In total we have 8 objects on which a k nearest neighbour classifier has been trained[12] These include trees, grass, leaves, bricks, sky, clouds, pebbles and road. The ....

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machinevision , PWS press, 1998.

....half profiles, and 84 on full profiles. One nice aspect of the work is that a full face model allows for higher levels of occlusion than do techniques which look only at features isolated from the face geometry [58] Another strategy successfully employed in object recognition is graph matching [60, 54]. This technique associates constituent features with nodes in a graph. Recognition is typically cast as an instance of sub graph isomorphism. In this case the points correspond to local maximums in a feature map. The feature map is con 9 structed with the use of a bank of Gabor filters at ....

M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall, Cambridge, 1993.

....shape algorithms in the field of computer vision which may be useful for such a task. Shape analysis programs can be classified in many different ways according to shape attributes and analysing techniques. The most common classification scheme divides the programs according to their input types [95, 113, 137]. A program is called contour based or external based, when only the boundary of the object is utilized. If the interior of the object is also analyzed, the algorithm is called structural based or internal based. Classification schemes can also be made based on the internal shape representation of ....

....descriptors, which are often analyzed subsequently by statistical or neural network methods. A space domain technique produces non numeric and graphic representations. Shape analysis programs can also be dissected in other ways: global features vs. local features and single scale vs. multi scale [8, 75, 137]. Algorithms based on global features tend to be simple, but they may be unstable as a small change in the input shape may drastically alter the analysis output [22, 95] The instability may be alleviated by introducing local information, at the expense of increasing programming complexity. ....

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M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis and Machine Vision. London: Chapman & Hall, 1995.

....Science Ltd. All rights reserved. Keywords: Fast Hough transform; Image decomposition; Segmentation; Skew detection 1. Introduction The description of a digital image in terms of simple geometrical shapes is a well established methodology that often proves useful for e#ective image segmentation [1]. Moreover, such a description can also contribute towards e#ective and fast implementation of image processing algorithms. Well known examples are the description of images in terms of polygonal shapes [2] and shape description using morphological operations [3] Both methods can be used to ....

....scan of the image to find a pixel at coordinates (x # , y # ) I(x # , y # ) 1. Step 2: a) Find the opposite vertex (x ## , y ## ) of the best fitting block at (x # , y # ) following steps 3(a) e) of the algorithm in the previous section. b) Set BlockNum 1. c) Set XF[1] x # , X[1] x ## , F[1] y # , 1] y ## . d) Set I(x, y) 0 # x3[XF[1] 2X[1] # y3[F[1] 1] Step 3: Set iter 1. Step 4: For every (x, y) belonging to the surrounding line of the block with opposite vertices at (XF(iter) F (iter) and (X(iter) iter) and obeying I (x, y) 1: a) ....

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M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1993.

....voices saying the same thing. A handful of algorithms attempt to address this problem. A three dimensional model of an object can be used to identify an object at any orientation. This method is however a fledgling one, only works for relatively simple objects, and is computationally expensive[50]. There is also the question as to whether it is appropriate to offer a media object depicting a different view. The user may want to find similar media objects to the query, or to find media objects depicting the same real world object(s) as the query. In the case of the former, the problem of ....

....containing cars, though the scene or video as a whole might 45 not produce a strong match with the query object. The scene will not be retrieved unless the car is entered into the system as a media object in its own right. There are methods for locating objects in scenes such as template matching [50]. These methods are usually computationally expensive. Currently, some (possibly minimal) form of user interaction is usually required when selecting objects in a scene. A.4.2 Retrieval Models Retrieval systems come in a variety of flavours [46] Boolean In such a system documents that contain ....

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Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.

....is most suited to the texture recognition problem and whether the combination of features from different methods provides any further advantage. 2. TEXTURE METHODS In this paper we analyse Meastex images using five different texture extraction methods. These methods are described in brief below [11,16]. Evaluation of texture measures Pattern Recognition Letters 3 Autocorrelation based texture features The textural character of an image depends on the spatial size of texture primitives. Large primitives give rise to coarse texture (e.g. rock surface) and small ....

M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS publishing, San Francisco, 1999.

....geometric used in creating the object (i.e. in manufacturing or in some natural formation process) can be directly applied to the recognition task. Consequently, the major difficulties might be merely in arranging the physical image acquisition. Plenty of methods exist for recognition of objects [14, 12, 17]. However, some of the methods, for example syntactical methods, are mainly suitable for regular or man made object recognition. In this paper the main concern is on irregular objects (for example surface defects) which are hard to recognize even for a human observer. British Machine Vision ....

....mainly suitable for regular or man made object recognition. In this paper the main concern is on irregular objects (for example surface defects) which are hard to recognize even for a human observer. British Machine Vision Conference 2 There are several methods for the shape analysis of objects [14, 12, 17]. These methods can be divided into two categories, the area based methods and the contour based methods. The latter methods are of interest in this paper since we are mainly concerned on the shape of a contour. The contour based methods include the following techniques. Simple descriptors, for ....

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Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.

....very much varying numerical values better compatible (see Fig. 8.7) In the previous part of this chapter, it was noted that the linearity of the features is the fundamental assumption underlying the whole analysis. Cal 5 For additional information on machine vision and texture analysis, see [20] 8.3. EXAMPLE: IMAGE ANALYSIS OF FLOTATION FROTH 131 0 50 100 150 200 250 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 Image1:normaloperation Image86:poisoning Figure 8.6: Typical intensity histograms of the normal surface and the collapsed one 0 50 100 150 200 250 300 0.052 0.054 0.056 ....

Sonka, M., Hlavac, V, and Boyle, R.: Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, London, 1993.

....used in adapting a system to an applicationspecific shape space. The results of the experiments are good. It is shown that fairly simple shape descriptors can be flexibly used in complex recognition tasks involving irregular objects. 1 Introduction Shape descriptors have been actively studied [7, 11] as an alternative to the approaches involving texture and colour distribution. Intuitively, using only the contour of an object instead of the whole area would imply a square root proportional saving in calculations. This is practically true although there does not always exist analog ....

....such as principal component analysis. We propose Self Organizing Map (Sec. 3) because of its feasibility for visualization. 2 2 Simple shape descriptors In this section, five simple shape descriptors are revised (Fig. 1) Variations of most of them have been widely used in object recognition [11]. The notations used here on are listed in Table 1. Compactness Principal axes Convexity Elliptic variance Variance Figure 1: Five simple shape descriptors. p i = x i y i contour point p = fp i g; i = 1 : N contour A area P perimeter jj Delta jj vector length = 1 N P i ....

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M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998. 19

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998. 19

....to the definition are proposed in mathematical morphology to cope with digital images and ensure that the homotopy of the original object and its skeleton is secured. The related algorithms either expel layers of the object (sequential thinning) or calculate maxima of a quench (distance) function [8]. Figure 5: Ambiguity of the center of the object and of the thinning to width one. Image in corner representation expressed by object outlines as a subset of points in a plane allows to avoid traditional problems in digital images, e.g. to be in the center or to be thinned to width one. The ....

M. Sonka, V. Hlavac, and R.D. Boyle. Image Processing, Analysis and Machine Vision. PWS, Boston, USA, second edition, 1998.

....K is a 33 matrix which is usually called the intrinsic parameter matrix of the camera. We have also considered two parameters of radial distortion, but for simplicity they are ignored in the following analysis. There are a number of camera calibration approaches that have been proposed today [5,6]. The approach we used is based on a sequence of images [2] This approach combines the traditional calibration chart approach with multiple image motion reconstruction. A novel linear algorithm for extracting calibration parameters from the chart to image homographies is introduced. These ....

M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS Publishing, second Edition, 1998.

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M. Sonka, V. Hlav'ac, and R.D. Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall, London, U.K., first edition, 1993.

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M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1993.

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M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1993.

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M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision, second ed., PWS Publications, Pacific Grove, CA, 1999.

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M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, 2 nd Edition, PWS Publishing, 1999

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M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, PWS Publishing, 1999.

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M. Sonka, V. Hlavac, and R. Boyle. Image Processing, Analysis and Machine Vision. PWS Publishinh, Pacific Grove, 1998.

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M. Sonka, V. Hlavac, and R. Boyle. Image Processing, analysis and machine vision. Chapman & Hall, London, 1993.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS publishing, San Francisco, 1999.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998. IEEE PAMI 19

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998.

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Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.

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M. Sonka, V. Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, PWS Publishing, 1999.

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M. Sonka, V. Hlavac and R. Boyle, Image processing, analysis and machine vision, PWS press, 1998.

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Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. PWS Publishing, second edition, 1999.

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Milan Sonka, H. Lovac, Vaclav and Richard Boyle, Image Processing, Analysis and Machine Vision, ITP Publishers, Pacic Grove, 1999.

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Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. PWS, Paci c Grove, USA, second ed. edition, 1998.

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M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision (London, Chapman & Hall, 1993).

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M. Sonka, V. Hlavac, R. Boyle. Image Processing, Analysis and Machine Vision. 2nd Edition. PWS Publishing. 1998. 41

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