{"title":"Optimum Edge Detection for Object-Background Picture","authors":"Kammoun F., Astruc J.P.","doi":"10.1006/cgip.1994.1004","DOIUrl":"10.1006/cgip.1994.1004","url":null,"abstract":"<div><p>The statistical method for edge gray-level detection of Kittler <em>et al</em>. is shown to estimate the middle of the object and the background luminance. Modifications to Kittler′s method are presented to estimate better the gray level above the maximum slope and to be less sensitive to the noise over uniform luminance areas.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 25-28"},"PeriodicalIF":0.0,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127949841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Curve and Surface Generation and Refinement Based on a High Speed Derivative Algorithm","authors":"Sankar P.V., Silbermann M.J., Ferrari L.A.","doi":"10.1006/cgip.1994.1008","DOIUrl":"10.1006/cgip.1994.1008","url":null,"abstract":"<div><p>We propose an efficient algorithm for the refinement of curves and surfaces using simple knot B-splines. We develop a high speed algorithm for the computation of the <em>r</em>th derivative of an <em>r</em>th order spline and demonstrate that the inverse of this algorithm leads to an efficient refinement algorithm which is an order of magnitude faster than the well known Oslo algorithm. We show how the high speed derivative algorithm can also be used to directly generate spline curves and surfaces much more efficiently than linear combination algorithms and forward differencing.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 94-101"},"PeriodicalIF":0.0,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117158321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Robust Image Restoration Algorithm Using Markov Random Field Model","authors":"Bhatt M.R., Desai U.B.","doi":"10.1006/cgip.1994.1006","DOIUrl":"https://doi.org/10.1006/cgip.1994.1006","url":null,"abstract":"<div><p>A new method is proposed for image restoration of a gray-level image blurred by an erroneous point spread function and corrupted by either additive or multiplicative noise. The proposed method is based on a Markov random field model with an appropriate line field, whereby it has the ability to restore discontinuities. Robustness is incorporated by the total least-squares term in the posterior energy function. A simulated annealing algorithm is used to implement the proposed method. Simulation results comparing restoration based on minimizing posterior energy functions of type ℓ<sub>2</sub> ℓ<sub>1</sub>, <em>total</em> ℓ<sub>1</sub>, and total least squares with and without line field are presented.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 61-74"},"PeriodicalIF":0.0,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137289361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On the Difference Method for Drawing Conic Arcs","authors":"Wang W.P., Joe B., Wang C.Y.","doi":"10.1006/cgip.1994.1002","DOIUrl":"10.1006/cgip.1994.1002","url":null,"abstract":"<div><p>The difference method is an efficient way to generate an inscribed polygon of a conic section. We address the implementation issues of the difference method, that is, how to choose the difference parameter and initial points so that the inscribed polygon has a minimal number of line segments and is within a given tolerance of the conic arc. We also discuss how the difference method can be used to draw conic arcs represented by rational quadratic Bézier curves.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 8-18"},"PeriodicalIF":0.0,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122946242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Recursive Wavelet Transform for 2D Signals","authors":"Barrat M., Lepetit O.","doi":"10.1006/cgip.1994.1010","DOIUrl":"10.1006/cgip.1994.1010","url":null,"abstract":"<div><p>A new type of analyzing wavelet is presented; it allows recursive processing of the signal. It is used together with lowpass filters for 2D signals processing. A very good edge detection is obtained.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 106-108"},"PeriodicalIF":0.0,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Compact Image Representation by Edge Primitives","authors":"Altergartenberg R., Huck F.O., Narayanswamy R.","doi":"10.1006/cgip.1994.1001","DOIUrl":"10.1006/cgip.1994.1001","url":null,"abstract":"<div><p>Bandpassed images, commonly used for edge detection, also retain information about intensities between the edge boundaries. Using the familiar Laplacian-of-Gaussian as a bandpass filter, we present a method to extract and code the edge-associated information (edge primitives) and recover an image representation with high structural fidelity. We demonstrate that the edge-primitives representation is compact and therefore can be coded with high compression ratios.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"56 1","pages":"Pages 1-7"},"PeriodicalIF":0.0,"publicationDate":"1994-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1994.1001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120876563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Contrast Enhancement Using the Laplacian-of-a-Gaussian Filter","authors":"Neycenssac F.","doi":"10.1006/cgip.1993.1034","DOIUrl":"10.1006/cgip.1993.1034","url":null,"abstract":"<div><p>A method of contrast enhancement for digital images is provided. As the method uses the LoG (Laplacian-of-a-Gaussian) filter, it is not significantly sensitive to noise. It is easy to develop this linear method when the Marr-Hildreth edge-detection technique has already been applied. LoG is roughly a bandpass filter.Therefore, only a single filter is required to enhance frequencies in one range. To enhance several frequency ranges, several standard deviation coefficients σ must be chosen for LoG convolutions. Progressive contrast ameliorations are obtained in this case. A description, an implementation, an estimate of efficiency, and a comparison with other methods are presented.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 6","pages":"Pages 447-463"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128839513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Image Encoding, Labeling, and Reconstruction from Differential Geometry","authors":"Barth E., Caelli T., Zetzsche C.","doi":"10.1006/cgip.1993.1033","DOIUrl":"10.1006/cgip.1993.1033","url":null,"abstract":"<div><p>In this paper we consider how the representation of images as surfaces, and their characterizations via surface differential forms, can be related to the concept of redundancy in the intensity signal. In contrast to common approaches, the basic surface types (planar, parabolic, elliptic/hyperbolic) are not seen as equal-priority classes, but as corresponding to different degrees of redundancy. This leads to a new approach to image representation and region labeling based upon generalized curvature measures. Furthermore, we employ different reconstruction algorithms to show that elliptic surface patches carry the significant information in natural images. Based upon deterministic and stochastic relaxation techniques, these algorithms allow one to reconstruct the original image from (i) \"elliptic intensities\" only and (ii) curvature measures which are zero for nonelliptic regions.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 6","pages":"Pages 428-446"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129949341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Analysis of Histogram-Based Thresholding Algorithms","authors":"Glasbey C.A.","doi":"10.1006/cgip.1993.1040","DOIUrl":"10.1006/cgip.1993.1040","url":null,"abstract":"<div><p>Eleven histogram-based global thresholding algorithms are presented in a common notational framework. Relationships among them are identified from 654 mixtures of two Gaussian distributions, plus effects of mixed pixels. The iterated version of Kittler and Illingworth′s minimum error algorithm (<em>Pattern Recognition</em>, 19, 1986, 41-47) is found to be best.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 6","pages":"Pages 532-537"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130725455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Physically Based Modeling and Control of Turning","authors":"Vandepanne M., Fiume E., Vranesic Z.","doi":"10.1006/cgip.1993.1038","DOIUrl":"10.1006/cgip.1993.1038","url":null,"abstract":"<div><p>Turning plays an important role in activities such as skiing and bicycling. The act of turning requires steering, which performs two functions: changing the direction of motion and ensuring that balance is maintained. We analyze and control turning motions for a simple physical model. The physically based motion of the simple model is then used as a basis for the motion of more complex display models. A phase-diagram description of periodic turning motions (as in slalom skiing) is presented. The phase diagram is used to construct a control algorithm parameterized in terms of the frequency, sharpness, and heading of the turns. A second method of control allows an animator to draw an arbitrary path for the turning figure to follow while avoiding obstacles. A finite-time optimization is used to find the best physically feasible motion that closely follows the desired path. Examples of alpine skiing, snowboarding, bicycling, and telemark skiing are given.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 6","pages":"Pages 507-521"},"PeriodicalIF":0.0,"publicationDate":"1993-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128302397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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