{"title":"Local colour image segmentation using singular value decomposition","authors":"C. B. Phillips, R. Jain","doi":"10.1109/IAI.1998.666876","DOIUrl":"https://doi.org/10.1109/IAI.1998.666876","url":null,"abstract":"A method was developed to segment images of complex scenes based on color content. The output of an interest operator provides focus toward regions within an image to be sampled for color content. Statistics for each data set sampled are used to cluster and estimate bounded regions within a transformed color space. Each region respectively represents a specific set. Mappings to transformed regions of color space are found using the singular value decomposition. The mean and variance of each color sample in the transformed color space represent characteristic features for their sampled set of points. Color segmentation is accomplished by establishing whether image pixels belong to any subset represented by the characteristic features. This work contributes a method to color-segment targets in images using local color information within an image stream.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133613781","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":"Adaptive reconstruction using multiple views","authors":"M. Ulvklo, H. Knutsson, G. Granlund","doi":"10.1109/IAI.1998.666858","DOIUrl":"https://doi.org/10.1109/IAI.1998.666858","url":null,"abstract":"This paper introduces a novel algorithm for extracting the optical flow obtained from a translating camera in a static scene. Occlusion between objects is incorporated as a natural component in a scene reconstruction strategy by first evaluating and reconstructing the foreground and then excluding its influence on the partly occluded objects behind.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134600398","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":"Lexicodes in the space of foot patterns for image classification","authors":"D. Ashlock, J. Davidson","doi":"10.1109/IAI.1998.666867","DOIUrl":"https://doi.org/10.1109/IAI.1998.666867","url":null,"abstract":"In this paper we extend work presented in Ashlock and Davidson (1997) on the automatic classification of textures with foot patterns. We begin by verifying that a technique suggested in the earlier research permits us to distinguish between textures which the original technique could not classify. We then define a metric on the space of the foot patterns and construct lexicodes of the foot patterns that yield a new technique for distinguishing the textures. The lexicodes of the foot patterns are used to construct vectors of entropy values in R/sup n/ and a clustering algorithm on those vectors is used to classify the textures. This new technique uses much of the machinery of the original technique but is unsupervised, requiring no training examples. The results of using this unsupervised technique are very similar to the results originally obtained with the supervised algorithm, including the inability to distinguish two of the six texture types in the test set. We blend the technique for distinguishing the two similar textures with the lexicode technique with partial success. We present results on binary image data but our goal is to achieve automatic classification of any gray-value texture. This has the potential to be used in automated object recognition, image retrieval from databases, and compression and data transmission applications.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653392","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}
D. Sheppard, K. Panchapakesan, A. Bilgin, B. Hunt, M. Marcellin
{"title":"Removal of image defocus and motion blur effects with a nonlinear interpolative vector quantizer","authors":"D. Sheppard, K. Panchapakesan, A. Bilgin, B. Hunt, M. Marcellin","doi":"10.1109/IAI.1998.666850","DOIUrl":"https://doi.org/10.1109/IAI.1998.666850","url":null,"abstract":"In this paper, results are presented which demonstrate the removal of image defocus and motion blur effects using an algorithm based on nonlinear interpolative vector quantization (NLIVQ). The algorithm is trained on original and diffraction-limited image pairs which are representative of the class of images of interest. The discrete cosine transform is used in the code-book design process to control complexity. Imagery processed with this algorithm demonstrate both qualitative and quantitative improvements (as measured by the peak signal-to-noise-ratio before and after processing).","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131395794","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":"Color recognition with a camera: a supervised algorithm for classification","authors":"T. Lalanne, C. Lempereur","doi":"10.1109/IAI.1998.666885","DOIUrl":"https://doi.org/10.1109/IAI.1998.666885","url":null,"abstract":"This paper presents a color recognition algorithm using neuronal techniques, applied to the characterization of thermal paints. The color of such a coating changes with temperature, allowing temperature maps to be established on gas turbine engine components for instance. After a formulation of the classification algorithm, performances are evaluated on a thermal paint samples set. The influence of lighting is studied. The results are compared with those obtained by more classical segmentation algorithms. A temperature profile on a test piece is finally achieved.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134166861","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":"Projected mean curvature smoothing for vector-valued imagery","authors":"A. Yezzi","doi":"10.1109/IAI.1998.666871","DOIUrl":"https://doi.org/10.1109/IAI.1998.666871","url":null,"abstract":"In this note, we formulate a general modified mean curvature based equation for image smoothing and enhancement. The key idea is to consider the image as a graph in some R/sup n/, and apply a mean curvature type motion to the graph. We consider some special cases relevant to greyscale and color images.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123372492","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":"Protein structure determination using 3-D grayscale skeletonization","authors":"K.M. Lee, P. Bhattacharya","doi":"10.1109/IAI.1998.666873","DOIUrl":"https://doi.org/10.1109/IAI.1998.666873","url":null,"abstract":"This paper describes ongoing work on an algorithm for performing 3-D grayscale skeletonization. The algorithm makes use of a variation of the gray weighted distance transform without performing any prior thresholding. It is being developed as a tool for structural biologists to utilize in determining the structure of large protein molecules.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134679","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":"Use of diffusion techniques for edge preservation for fractal coders","authors":"N. Bruner, R. Yarlagadda","doi":"10.1109/IAI.1998.666861","DOIUrl":"https://doi.org/10.1109/IAI.1998.666861","url":null,"abstract":"Based on the correlation of self-similarity, fractal coders compress digital images by relating image blocks pairs at different scales in the images. The pairing of large to small block sizes dictates the compression ratio and the quality of the reconstructed image. Inaccurate fractal mappings at large image block sizes increase losses of edge information, discontinuities at boundaries and blocking effects. Partitioning the large block into smaller blocks overcomes these problems but significantly lowers the compression ratios. In addition, partitioning does not insure the retention of significant edges. We show how diffusion techniques can be used to overcome some of these problems while preserving significant edge information at a lower bit rate cost. By expanding the basic diffusion equation to contain a scalar function based on the edges of the original image, we can use the diffusion process to smooth along the direction of significant edges and sharpen in the direction of the edges. In this manner, we can restore edge information and smooth discontinuities and blocking effects.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"19 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134387940","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":"Practical implementation of multirate convolution for multiresolution image processing","authors":"B. A. Thomas, J.J. Rodriguez","doi":"10.1109/IAI.1998.666888","DOIUrl":"https://doi.org/10.1109/IAI.1998.666888","url":null,"abstract":"This paper describes key issues associated with the computation of undecimated multiresolution signal decompositions. An effective solution is obtained by a general-purpose convolution programming model that automatically accounts for filter origin displacement and the sparse nature of upsampled filters. The handling of image boundaries during convolution is also discussed in detail.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133377345","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 reconstruction using the Viterbi algorithm","authors":"C. Miller, B. Hunt, M. Neifeld, M. Marcellin","doi":"10.1109/IAI.1998.666878","DOIUrl":"https://doi.org/10.1109/IAI.1998.666878","url":null,"abstract":"Many systems in widespread use concentrate on the imaging of binary objects, e.g., the archival storage of text documents on microfilm or the facsimile transmission of text. Due to the imperfect nature of such systems, the binary image is unavoidably corrupted by blur and noise to form a grey-scale image. We present a technique to reverse this degradation which maps the binary object reconstruction problem into a Viterbi state-trellis. We assign states of the trellis to possible outcomes of the reconstruction estimate and search the trellis in the usual optimal fashion. Our method yields superior estimates of the original binary object over a wide range of signal-to-noise ratios (SNR) when compared with conventional Wiener filter (WF) estimates. For moderate blur and SNR levels, the estimates produced approach the maximum likelihood (ML) bound on estimation performance.","PeriodicalId":373701,"journal":{"name":"1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126979028","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}