A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms. The proposed method is tested on two sets of mammograms: a set of 55 mammograms chosen from a publicly available Mini-MIAS database, and a set of 37 mammograms selected from a local database. The method performance is evaluated in conjunction with three different preprocessing filters: gaussian, anisotropic and neutrosophic. Results show that the automatic tuning has the potential to produce state-of-the art segmentation of mass-like objects in mammograms. The neutrosophic filtering provided the best performance.
{"title":"Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms","authors":"M. Bajger, Fei Ma, M. Bottema","doi":"10.1109/DICTA.2009.72","DOIUrl":"https://doi.org/10.1109/DICTA.2009.72","url":null,"abstract":"A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms. The proposed method is tested on two sets of mammograms: a set of 55 mammograms chosen from a publicly available Mini-MIAS database, and a set of 37 mammograms selected from a local database. The method performance is evaluated in conjunction with three different preprocessing filters: gaussian, anisotropic and neutrosophic. Results show that the automatic tuning has the potential to produce state-of-the art segmentation of mass-like objects in mammograms. The neutrosophic filtering provided the best performance.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125984285","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}
Recent efforts show that it is possible to calibrate a surveillance camera simply from observing a walking human. This procedure can be seen as a special application of the camera self-calibration technique. Several methods have been proposed along this line, but most of them have certain restrictions, such as require the human walking at a constant speed, or require two orthogonal lines marked on the ground, etc. This has hindered their applicability. In this paper we propose a new method that removes most of these restrictions. By clever uses of the cross-ratio relationship in projective geometry, our method shows it is possible to directly estimate a full 3x4 camera projection matrix without first decomposing it into physical parameters like focal-length, optical center, etc. Extensive experiments on real data show our algorithm performs well in real situations.
{"title":"A Direct Method to Self-Calibrate a Surveillance Camera by Observing a Walking Pedestrian","authors":"Worapan Kusakunniran, Hongdong Li, Jian Zhang","doi":"10.1109/DICTA.2009.49","DOIUrl":"https://doi.org/10.1109/DICTA.2009.49","url":null,"abstract":"Recent efforts show that it is possible to calibrate a surveillance camera simply from observing a walking human. This procedure can be seen as a special application of the camera self-calibration technique. Several methods have been proposed along this line, but most of them have certain restrictions, such as require the human walking at a constant speed, or require two orthogonal lines marked on the ground, etc. This has hindered their applicability. In this paper we propose a new method that removes most of these restrictions. By clever uses of the cross-ratio relationship in projective geometry, our method shows it is possible to directly estimate a full 3x4 camera projection matrix without first decomposing it into physical parameters like focal-length, optical center, etc. Extensive experiments on real data show our algorithm performs well in real situations.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126520956","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}
In this paper we present the geometric property of perspective invariant angle ordering; the order of angles between point features. We describe how this can be used to exploit the structure of the appearance of features on planar or near planar surfaces to improve precision for localisation and object recognition. We show test results on real-world images that show marked improvement over straight bag-of-features approaches.
{"title":"Perspective Invariant Angle Ordering","authors":"David Shaw, N. Barnes","doi":"10.1109/DICTA.2009.50","DOIUrl":"https://doi.org/10.1109/DICTA.2009.50","url":null,"abstract":"In this paper we present the geometric property of perspective invariant angle ordering; the order of angles between point features. We describe how this can be used to exploit the structure of the appearance of features on planar or near planar surfaces to improve precision for localisation and object recognition. We show test results on real-world images that show marked improvement over straight bag-of-features approaches.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128158177","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}
Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.
{"title":"Extraction of Road Lanes from High-Resolution Stereo Aerial Imagery Based on Maximum Likelihood Segmentation and Texture Enhancement","authors":"Hang Jin, Yanming Feng, Zhengrong Li","doi":"10.1109/DICTA.2009.52","DOIUrl":"https://doi.org/10.1109/DICTA.2009.52","url":null,"abstract":"Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116349675","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}
The integral image is an intermediate image representation that allows rapid calculation of rectangular features at constant speed, irrespective of filter size, and is particularly useful for multi-scale computer vision algorithms like Speeded-Up Robust Features (SURF). Although calculation of the integral image involves simple addition operations, the total number of operations is significant due to the generally large size of image data. Recursive equations allow considerable reduction in the required number of addition operations but require calculation of the integral image in a serial fashion. This is generally not desirable for real-time embedded vision systems with strict time limitations and low-powered but parallel hardware resources. With the objective of minimizing the hardware resources involved, this paper proposes two novel hardware algorithms based on decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way with out significantly increasing the number of addition operations.
{"title":"Novel Hardware Algorithms for Row-Parallel Integral Image Calculation","authors":"Shoaib Ehsan, A. Clark, K. Mcdonald-Maier","doi":"10.1109/DICTA.2009.20","DOIUrl":"https://doi.org/10.1109/DICTA.2009.20","url":null,"abstract":"The integral image is an intermediate image representation that allows rapid calculation of rectangular features at constant speed, irrespective of filter size, and is particularly useful for multi-scale computer vision algorithms like Speeded-Up Robust Features (SURF). Although calculation of the integral image involves simple addition operations, the total number of operations is significant due to the generally large size of image data. Recursive equations allow considerable reduction in the required number of addition operations but require calculation of the integral image in a serial fashion. This is generally not desirable for real-time embedded vision systems with strict time limitations and low-powered but parallel hardware resources. With the objective of minimizing the hardware resources involved, this paper proposes two novel hardware algorithms based on decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way with out significantly increasing the number of addition operations.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124724338","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}
A neuro-physiologically inspired model is presented for the contrast enhancement of images. The contrast of an image is calculated using simulated on- and off-centre receptive fields whereby obtaining the corresponding two contrast maps. We propose an adaptive asymmetric gain control function that is applied to the two contrast maps which are then used to reconstruct the image resulting in its contrast enhancement. The image's mean luminance can be adjusted as desired by adjusting the asymmetricity between the gain control factors of the two maps. The model performs local contrast enhancement in the contrast domain of an image where it lends itself very naturally to such adjustments. Furthermore, the model is extended on to colour images using the concept of colour-opponent receptive fields found in the human visual system. The colour model enhances the contrast right in the colour space without extracting the luminance information from it. Being neuro-physiologically plausible, this model can be beneficial in theorising and understanding the gain control mechanisms in the primate visual system. We compare our results with the CLAHE algorithm.
{"title":"Biologically Inspired Contrast Enhancement Using Asymmetric Gain Control","authors":"Asim A. Khwaja, Roland Göcke","doi":"10.1109/DICTA.2009.75","DOIUrl":"https://doi.org/10.1109/DICTA.2009.75","url":null,"abstract":"A neuro-physiologically inspired model is presented for the contrast enhancement of images. The contrast of an image is calculated using simulated on- and off-centre receptive fields whereby obtaining the corresponding two contrast maps. We propose an adaptive asymmetric gain control function that is applied to the two contrast maps which are then used to reconstruct the image resulting in its contrast enhancement. The image's mean luminance can be adjusted as desired by adjusting the asymmetricity between the gain control factors of the two maps. The model performs local contrast enhancement in the contrast domain of an image where it lends itself very naturally to such adjustments. Furthermore, the model is extended on to colour images using the concept of colour-opponent receptive fields found in the human visual system. The colour model enhances the contrast right in the colour space without extracting the luminance information from it. Being neuro-physiologically plausible, this model can be beneficial in theorising and understanding the gain control mechanisms in the primate visual system. We compare our results with the CLAHE algorithm.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126862816","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}
Hyperspectral imagery generally contains enormous amounts of data due to hundreds of spectral bands. As recent researchers have discovered, many of the bands are highly correlated and may provide redundant information for the classification related problems. Therefore, feature selection is very important in hyperspectral image processing problem. ''Pathwise Coordinate Descent'' algorithm is the ''one-at-a-time'' coordinate-wise descent algorithm for a class of convex optimization problems. When applied on the L1-regularized regression (lasso) problem, the algorithm can handle large problems and can also efficiently obtain sparse features in a comparatively very low timing cost. Through computing the solutions for a decreasing sequence of regularization parameters, the algorithm also combines model selection procedure into itself. In this paper, we utilize the multinomial logistic regression with lasso, elastic-net convex penalties on hyperspectral image classification. Pathwise Coordinate Descent is used for estimation these models. Experimental results demonstrate that, in the context of the hyperspectral data classification problem, models obtained by Pathwise Coordinate Descent algorithm do effectively achieve a sparse feature subsets and very good classification results with very low computational costs.
{"title":"Regularized Multinomial Regression Method for Hyperspectral Data Classification via Pathwise Coordinate Optimization","authors":"Jiming Li, Y. Qian","doi":"10.1109/DICTA.2009.89","DOIUrl":"https://doi.org/10.1109/DICTA.2009.89","url":null,"abstract":"Hyperspectral imagery generally contains enormous amounts of data due to hundreds of spectral bands. As recent researchers have discovered, many of the bands are highly correlated and may provide redundant information for the classification related problems. Therefore, feature selection is very important in hyperspectral image processing problem. ''Pathwise Coordinate Descent'' algorithm is the ''one-at-a-time'' coordinate-wise descent algorithm for a class of convex optimization problems. When applied on the L1-regularized regression (lasso) problem, the algorithm can handle large problems and can also efficiently obtain sparse features in a comparatively very low timing cost. Through computing the solutions for a decreasing sequence of regularization parameters, the algorithm also combines model selection procedure into itself. In this paper, we utilize the multinomial logistic regression with lasso, elastic-net convex penalties on hyperspectral image classification. Pathwise Coordinate Descent is used for estimation these models. Experimental results demonstrate that, in the context of the hyperspectral data classification problem, models obtained by Pathwise Coordinate Descent algorithm do effectively achieve a sparse feature subsets and very good classification results with very low computational costs.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123400656","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}
The ‘Fuzzy Co-Clustering Algorithm for Images (FCCI)’ technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. Experiments show the efficacy of the proposed method in extracting malignant blotches from different types of pigmented skin lesion images.
{"title":"Blotch Detection in Pigmented Skin Lesions Using Fuzzy Co-clustering and Texture Segmentation","authors":"V. Madasu, B. Lovell","doi":"10.1109/DICTA.2009.15","DOIUrl":"https://doi.org/10.1109/DICTA.2009.15","url":null,"abstract":"The ‘Fuzzy Co-Clustering Algorithm for Images (FCCI)’ technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. Experiments show the efficacy of the proposed method in extracting malignant blotches from different types of pigmented skin lesion images.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115125493","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}
Image watermarking is the robust, imperceptible embedding of a small quantity of data into a digital image, and the subsequent recovery of this data, perhaps after the watermarked image has been distorted. We present a new watermarking technique which is robust to many image distortions, in particular arbitrary affine transformations of the image. The method achieves its robustness through the use of one-dimensional chirp functions. An affine-invariant detection method exists for such functions using a Radon transform, yet they are not detected trivially by a malicious attacker. The method also provides a way to determine any affine transformation applied to the watermarked image by using an affine-invariant property of groups of intersecting lines.
{"title":"Affine-Invariant Image Watermarking Using the Hyperbolic Chirp","authors":"P. Fletcher, K. Larkin, S. J. Hardy","doi":"10.1109/DICTA.2009.12","DOIUrl":"https://doi.org/10.1109/DICTA.2009.12","url":null,"abstract":"Image watermarking is the robust, imperceptible embedding of a small quantity of data into a digital image, and the subsequent recovery of this data, perhaps after the watermarked image has been distorted. We present a new watermarking technique which is robust to many image distortions, in particular arbitrary affine transformations of the image. The method achieves its robustness through the use of one-dimensional chirp functions. An affine-invariant detection method exists for such functions using a Radon transform, yet they are not detected trivially by a malicious attacker. The method also provides a way to determine any affine transformation applied to the watermarked image by using an affine-invariant property of groups of intersecting lines.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133182096","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}
Digital vector maps are an expensive commodity. Like any digital data, they are also very easy to copy. Piracy (or unauthorised reselling) of maps will become increasingly common in the future. This project looks at embedding a hidden message or watermark in a digital map so that its original authorship can be ascertained. This information enables a 3rd party to verify a seller's rights to the map and aid in the resolution of copyright disputes. Some other vector watermarking schemes, look at vector maps as a cloud of coordinates, to be perturbed in some way that is independent of actual usage. These papers generally do not discuss how large a subset of the map is needed to reliably retain the watermark. Instead, we concentrate on watermarking map feature lines, so that feature extraction from a watermarked vector map may not necessarily compromise watermark integrity.
{"title":"A Shape-Based Vector Watermark for Digital Mapping","authors":"S. Bird, C. Bellman, R. V. Schyndel","doi":"10.1109/DICTA.2009.78","DOIUrl":"https://doi.org/10.1109/DICTA.2009.78","url":null,"abstract":"Digital vector maps are an expensive commodity. Like any digital data, they are also very easy to copy. Piracy (or unauthorised reselling) of maps will become increasingly common in the future. This project looks at embedding a hidden message or watermark in a digital map so that its original authorship can be ascertained. This information enables a 3rd party to verify a seller's rights to the map and aid in the resolution of copyright disputes. Some other vector watermarking schemes, look at vector maps as a cloud of coordinates, to be perturbed in some way that is independent of actual usage. These papers generally do not discuss how large a subset of the map is needed to reliably retain the watermark. Instead, we concentrate on watermarking map feature lines, so that feature extraction from a watermarked vector map may not necessarily compromise watermark integrity.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"43 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132536785","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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