Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches for multiple feature representations. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns and is convolved with Gabor filter. The real part of this convolution is only preserved for further processing. Multiple features are extracted from the real parts of the convolved images using the proposed branch point based feature extraction techniques. The multiple features are then integrated at the decision level. AND and OR fusion rules are employed to combine the decisions taken by the individual matcher. Experiments conducted on a database of 100 users result in a False Acceptance Rate (FAR) of 0.1% for the Genuine Acceptance Rate (GAR) of 99% for decision level fusion.
{"title":"Biometric Authentication Based on Infrared Thermal Hand Vein Patterns","authors":"Amioy Kumar, M. Hanmandlu, V. Madasu, B. Lovell","doi":"10.1109/DICTA.2009.63","DOIUrl":"https://doi.org/10.1109/DICTA.2009.63","url":null,"abstract":"Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches for multiple feature representations. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns and is convolved with Gabor filter. The real part of this convolution is only preserved for further processing. Multiple features are extracted from the real parts of the convolved images using the proposed branch point based feature extraction techniques. The multiple features are then integrated at the decision level. AND and OR fusion rules are employed to combine the decisions taken by the individual matcher. Experiments conducted on a database of 100 users result in a False Acceptance Rate (FAR) of 0.1% for the Genuine Acceptance Rate (GAR) of 99% for decision level fusion.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"165 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":"123330216","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}
M. Awrangjeb, Guojun Lu, C. Fraser, M. Ravanbakhsh
The previously proposed contour-based multi-scale corner detector based on the chord-to-point distance accumulation (CPDA) technique has proved its superior robustness over many other single- and multi-scale detectors. However, the original CPDA detector is computationally expensive since it calculates the CPDA discrete curvature on each point of the curve. The proposed improvement obtains a set of probable candidate points before the CPDA curvature estimation. The CPDA curvature is estimated on these chosen candidate points only. Consequently, the improved CPDA detector becomes faster, while retaining a similar robustness to the original CPDA detector.
{"title":"A Fast Corner Detector Based on the Chord-to-Point Distance Accumulation Technique","authors":"M. Awrangjeb, Guojun Lu, C. Fraser, M. Ravanbakhsh","doi":"10.1109/DICTA.2009.91","DOIUrl":"https://doi.org/10.1109/DICTA.2009.91","url":null,"abstract":"The previously proposed contour-based multi-scale corner detector based on the chord-to-point distance accumulation (CPDA) technique has proved its superior robustness over many other single- and multi-scale detectors. However, the original CPDA detector is computationally expensive since it calculates the CPDA discrete curvature on each point of the curve. The proposed improvement obtains a set of probable candidate points before the CPDA curvature estimation. The CPDA curvature is estimated on these chosen candidate points only. Consequently, the improved CPDA detector becomes faster, while retaining a similar robustness to the original CPDA detector.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"115 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":"123466481","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}
Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog. Recently, several investigations have presented methods for recovering depth maps using only the information contained in a single foggy image. Each technique estimates the depth of each pixel independently, and assumes neighbouring pixels will have similar depths. In this work, we employ the fact that images containing fog are captured from outdoor cameras. As a result, the scene geometry is usually dominated by a ground plane. More importantly, objects which appear towards the top of the image are usually further away. We show how this preference (implemented as a soft constraint) is compatible with the alpha-expansion optimization technique and illustrate how it can be used to improve the robustness of any single image dehazing technique.
{"title":"Improved Single Image Dehazing Using Geometry","authors":"Peter Carr, R. Hartley","doi":"10.1109/DICTA.2009.25","DOIUrl":"https://doi.org/10.1109/DICTA.2009.25","url":null,"abstract":"Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog. Recently, several investigations have presented methods for recovering depth maps using only the information contained in a single foggy image. Each technique estimates the depth of each pixel independently, and assumes neighbouring pixels will have similar depths. In this work, we employ the fact that images containing fog are captured from outdoor cameras. As a result, the scene geometry is usually dominated by a ground plane. More importantly, objects which appear towards the top of the image are usually further away. We show how this preference (implemented as a soft constraint) is compatible with the alpha-expansion optimization technique and illustrate how it can be used to improve the robustness of any single image dehazing technique.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"22 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":"114799213","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}
Y. Gal, A. Mehnert, A. Bradley, D. Kennedy, S. Crozier
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).
{"title":"Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast","authors":"Y. Gal, A. Mehnert, A. Bradley, D. Kennedy, S. Crozier","doi":"10.1109/DICTA.2009.29","DOIUrl":"https://doi.org/10.1109/DICTA.2009.29","url":null,"abstract":"The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"47 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":"134414454","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 segmentation is a crucial phase in the implementation of an iris recognition system. In this paper we investigate a novel technique for iris segmentation. Morphological operations and area computation are applied together with other iris segmentation techniques in order to increase the speed and accuracy of the preprocessing phase. A rough approximation of the pupil’s location is first determined in the initial stage, followed by edge detection and circular Hough transform for accurate iris segmentation. The edge image used to localize the outer iris border is modified increasing the speed and accuracy of the process. Finally, we investigate the effect of eyelids detection using a parabolic curve fitting technique. Two data sets of eye images are used to evaluate the proposed techniques. Experimental results show that the proposed segmentation technique is efficient and performs well on both data sets of images.
{"title":"An Efficient and Accurate Iris Segmentation Technique","authors":"Nitin K. Mahadeo, Nandita Bhattacharjee","doi":"10.1109/DICTA.2009.65","DOIUrl":"https://doi.org/10.1109/DICTA.2009.65","url":null,"abstract":"Accurate segmentation is a crucial phase in the implementation of an iris recognition system. In this paper we investigate a novel technique for iris segmentation. Morphological operations and area computation are applied together with other iris segmentation techniques in order to increase the speed and accuracy of the preprocessing phase. A rough approximation of the pupil’s location is first determined in the initial stage, followed by edge detection and circular Hough transform for accurate iris segmentation. The edge image used to localize the outer iris border is modified increasing the speed and accuracy of the process. Finally, we investigate the effect of eyelids detection using a parabolic curve fitting technique. Two data sets of eye images are used to evaluate the proposed techniques. Experimental results show that the proposed segmentation technique is efficient and performs well on both data sets of images.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"14 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":"131981298","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}
Approximate solutions to labelling problems can be found using binary graph cuts and either the alpha-expansion or alpha-beta swap algorithms. In some specific cases, an exact solution can be computed by constructing a multilabel graph. However, in many practical applications the multilabel graph construction is infeasible due to its excessively large memory requirements. In this work, we expand the concept of alpha-beta swap to consider larger sets of labels at each iteration, and demonstrate how this approach is able to produce good approximate solutions to problems which can be solved using multilabel graph cuts. Furthermore, we show how alpha-expansion is a special case of multilabel swap, and from this new formulation, illustrate how alpha-expansion is now able to handle binary energy functions which do not satisfy the triangle inequality. Compared to alpha-beta swap, multilabel swap is able to produce an approximate solution in a shorter amount of time. We demonstrate the merits of our approach by considering the denoising and stereo problems. We illustrate how multilabel swap can be used in a recursive fashion to produce a good solution quickly and without requiring excessive amounts of memory.
{"title":"Solving Multilabel Graph Cut Problems with Multilabel Swap","authors":"Peter Carr, R. Hartley","doi":"10.1109/DICTA.2009.90","DOIUrl":"https://doi.org/10.1109/DICTA.2009.90","url":null,"abstract":"Approximate solutions to labelling problems can be found using binary graph cuts and either the alpha-expansion or alpha-beta swap algorithms. In some specific cases, an exact solution can be computed by constructing a multilabel graph. However, in many practical applications the multilabel graph construction is infeasible due to its excessively large memory requirements. In this work, we expand the concept of alpha-beta swap to consider larger sets of labels at each iteration, and demonstrate how this approach is able to produce good approximate solutions to problems which can be solved using multilabel graph cuts. Furthermore, we show how alpha-expansion is a special case of multilabel swap, and from this new formulation, illustrate how alpha-expansion is now able to handle binary energy functions which do not satisfy the triangle inequality. Compared to alpha-beta swap, multilabel swap is able to produce an approximate solution in a shorter amount of time. We demonstrate the merits of our approach by considering the denoising and stereo problems. We illustrate how multilabel swap can be used in a recursive fashion to produce a good solution quickly and without requiring excessive amounts of memory.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"1 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":"129227866","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}
Benson S. Y. Lam, Yongsheng Gao, Alan Wee-Chung Liew
Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.
{"title":"Optimizing Sharpness Measure for Bright Lesion Detection in Retinal Image Analysis","authors":"Benson S. Y. Lam, Yongsheng Gao, Alan Wee-Chung Liew","doi":"10.1109/DICTA.2009.14","DOIUrl":"https://doi.org/10.1109/DICTA.2009.14","url":null,"abstract":"Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"475 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":"127555572","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}
This paper presents a new fast method to map between images and their digital projections based on the Number Theoretic Transform (NTT) and the Finite Radon Transform (FRT). The FRT is a Discrete Radon Transform (DRT) defined on the same finite geometry as the Finite or Discrete Fourier Transform (DFT). Consequently, it may be inverted directly and exactly via the Fast Fourier Transform (FFT) without any interpolation or filtering [1]. As with the FFT, the FRT can be adapted to square images of arbitrary sizes such as dyadic images, prime-adic images and arbitrary-sized images. However, its simplest form is that of prime-sized images [2]. The FRT also preserves the discrete versions of both the Fourier Slice Theorem (FST) and Convolution Property of the Radon Transform (RT). The NTT is also defined on the same geometry as the DFT and preserves the Circular Convolution Property (CCP) of the DFT [3, 4]. This paper shows that the Slice Theorem is also valid within the NTT and that it can be utilized as a new exact, integer-only and fast inversion scheme for the FRT, with the same computational complexity as the FFT. Digital convolutions and exact digital filtering of projections can also be performed using this Number Theoretic FRT (NFRT).
{"title":"A Fast Number Theoretic Finite Radon Transform","authors":"S. Chandra, I. Svalbe","doi":"10.1109/DICTA.2009.67","DOIUrl":"https://doi.org/10.1109/DICTA.2009.67","url":null,"abstract":"This paper presents a new fast method to map between images and their digital projections based on the Number Theoretic Transform (NTT) and the Finite Radon Transform (FRT). The FRT is a Discrete Radon Transform (DRT) defined on the same finite geometry as the Finite or Discrete Fourier Transform (DFT). Consequently, it may be inverted directly and exactly via the Fast Fourier Transform (FFT) without any interpolation or filtering [1]. As with the FFT, the FRT can be adapted to square images of arbitrary sizes such as dyadic images, prime-adic images and arbitrary-sized images. However, its simplest form is that of prime-sized images [2]. The FRT also preserves the discrete versions of both the Fourier Slice Theorem (FST) and Convolution Property of the Radon Transform (RT). The NTT is also defined on the same geometry as the DFT and preserves the Circular Convolution Property (CCP) of the DFT [3, 4]. This paper shows that the Slice Theorem is also valid within the NTT and that it can be utilized as a new exact, integer-only and fast inversion scheme for the FRT, with the same computational complexity as the FFT. Digital convolutions and exact digital filtering of projections can also be performed using this Number Theoretic FRT (NFRT).","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"24 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":"115449366","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 public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.
{"title":"Crowd Counting Using Multiple Local Features","authors":"D. Ryan, S. Denman, C. Fookes, S. Sridharan","doi":"10.1109/DICTA.2009.22","DOIUrl":"https://doi.org/10.1109/DICTA.2009.22","url":null,"abstract":"In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"237 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":"115070553","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}
Wen-Shan Wang, Ya-Fei Hung, Jen-Kuei Yang, S. Tseng
Connected component labeling is an indispensable and one of most time consuming tasks of the applications in computer vision. Many labeling algorithms have been introduced, such as scan plus connection table, scan plus union-find, and contour tracing etc. They would rather use byte data than bit data to represent the binary pixel, which is either 1 or 0, due to the heavy cost of bitwise operations. This paper will propose a mechanism employing bit data to stand for the binary image pixels and labeling multiple pixels in one labeling process so that it can turn the weakness of bit data into the strength. According to the test results run in ARM926EJ-S, this new mechanism can double the speed of the scanning and analysis phases of an array based scan plus union-find algorithm. Besides, the much smaller binary image buffer needed by this mechanism is critical for the limited hardware-resource embedded devices, which are implemented in the field of computer vision gradually.
{"title":"The Dynamic Decision Switch for Multiple Pixel Connected Component Labeling Algorithm","authors":"Wen-Shan Wang, Ya-Fei Hung, Jen-Kuei Yang, S. Tseng","doi":"10.1109/DICTA.2009.30","DOIUrl":"https://doi.org/10.1109/DICTA.2009.30","url":null,"abstract":"Connected component labeling is an indispensable and one of most time consuming tasks of the applications in computer vision. Many labeling algorithms have been introduced, such as scan plus connection table, scan plus union-find, and contour tracing etc. They would rather use byte data than bit data to represent the binary pixel, which is either 1 or 0, due to the heavy cost of bitwise operations. This paper will propose a mechanism employing bit data to stand for the binary image pixels and labeling multiple pixels in one labeling process so that it can turn the weakness of bit data into the strength. According to the test results run in ARM926EJ-S, this new mechanism can double the speed of the scanning and analysis phases of an array based scan plus union-find algorithm. Besides, the much smaller binary image buffer needed by this mechanism is critical for the limited hardware-resource embedded devices, which are implemented in the field of computer vision gradually.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"19 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":"124976365","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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