The Experts below are selected from a list of 4632 Experts worldwide ranked by ideXlab platform
Brian C. Lovell - One of the best experts on this subject based on the ideXlab platform.
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Square Patch Feature: Faster Weak-Classifier for robust object detection
2010 11th International Conference on Control Automation Robotics & Vision, 2010Co-Authors: Yasir M. Mustafah, Abbas Bigdeli, Amelia W. Azman, Farhad Dadgostar, Brian C. LovellAbstract:This paper presents a novel generic Weak Classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other Weak Classifiers shows improvement. Each Weak Classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the Weak Classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like Classifier. In addition to the faster computation, the Weak Classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature Classifier is as accurate as the Viola-Jones Haar-like Classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
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ICARCV - Square Patch Feature: Faster Weak-Classifier for robust object detection
2010 11th International Conference on Control Automation Robotics & Vision, 2010Co-Authors: Yasir M. Mustafah, Abbas Bigdeli, Amelia W. Azman, Farhad Dadgostar, Brian C. LovellAbstract:This paper presents a novel generic Weak Classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other Weak Classifiers shows improvement. Each Weak Classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the Weak Classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like Classifier. In addition to the faster computation, the Weak Classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature Classifier is as accurate as the Viola-Jones Haar-like Classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
Lars Petersson - One of the best experts on this subject based on the ideXlab platform.
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The histogram feature - a resource-efficient Weak Classifier
2008 IEEE Intelligent Vehicles Symposium, 2008Co-Authors: Niklas Pettersson, Lars Petersson, Lars AnderssonAbstract:This paper presents a Weak Classifier that is extremely fast to compute, yet highly discriminant. This Weak Classifier may be used in, for example, a boosting framework and is the result of a novel way of organizing and evaluating Histograms of Oriented Gradients. The method requires only one access to main memory to evaluate each feature, in comparison with the more well-known Haar features which require somewhere between six and nine memory accesses to evaluate each feature. This low memory bandwidth makes the Weak Classifier especially ideal for use in small systems with little or no memory cache available. The presented Weak Classifier has been extensively tested in a boosted framework on data sets consisting of pedestrians and various road signs. The Classifier yields detection results that are far superior than the results obtained from Haar features when tested on road signs and similar structures, whereas the detection results are comparable to those of Haar features when tested on pedestrians. In addition, the computational resources necessary for these results have been shown to be considerably smaller for the new Weak Classifier.
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On the importance of accurate Weak Classifier learning for boosted Weak Classifiers
2008 IEEE Intelligent Vehicles Symposium, 2008Co-Authors: Gary Overett, Lars PeterssonAbstract:Recent work has shown that improving model learning for Weak Classifiers can yield significant gains in the overall accuracy of a boosted Classifier. However, most published Classifier boosting research relies only on rudimentary learning techniques for Weak Classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong Classifier, it remains to be shown how much can yet be gained by further improving the model learning at the Weak Classifier level. This paper derives a very accurate model learning method for Weak Classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate Weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using Classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.
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Response Binning: Improved Weak Classifiers for Boosting
2006 IEEE Intelligent Vehicles Symposium, 1Co-Authors: B. Rasolzadeh, Lars Petersson, Niklas PetterssonAbstract:This paper demonstrates the value of improving the discriminating strength of Weak Classifiers in the context of boosting by using response binning. The reasoning is centered around, but not limited to, the well known Haar-features used by Viola and Jones (2001) in their face detection/pedestrian detection systems. It is shown that using a Weak Classifier based on a single threshold is sub-optimal and in the case of the Haar-feature inadequate. A more general method for features with multi-modal responses is derived that is easily used in boosting mechanisms that accepts a confidence measure, such as the RealBoost algorithm. The method is evaluated by boosting a single stage Classifier and compare the performance to previous approaches
Yasir M. Mustafah - One of the best experts on this subject based on the ideXlab platform.
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Square Patch Feature: Faster Weak-Classifier for robust object detection
2010 11th International Conference on Control Automation Robotics & Vision, 2010Co-Authors: Yasir M. Mustafah, Abbas Bigdeli, Amelia W. Azman, Farhad Dadgostar, Brian C. LovellAbstract:This paper presents a novel generic Weak Classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other Weak Classifiers shows improvement. Each Weak Classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the Weak Classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like Classifier. In addition to the faster computation, the Weak Classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature Classifier is as accurate as the Viola-Jones Haar-like Classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
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ICARCV - Square Patch Feature: Faster Weak-Classifier for robust object detection
2010 11th International Conference on Control Automation Robotics & Vision, 2010Co-Authors: Yasir M. Mustafah, Abbas Bigdeli, Amelia W. Azman, Farhad Dadgostar, Brian C. LovellAbstract:This paper presents a novel generic Weak Classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other Weak Classifiers shows improvement. Each Weak Classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the Weak Classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like Classifier. In addition to the faster computation, the Weak Classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature Classifier is as accurate as the Viola-Jones Haar-like Classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
Qianmei Feng - One of the best experts on this subject based on the ideXlab platform.
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Modeling and recognition of steel-plate surface defects based on a new backward boosting algorithm
The International Journal of Advanced Manufacturing Technology, 2018Co-Authors: Lianting Hu, Feng Xiang, Min Zhou, Qianmei FengAbstract:The surface quality of steel plates that have been widely used in the manufacturing industry directly affects the final product performance. The existing inspection system of steel-plate surface defects has some drawbacks: (1) an unbalance problem in the steel-plate surface defect dataset, and (2) the number of Classifiers used in the recognition process is insufficient for identifying stains, dirtiness, and other non-common defects. It is imperative to develop a new method to identify steel-plate surface defects. In this paper, the normalization technique and the synthetic minority over-sampling technique (SMOTE) are used to establish a steel-plate surface defect dataset with complete categories, balanced quantities, and normalized features. Based on the existing boosting algorithms in the literature, a new backward AdaBoost (AdaBoost.BK) algorithm is proposed for defect recognition. AdaBoost.BK selects the most suitable Weak Classifier by the filtering mechanism, thus increasing the number of Weak Classifiers that can be combined. Experiments show that the model not only improves the recognition accuracy of non-common defects, but also improves the accuracy of the whole classification.
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Modeling and recognition of steel-plate surface defects based on a new backward boosting algorithm
The International Journal of Advanced Manufacturing Technology, 2018Co-Authors: Lianting Hu, Feng Xiang, Min Zhou, Qianmei FengAbstract:The surface quality of steel plates that have been widely used in the manufacturing industry directly affects the final product performance. The existing inspection system of steel-plate surface defects has some drawbacks: (1) an unbalance problem in the steel-plate surface defect dataset, and (2) the number of Classifiers used in the recognition process is insufficient for identifying stains, dirtiness, and other non-common defects. It is imperative to develop a new method to identify steel-plate surface defects. In this paper, the normalization technique and the synthetic minority over-sampling technique (SMOTE) are used to establish a steel-plate surface defect dataset with complete categories, balanced quantities, and normalized features. Based on the existing boosting algorithms in the literature, a new backward AdaBoost (AdaBoost.BK) algorithm is proposed for defect recognition. AdaBoost.BK selects the most suitable Weak Classifier by the filtering mechanism, thus increasing the number of Weak Classifiers that can be combined. Experiments show that the model not only improves the recognition accuracy of non-common defects, but also improves the accuracy of the whole classification.
Farhad Dadgostar - One of the best experts on this subject based on the ideXlab platform.
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Square Patch Feature: Faster Weak-Classifier for robust object detection
2010 11th International Conference on Control Automation Robotics & Vision, 2010Co-Authors: Yasir M. Mustafah, Abbas Bigdeli, Amelia W. Azman, Farhad Dadgostar, Brian C. LovellAbstract:This paper presents a novel generic Weak Classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other Weak Classifiers shows improvement. Each Weak Classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the Weak Classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like Classifier. In addition to the faster computation, the Weak Classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature Classifier is as accurate as the Viola-Jones Haar-like Classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
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ICARCV - Square Patch Feature: Faster Weak-Classifier for robust object detection
2010 11th International Conference on Control Automation Robotics & Vision, 2010Co-Authors: Yasir M. Mustafah, Abbas Bigdeli, Amelia W. Azman, Farhad Dadgostar, Brian C. LovellAbstract:This paper presents a novel generic Weak Classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other Weak Classifiers shows improvement. Each Weak Classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the Weak Classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like Classifier. In addition to the faster computation, the Weak Classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature Classifier is as accurate as the Viola-Jones Haar-like Classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
