Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204262
A. Oikonomopoulos, I. Patras, M. Pantic
In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic, sparse, `visual words' and `visual verbs'. Evidence for the spatiotemporal localization of the activity are accumulated in a probabilistic spatiotemporal voting scheme. The local nature of our voting framework allows us to recover multiple activities that take place in the same scene, as well as activities in the presence of clutter and occlusions. We construct class-specific codebooks using the descriptors in the training set, where we take the spatial co-occurrences of pairs of codewords into account. The positions of the codeword pairs with respect to the object centre, as well as the frame in the training set in which they occur are subsequently stored in order to create a spatiotemporal model of codeword co-occurrences. During the testing phase, we use mean shift mode estimation in order to spatially segment the subject that performs the activities in every frame, and the Radon transform in order to extract the most probable hypotheses concerning the temporal segmentation of the activities within the continuous stream.
{"title":"An implicit spatiotemporal shape model for human activity localization and recognition","authors":"A. Oikonomopoulos, I. Patras, M. Pantic","doi":"10.1109/CVPRW.2009.5204262","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204262","url":null,"abstract":"In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization of characteristic, sparse, `visual words' and `visual verbs'. Evidence for the spatiotemporal localization of the activity are accumulated in a probabilistic spatiotemporal voting scheme. The local nature of our voting framework allows us to recover multiple activities that take place in the same scene, as well as activities in the presence of clutter and occlusions. We construct class-specific codebooks using the descriptors in the training set, where we take the spatial co-occurrences of pairs of codewords into account. The positions of the codeword pairs with respect to the object centre, as well as the frame in the training set in which they occur are subsequently stored in order to create a spatiotemporal model of codeword co-occurrences. During the testing phase, we use mean shift mode estimation in order to spatially segment the subject that performs the activities in every frame, and the Radon transform in order to extract the most probable hypotheses concerning the temporal segmentation of the activities within the continuous stream.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133820322","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204099
S. Mohottala, Shintaro Ono, M. Kagesawa, K. Ikeuchi
This paper presents a system that fuses data from a vision sensor and a laser sensor for detection and classification. Fusion of a vision sensor and a laser range sensor enables us to obtain 3D information of an object together with its textures, offering high reliability and robustness to outdoor conditions. To evaluate the performance of the system, it is applied to recognition of on-street parked vehicles scanned from a moving probe vehicle. The evaluation experiments show obviously successful results, with a detection rate of 100% and an accuracy over 95% in recognizing four vehicle classes.
{"title":"Fusion of a camera and a laser range sensor for vehicle recognition","authors":"S. Mohottala, Shintaro Ono, M. Kagesawa, K. Ikeuchi","doi":"10.1109/CVPRW.2009.5204099","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204099","url":null,"abstract":"This paper presents a system that fuses data from a vision sensor and a laser sensor for detection and classification. Fusion of a vision sensor and a laser range sensor enables us to obtain 3D information of an object together with its textures, offering high reliability and robustness to outdoor conditions. To evaluate the performance of the system, it is applied to recognition of on-street parked vehicles scanned from a moving probe vehicle. The evaluation experiments show obviously successful results, with a detection rate of 100% and an accuracy over 95% in recognizing four vehicle classes.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115870690","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204043
Xiao Han, L. Hibbard, V. Willcut
Brain structure segmentation is an important task in many neuroscience and clinical applications. In this paper, we introduce a novel MI-based dense deformable registration method and apply it to the automatic segmentation of detailed brain structures. Together with a multiple atlas fusion strategy, very accurate segmentation results were obtained, as compared with other reported methods in the literature. To make multi-atlas segmentation computationally feasible, we also propose to take advantage of the recent advancements in GPU technology and introduce a GPU-based implementation of the proposed registration method. With GPU acceleration it takes less than 8 minutes to compile a multi-atlas segmentation for each subject even with as many as 17 atlases, which demonstrates that the use of GPUs can greatly facilitate the application of such atlas-based segmentation methods in practice.
{"title":"GPU-accelerated, gradient-free MI deformable registration for atlas-based MR brain image segmentation","authors":"Xiao Han, L. Hibbard, V. Willcut","doi":"10.1109/CVPRW.2009.5204043","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204043","url":null,"abstract":"Brain structure segmentation is an important task in many neuroscience and clinical applications. In this paper, we introduce a novel MI-based dense deformable registration method and apply it to the automatic segmentation of detailed brain structures. Together with a multiple atlas fusion strategy, very accurate segmentation results were obtained, as compared with other reported methods in the literature. To make multi-atlas segmentation computationally feasible, we also propose to take advantage of the recent advancements in GPU technology and introduce a GPU-based implementation of the proposed registration method. With GPU acceleration it takes less than 8 minutes to compile a multi-atlas segmentation for each subject even with as many as 17 atlases, which demonstrates that the use of GPUs can greatly facilitate the application of such atlas-based segmentation methods in practice.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114562914","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204334
D. Mumford
Summary form only given. Linguists have proposed dozens of formalisms for grammars and now vision is weighing in with its versions based on its needs. Ulf Grenander has proposed general pattern theory, and has used grammar-like graphical parses of "thoughts" in the style of AI. One wants a natural, simple formalism treating all these cases. I want to pose this as a central problem in modeling intelligence. Pattern theory started in the 70's with the ideas of Ulf Grenander and his school at Brown. The aim is to analyze from a statistical point of view the patterns in all "signals" generated by the world, whether they be images, sounds, written text, DNA or protein strings, spike trains in neurons, time series of prices or weather, etc. Pattern theory proposes that the types of patterns-and the hidden variables needed to describe these patterns - found in one class of signals will often be found in the others and that their characteristic variability will be similar. The underlying idea is to find classes of stochastic models which can capture all the patterns that we see in nature, so that random samples from these models have the same "look and feel" as the samples from the world itself. Then the detection of patterns in noisy and ambiguous samples can be achieved by the use of Bayes' rule, a method that can be described as "analysis by synthesis".
{"title":"Is there a general structure for grammars?","authors":"D. Mumford","doi":"10.1109/CVPRW.2009.5204334","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204334","url":null,"abstract":"Summary form only given. Linguists have proposed dozens of formalisms for grammars and now vision is weighing in with its versions based on its needs. Ulf Grenander has proposed general pattern theory, and has used grammar-like graphical parses of \"thoughts\" in the style of AI. One wants a natural, simple formalism treating all these cases. I want to pose this as a central problem in modeling intelligence. Pattern theory started in the 70's with the ideas of Ulf Grenander and his school at Brown. The aim is to analyze from a statistical point of view the patterns in all \"signals\" generated by the world, whether they be images, sounds, written text, DNA or protein strings, spike trains in neurons, time series of prices or weather, etc. Pattern theory proposes that the types of patterns-and the hidden variables needed to describe these patterns - found in one class of signals will often be found in the others and that their characteristic variability will be similar. The underlying idea is to find classes of stochastic models which can capture all the patterns that we see in nature, so that random samples from these models have the same \"look and feel\" as the samples from the world itself. Then the detection of patterns in noisy and ambiguous samples can be achieved by the use of Bayes' rule, a method that can be described as \"analysis by synthesis\".","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123248761","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204044
P. MomayyezSiahkal, Kaleem Siddiqi
We approach the problem of fiber tractography from the viewpoint that a computational theory should relate to the underlying quantity that is being measured - the diffusion of water molecules. We characterize the Brownian motion of water by a 3D random walk described by a stochastic non-linear differential equation. We show that the maximum-likelihood trajectories are 3D elastica, or curves of least energy. We illustrate the model with Monte-Carlo (sequential) simulations and then develop a more efficient (local, parallelizable) implementation, based on the Fokker-Planck equation. The final algorithm allows us to efficiently compute stochastic completion fields to connect a source region to a sink region, while taking into account the underlying diffusion MRI data. We demonstrate promising tractography results using high angular resolution diffusion data as input.
{"title":"3D stochastic completion fields for fiber tractography","authors":"P. MomayyezSiahkal, Kaleem Siddiqi","doi":"10.1109/CVPRW.2009.5204044","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204044","url":null,"abstract":"We approach the problem of fiber tractography from the viewpoint that a computational theory should relate to the underlying quantity that is being measured - the diffusion of water molecules. We characterize the Brownian motion of water by a 3D random walk described by a stochastic non-linear differential equation. We show that the maximum-likelihood trajectories are 3D elastica, or curves of least energy. We illustrate the model with Monte-Carlo (sequential) simulations and then develop a more efficient (local, parallelizable) implementation, based on the Fokker-Planck equation. The final algorithm allows us to efficiently compute stochastic completion fields to connect a source region to a sink region, while taking into account the underlying diffusion MRI data. We demonstrate promising tractography results using high angular resolution diffusion data as input.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123565647","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204207
H. Seo, P. Milanfar
We present a novel bottom-up saliency detection algorithm. Our method computes so-called local regression kernels (i.e., local features) from the given image, which measure the likeness of a pixel to its surroundings. Visual saliency is then computed using the said “self-resemblance” measure. The framework results in a saliency map where each pixel indicates the statistical likelihood of saliency of a feature matrix given its surrounding feature matrices. As a similarity measure, matrix cosine similarity (a generalization of cosine similarity) is employed. State of the art performance is demonstrated on commonly used human eye fixation data [3] and some psychological patterns.
{"title":"Nonparametric bottom-up saliency detection by self-resemblance","authors":"H. Seo, P. Milanfar","doi":"10.1109/CVPRW.2009.5204207","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204207","url":null,"abstract":"We present a novel bottom-up saliency detection algorithm. Our method computes so-called local regression kernels (i.e., local features) from the given image, which measure the likeness of a pixel to its surroundings. Visual saliency is then computed using the said “self-resemblance” measure. The framework results in a saliency map where each pixel indicates the statistical likelihood of saliency of a feature matrix given its surrounding feature matrices. As a similarity measure, matrix cosine similarity (a generalization of cosine similarity) is employed. State of the art performance is demonstrated on commonly used human eye fixation data [3] and some psychological patterns.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121938672","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204314
S. Taylor, E. Rosten, T. Drummond
In this paper we present a robust feature matching scheme in which features can be matched in 2.3µs. For a typical task involving 150 features per image, this results in a processing time of 500µs for feature extraction and matching. In order to achieve very fast matching we use simple features based on histograms of pixel intensities and an indexing scheme based on their joint distribution. The features are stored with a novel bit mask representation which requires only 44 bytes of memory per feature and allows computation of a dissimilarity score in 20ns. A training phase gives the patch-based features invariance to small viewpoint variations. Larger viewpoint variations are handled by training entirely independent sets of features from different viewpoints. A complete system is presented where a database of around 13,000 features is used to robustly localise a single planar target in just over a millisecond, including all steps from feature detection to model fitting. The resulting system shows comparable robustness to SIFT [8] and Ferns [14] while using a tiny fraction of the processing time, and in the latter case a fraction of the memory as well.
{"title":"Robust feature matching in 2.3µs","authors":"S. Taylor, E. Rosten, T. Drummond","doi":"10.1109/CVPRW.2009.5204314","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204314","url":null,"abstract":"In this paper we present a robust feature matching scheme in which features can be matched in 2.3µs. For a typical task involving 150 features per image, this results in a processing time of 500µs for feature extraction and matching. In order to achieve very fast matching we use simple features based on histograms of pixel intensities and an indexing scheme based on their joint distribution. The features are stored with a novel bit mask representation which requires only 44 bytes of memory per feature and allows computation of a dissimilarity score in 20ns. A training phase gives the patch-based features invariance to small viewpoint variations. Larger viewpoint variations are handled by training entirely independent sets of features from different viewpoints. A complete system is presented where a database of around 13,000 features is used to robustly localise a single planar target in just over a millisecond, including all steps from feature detection to model fitting. The resulting system shows comparable robustness to SIFT [8] and Ferns [14] while using a tiny fraction of the processing time, and in the latter case a fraction of the memory as well.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125116364","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204309
N. Schmid, Francesco Nicolo
In the field of biometrics evaluation of quality of biometric samples has a number of important applications. The main applications include (1) to reject poor quality images during acquisition, (2) to use as enhancement metric, and (3) to apply as a weighting factor in fusion schemes. Since a biometric-based recognition system relies on measures of performance such as matching scores and recognition probability of error, it becomes intuitive that the metrics evaluating biometric sample quality have to be linked to the recognition performance of the system. The goal of this work is to design a method for evaluating and ranking various quality metrics applied to biometric images or signals based on their ability to predict recognition performance of a biometric recognition system. The proposed method involves: (1) Preprocessing algorithm operating on pairs of quality scores and generating relative scores, (2) Adaptive multivariate mapping relating quality scores and measures of recognition performance and (3) Ranking algorithm that selects the best combinations of quality measures. The performance of the method is demonstrated on face and iris biometric data.
{"title":"A method for selecting and ranking quality metrics for optimization of biometric recognition systems","authors":"N. Schmid, Francesco Nicolo","doi":"10.1109/CVPRW.2009.5204309","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204309","url":null,"abstract":"In the field of biometrics evaluation of quality of biometric samples has a number of important applications. The main applications include (1) to reject poor quality images during acquisition, (2) to use as enhancement metric, and (3) to apply as a weighting factor in fusion schemes. Since a biometric-based recognition system relies on measures of performance such as matching scores and recognition probability of error, it becomes intuitive that the metrics evaluating biometric sample quality have to be linked to the recognition performance of the system. The goal of this work is to design a method for evaluating and ranking various quality metrics applied to biometric images or signals based on their ability to predict recognition performance of a biometric recognition system. The proposed method involves: (1) Preprocessing algorithm operating on pairs of quality scores and generating relative scores, (2) Adaptive multivariate mapping relating quality scores and measures of recognition performance and (3) Ranking algorithm that selects the best combinations of quality measures. The performance of the method is demonstrated on face and iris biometric data.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128244961","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204274
Oksana Yakhnenko, Vasant G Honavar
Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of an image into some semantic space, and then recovers the caption by performing another linear transformation from the semantic space into the label space. The model is trained so that model parameters minimize the error of reconstruction directly. This model is related to Canonical Correlation Analysis (CCA) which maps both images and caption into the semantic space to minimize the distance of mapping in the semantic space. Kernel trick is then used for the MLR resulting in Kernel Multiple Linear Regression model. The solution to KMLR is a solution to the generalized eigen-value problem, related to KCCA (Kernel Canonical Correlation Analysis). We then extend Kernel Multiple Linear Regression and Kernel Canonical Correlation analysis models to multiple kernel setting, to allow various representations of images and captions. We present results for image annotation using Multiple Kernel Learning CCA and MLR on Oliva and Torralba (2001) scene recognition that show kernel selection behaviour.
图像注释是一项具有挑战性的任务，它允许将文本关键字与图像关联起来。本文利用核多元线性回归模型解决了图像标注问题。多元线性回归(Multiple Linear Regression, MLR)模型通过对图像进行某种语义空间的线性变换来重建图像标题，然后对图像进行另一次从语义空间到标签空间的线性变换来恢复图像标题。对模型进行训练，使模型参数直接减小重构误差。该模型与典型相关分析(CCA)有关，典型相关分析将图像和标题都映射到语义空间中，以最小化语义空间中的映射距离。然后将核技巧用于MLR，从而得到核多元线性回归模型。KMLR的解是广义特征值问题的解，与核典型相关分析(KCCA)有关。然后，我们将核多元线性回归和核典型相关分析模型扩展到多个核设置，以允许图像和标题的各种表示。我们展示了在Oliva和Torralba(2001)场景识别上使用多核学习CCA和MLR进行图像注释的结果，这些结果显示了核选择行为。
{"title":"Multiple label prediction for image annotation with multiple Kernel correlation models","authors":"Oksana Yakhnenko, Vasant G Honavar","doi":"10.1109/CVPRW.2009.5204274","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204274","url":null,"abstract":"Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of an image into some semantic space, and then recovers the caption by performing another linear transformation from the semantic space into the label space. The model is trained so that model parameters minimize the error of reconstruction directly. This model is related to Canonical Correlation Analysis (CCA) which maps both images and caption into the semantic space to minimize the distance of mapping in the semantic space. Kernel trick is then used for the MLR resulting in Kernel Multiple Linear Regression model. The solution to KMLR is a solution to the generalized eigen-value problem, related to KCCA (Kernel Canonical Correlation Analysis). We then extend Kernel Multiple Linear Regression and Kernel Canonical Correlation analysis models to multiple kernel setting, to allow various representations of images and captions. We present results for image annotation using Multiple Kernel Learning CCA and MLR on Oliva and Torralba (2001) scene recognition that show kernel selection behaviour.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130887149","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}
Pub Date : 2009-06-20DOI: 10.1109/CVPRW.2009.5204297
Juwei Lu, K. Plataniotis
The paper presents a study on color to gray image conversion from a novel point of view: face detection. To the best knowledge of the authors, research in such a specific topic has not been conducted before. Our work reveals that the standard NTSC conversion is not optimal for face detection tasks, although it may be the best for use to display pictures on monochrome televisions. It is further found experimentally with two AdaBoost-based face detection systems that the detect rates may vary up to 10% by simply changing the parameters of the RGB to Gray conversion. On the other hand, the change has little influence on the false positive rates. Compared to the standard NTSC conversion, the detect rate with the best found parameter setting is 2.85% and 3.58% higher for the two evaluated face detection systems. Promisingly, the work suggests a new solution to the color to gray conversion. It could be extremely easy to be incorporated into most existing face detection systems for accuracy improvement without introduction of any extra cost in computational complexity.
{"title":"On conversion from color to gray-scale images for face detection","authors":"Juwei Lu, K. Plataniotis","doi":"10.1109/CVPRW.2009.5204297","DOIUrl":"https://doi.org/10.1109/CVPRW.2009.5204297","url":null,"abstract":"The paper presents a study on color to gray image conversion from a novel point of view: face detection. To the best knowledge of the authors, research in such a specific topic has not been conducted before. Our work reveals that the standard NTSC conversion is not optimal for face detection tasks, although it may be the best for use to display pictures on monochrome televisions. It is further found experimentally with two AdaBoost-based face detection systems that the detect rates may vary up to 10% by simply changing the parameters of the RGB to Gray conversion. On the other hand, the change has little influence on the false positive rates. Compared to the standard NTSC conversion, the detect rate with the best found parameter setting is 2.85% and 3.58% higher for the two evaluated face detection systems. Promisingly, the work suggests a new solution to the color to gray conversion. It could be extremely easy to be incorporated into most existing face detection systems for accuracy improvement without introduction of any extra cost in computational complexity.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508637","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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