Over the past years, digital imagery applications had a strong growth. In scientific fields, this... more Over the past years, digital imagery applications had a strong growth. In scientific fields, this technology is fast becoming mainstream in many applications and is implanted solidly in remote sensing domain. This fact produces the necessity of software to process digital images. Tn Estudio software joins the tools developed in Cuba since last ten years in digital image processing applied to mapping and remote sensing. The software was developed taking into consideration the working experience with national and foreign software, using object oriented programming with C++ language for Windows 98/NT platform. There are available both, general and transform operations to apply on images. Among the last kind of operations, the user will find pixelwise operations, binary operations (arithmetical, logical, comparative, vegetation index), multi band operations (principal component analysis, statistical, comparative, false color, color decomposition), enhancement, linear and non linear digital filtering with internal and external filters, filter design in space and frequency domain (low pass filter, high pass filter, band pass filter, second and fourth vertical derivative filter), edge and line detectors, local segmentation, global segmentation (histogram based segmentation), region based segmentation (region growing, split, merge and split-merge). In our digital image processing system, a set of supervised and unsupervised classification techniques is added. of Some examples of applications in different scientific fields such as remote sensing, geoscience, agriculture, forestry, environmental studies are presented. This system has been used as an useful tool to teach digital image processing in postgraduate courses at Universities and research institutes in our country.
Computation of homology generators using a graph pyramid can significantly increase performance, ... more Computation of homology generators using a graph pyramid can significantly increase performance, compared to the classical methods. First results in 2D exist and show the advantages of the method. Generators are computed on the upper level of a graph pyramid. Toplevel graphs may contain self loops and multiple edges, as a side product of the contraction process. Using straight lines to draw these edges would not show the full information: self loops disappear, parallel edges collapse. This paper presents a novel algorithm for correctly visualizing graph pyramids, including multiple edges and self loops which preserves the geometry and the topology of the original image. New insights about the top-down delineation of homology generators in graph pyramids are given.
Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single... more Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single image haze removal is an ill-posed problem, because the number of the equations is smaller than the number of unknowns. In this paper, a deep learning-based method, called Dehaze CNN, is proposed to estimate a clear image patch from a hazy image patch, which can be used to reconstruct a haze-free image. Our method recovers a clear image by a learning model containing no hazy information. Our method also adopts Deep Convolution Neural Networks which takes the patch atom that can be used to generate hazy image patches and haze-free ones as the input and outputs the corresponding haze-free patch. Then we reconstruct a haze-free image from those patches. Finally, we remove the color distortion in the haze-free image via contextual regularization effectively. Experimental results show that the proposed method outperforms the state-of-the-art haze removal methods.
Gait is a kind of attractive feature for human identification at a distance. It can be regarded a... more Gait is a kind of attractive feature for human identification at a distance. It can be regarded as a kind of temporal signal. At the same time the human body shape can be regarded as the signal in the spatial domain. In the proposed method, we try to extract discriminative feature from video sequences in the spatial and temporal domains by only one network, Spatial-Temporal Graph Attention Network (STGAN). In spatial domain, we designed one branch to select some distinguished regions and enhance their contribution. It can make the network focus on these distinguished regions. We also constructed another branch, a Spatial-Temporal Graph (STG), to discover the relationship between frames and the variation of a region in temporal domain. The proposed method can extract gait feature in the two domains, and the two branches in the model can be trained end to end. The experimental results on two popular datasets, CASIA-B and OU-ISIR Treadmill-B, show the proposed method can improve gait recognition obviously.
One of the most attractive biometric techniques is gait recognition, since its potential for huma... more One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. The body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions.
In this paper, we introduce a method based on Windowed Dynamic Mode Decomposition to enhance the ... more In this paper, we introduce a method based on Windowed Dynamic Mode Decomposition to enhance the texture of body parts on the Gait Energy Image that are not affected by the clothing and carrying condition variations, in order to improve the gait recognition accuracy under these kinds of variations. We obtain the best accurracy (\(71.37 \%\)) for large carrying condition variations reported in the literature for CASIA-B dataset. Unlike the deep learning based approaches the proposal method is simple and does not need training.
The performance of gait recognition can be adversely affected by many sources of variation such a... more The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. To extract invariant gait features, we proposed a method called GaitGANv2 which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate a canonical side view of a walking gait in normal clothing without carrying any bag. A unique advantage of this approach is that, unlike other methods, GaitGANv2 does not need to determine the view angle before generating invariant gait images. Indeed, only one model is needed to account for all possible sources of variation such as with or without carrying accessories and varying degrees of view angle. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN in that GaitGANv2 contains two discriminators instead of one. They are respectively called fake/real discriminator and identification discriminator. While the first discriminator ensures that the generated gait images are realistic, the second one maintains the human identity information. The proposed GaitGANv2 represents an improvement over GaitGANv1 in that the former adopts a multi-loss strategy to optimize the network to increase the inter-class distance and to reduce the intra-class distance, at the same time. Experimental results show that GaitGANv2 can achieve state-of-the-art performance.
2021 IEEE International Joint Conference on Biometrics (IJCB), 2021
Gait recognition is an effective way to identify a person due to its non-contact and long-distanc... more Gait recognition is an effective way to identify a person due to its non-contact and long-distance acquisition. In addition, the length of human limbs and the motion pattern of human from human skeletons have been proved to be effective features for gait recognition. However, the length of human limbs and motion pattern are calculated through human prior knowledge, more important or detailed information may be missing. Our method proposes to obtain the dynamic information and static information from human skeletons through disentanglement learning. In the experiments, it has been shown that the features extracted by our method are effective.
2021 IEEE International Joint Conference on Biometrics (IJCB), 2021
The Competition on Human Identification at a Distance 2021 (HID 2021) is to promote the research ... more The Competition on Human Identification at a Distance 2021 (HID 2021) is to promote the research in human identification at a distance and to provide a benchmark to evaluate different methods. HID 2021 is the second follow-up from the first one, HID 2020. The dataset size and the evaluation protocal are the same with the previous competition, but the data in the test set has been changed. The paper firstly introduces the dataset and the evaluation protocol, then describes the methods from the top teams and their results. The methods show how to achieve state-of-the-art performance on gait recognition. The results in HID 2021 are better than those in HID 2020. From the comparisons and analysis, some useful conclusions can be drawn. We hope more improvements can be achieved by better followup competitions.
There are surveillance scenarios where it is important to emit an alarm when a person carrying an... more There are surveillance scenarios where it is important to emit an alarm when a person carrying an object is detected. In order to detect when a person is carrying an object, we build models of naturally-walking and object-carrying persons using topological features. First, a stack of human silhouettes, extracted by background subtraction and thresholding, are glued through their gravity centers, forming a 3D digital image I. Second, different filters (i.e. orderings of the cells) are applied on ∂K(I) (cubical complex obtained from I) which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagrams according to each filter. We build some clusters of persons walking naturally, without carrying object and some clusters of persons carrying bags. We obtain vector prototypes for each cluster. Simple distances to the means are calculated for detecting the presence of carrying object. The measure cosine is used to give a similarity value between topological signatures. The accuracies obtained are 95.7% and 95.9% for naturally-walking and object-carrying respectively.
2013 International Conference on Biometrics (ICB), 2013
ABSTRACT In the last years spatio-temporal representations have shown to be successful for the an... more ABSTRACT In the last years spatio-temporal representations have shown to be successful for the analysis of video sequences in applications such as event detection, action and face recognition in videos. In this paper, we propose the use of local spatio-temporal features for the faces/non-faces classification stage, in the process of face detection in videos. Specifically, the extension of the Local Binary Patterns operator to the spatio-temporal domain is evaluated and compared with other schemes based on the same operator without considering the temporal information. The obtained results in the very challenged YouTube Faces database show that combining local appearance with motion can help to discriminate between faces and non-faces in the context of video applications.
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017
The performance of gait recognition can be adversely affected by many sources of variation such a... more The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. In order to extract invariant gait features, we proposed a method named as GaitGAN which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate invariant gait images that is side view images with normal clothing and without carrying bags. A unique advantage of this approach is that the view angle and other variations are not needed before generating invariant gait images. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN which has only one discriminator in that GaitGAN contains two discriminators. One is a fake/real discriminator which can make the generated gait images to be realistic. Another one is an identification discriminator which ensures that the the generated gait images contain human identification information. Experimental results show that GaitGAN can achieve state-of-the-art performance. To the best of our knowledge this is the first gait recognition method based on GAN with encouraging results. Nevertheless, we have identified several research directions to further improve GaitGAN.
In this paper, a topological approach for monitoring human activities is presented. This approach... more In this paper, a topological approach for monitoring human activities is presented. This approach makes possible to protect the person's privacy hiding details that are not essential for processing a security alarm. First, a stack of human silhouettes, extracted by background subtraction and thresholding, are glued through their gravity centers, forming a 3D digital binary image I. Secondly, different orders of the simplices are applied on a simplicial complex obtained from I, which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagrams according to each order. The measure cosine is used to give a similarity value between topological signatures. In this way, the powerful topological tool known as persistent homology is novelty adapted to deal with gender classification, person identification, carrying bag detection and simple action recognition. Four experiments show the strength of the topological feature used; three of they use the CASIA-B database, and the fourth use the KTH database to present the results in the case of simple actions recognition. In the first experiment the named topological signature is evaluated, obtaining 98.8 % (lateral view) of correct classification rates for gender identification. In the second one are shown results for person identification, obtaining an average of 98.5 %. In the third one the result obtained is 93.8 % for carrying bag detection. And in the last experiment the results were 97.7 % walking and 97.5 % running, which were the actions took from the KTH database.
Desde hace algunos años el desarrollo tecnológico y su abaratamiento, ha ido influenciando notabl... more Desde hace algunos años el desarrollo tecnológico y su abaratamiento, ha ido influenciando notablemente en el aumento del volumen de información a almacenar, si esta información presenta un componente espacial, su procesamiento y análisis se hace aun más complejo, teniéndose que emplear nuevos métodos para obtener la información y el conocimiento que necesitamos del fenómeno que se esta analizando. Todo esto es posible si se emplean técnicas de minería de datos, ya que los métodos analíticos que se emplean para analizar los datos y que tiene su origen en la estadística no son eficientes en datos no estadísticos y no se integran bien con los sistemas de información. Las técnicas de minería de datos pueden ser Predictivas y Descriptivas, abordándose esta última través de las reglas de asociación espacial, la cual describe las relaciones entre los objetos geográficos. En este trabajo se expone el desarrollo de una extensión para gvSIG, que implementa la técnica de minería de datos “Reg...
Abstract. In this paper we resolve the problem of automatically normalize front view photos from ... more Abstract. In this paper we resolve the problem of automatically normalize front view photos from a database that contain images of human faces with different size, angle and position. It was used a template with a standardized inter eye distance and dimensions. We are mapping ...
Graph-based data representations are an important research topic due to the suitability of this k... more Graph-based data representations are an important research topic due to the suitability of this kind of data structure to model entities and the complex relations among them. In computer vision, graphs have been used to model images in order to add some high level information (relations) to the low-level representation of individual parts. How to deal with these representations for specific tasks is not easy due to the complexity of the data structure itself. In this paper we propose to use a graph mining technique for image classification, introducing approximate patterns discovery in the mining process in order to allow certain distortions in the data being modeled. We are proposing to combine a powerful graph-based image representation adapted to this specific task and frequent approximate subgraph (FAS) mining algorithms in order to classify images. In the case of image representation we are proposing to use more robust descriptors than our previous approach in this topic, and we also suggest a criterion to select the isomorphism threshold for the graph mining step. This proposal is tested in two well-known collections to show the improvement with respect to the previous related works.
Autores: Edel B. García Reyes (1), Eduardo Garea Llanos (1), José Luis Gil Rodríguez (1), Gustavo... more Autores: Edel B. García Reyes (1), Eduardo Garea Llanos (1), José Luis Gil Rodríguez (1), Gustavo Martín Morales (1), Luis B. Rivero Ramos (2), Dámaso R. Ponvert Delisles Batista (3) ... 1 GEOCUBA - Investigación y Consultaría, Calle 4 No. 304, Playa, C. Habana, Cuba. Telf: (537) 202 ...
Over the past years, digital imagery applications had a strong growth. In scientific fields, this... more Over the past years, digital imagery applications had a strong growth. In scientific fields, this technology is fast becoming mainstream in many applications and is implanted solidly in remote sensing domain. This fact produces the necessity of software to process digital images. Tn Estudio software joins the tools developed in Cuba since last ten years in digital image processing applied to mapping and remote sensing. The software was developed taking into consideration the working experience with national and foreign software, using object oriented programming with C++ language for Windows 98/NT platform. There are available both, general and transform operations to apply on images. Among the last kind of operations, the user will find pixelwise operations, binary operations (arithmetical, logical, comparative, vegetation index), multi band operations (principal component analysis, statistical, comparative, false color, color decomposition), enhancement, linear and non linear digital filtering with internal and external filters, filter design in space and frequency domain (low pass filter, high pass filter, band pass filter, second and fourth vertical derivative filter), edge and line detectors, local segmentation, global segmentation (histogram based segmentation), region based segmentation (region growing, split, merge and split-merge). In our digital image processing system, a set of supervised and unsupervised classification techniques is added. of Some examples of applications in different scientific fields such as remote sensing, geoscience, agriculture, forestry, environmental studies are presented. This system has been used as an useful tool to teach digital image processing in postgraduate courses at Universities and research institutes in our country.
Computation of homology generators using a graph pyramid can significantly increase performance, ... more Computation of homology generators using a graph pyramid can significantly increase performance, compared to the classical methods. First results in 2D exist and show the advantages of the method. Generators are computed on the upper level of a graph pyramid. Toplevel graphs may contain self loops and multiple edges, as a side product of the contraction process. Using straight lines to draw these edges would not show the full information: self loops disappear, parallel edges collapse. This paper presents a novel algorithm for correctly visualizing graph pyramids, including multiple edges and self loops which preserves the geometry and the topology of the original image. New insights about the top-down delineation of homology generators in graph pyramids are given.
Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single... more Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single image haze removal is an ill-posed problem, because the number of the equations is smaller than the number of unknowns. In this paper, a deep learning-based method, called Dehaze CNN, is proposed to estimate a clear image patch from a hazy image patch, which can be used to reconstruct a haze-free image. Our method recovers a clear image by a learning model containing no hazy information. Our method also adopts Deep Convolution Neural Networks which takes the patch atom that can be used to generate hazy image patches and haze-free ones as the input and outputs the corresponding haze-free patch. Then we reconstruct a haze-free image from those patches. Finally, we remove the color distortion in the haze-free image via contextual regularization effectively. Experimental results show that the proposed method outperforms the state-of-the-art haze removal methods.
Gait is a kind of attractive feature for human identification at a distance. It can be regarded a... more Gait is a kind of attractive feature for human identification at a distance. It can be regarded as a kind of temporal signal. At the same time the human body shape can be regarded as the signal in the spatial domain. In the proposed method, we try to extract discriminative feature from video sequences in the spatial and temporal domains by only one network, Spatial-Temporal Graph Attention Network (STGAN). In spatial domain, we designed one branch to select some distinguished regions and enhance their contribution. It can make the network focus on these distinguished regions. We also constructed another branch, a Spatial-Temporal Graph (STG), to discover the relationship between frames and the variation of a region in temporal domain. The proposed method can extract gait feature in the two domains, and the two branches in the model can be trained end to end. The experimental results on two popular datasets, CASIA-B and OU-ISIR Treadmill-B, show the proposed method can improve gait recognition obviously.
One of the most attractive biometric techniques is gait recognition, since its potential for huma... more One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. The body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions.
In this paper, we introduce a method based on Windowed Dynamic Mode Decomposition to enhance the ... more In this paper, we introduce a method based on Windowed Dynamic Mode Decomposition to enhance the texture of body parts on the Gait Energy Image that are not affected by the clothing and carrying condition variations, in order to improve the gait recognition accuracy under these kinds of variations. We obtain the best accurracy (\(71.37 \%\)) for large carrying condition variations reported in the literature for CASIA-B dataset. Unlike the deep learning based approaches the proposal method is simple and does not need training.
The performance of gait recognition can be adversely affected by many sources of variation such a... more The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. To extract invariant gait features, we proposed a method called GaitGANv2 which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate a canonical side view of a walking gait in normal clothing without carrying any bag. A unique advantage of this approach is that, unlike other methods, GaitGANv2 does not need to determine the view angle before generating invariant gait images. Indeed, only one model is needed to account for all possible sources of variation such as with or without carrying accessories and varying degrees of view angle. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN in that GaitGANv2 contains two discriminators instead of one. They are respectively called fake/real discriminator and identification discriminator. While the first discriminator ensures that the generated gait images are realistic, the second one maintains the human identity information. The proposed GaitGANv2 represents an improvement over GaitGANv1 in that the former adopts a multi-loss strategy to optimize the network to increase the inter-class distance and to reduce the intra-class distance, at the same time. Experimental results show that GaitGANv2 can achieve state-of-the-art performance.
2021 IEEE International Joint Conference on Biometrics (IJCB), 2021
Gait recognition is an effective way to identify a person due to its non-contact and long-distanc... more Gait recognition is an effective way to identify a person due to its non-contact and long-distance acquisition. In addition, the length of human limbs and the motion pattern of human from human skeletons have been proved to be effective features for gait recognition. However, the length of human limbs and motion pattern are calculated through human prior knowledge, more important or detailed information may be missing. Our method proposes to obtain the dynamic information and static information from human skeletons through disentanglement learning. In the experiments, it has been shown that the features extracted by our method are effective.
2021 IEEE International Joint Conference on Biometrics (IJCB), 2021
The Competition on Human Identification at a Distance 2021 (HID 2021) is to promote the research ... more The Competition on Human Identification at a Distance 2021 (HID 2021) is to promote the research in human identification at a distance and to provide a benchmark to evaluate different methods. HID 2021 is the second follow-up from the first one, HID 2020. The dataset size and the evaluation protocal are the same with the previous competition, but the data in the test set has been changed. The paper firstly introduces the dataset and the evaluation protocol, then describes the methods from the top teams and their results. The methods show how to achieve state-of-the-art performance on gait recognition. The results in HID 2021 are better than those in HID 2020. From the comparisons and analysis, some useful conclusions can be drawn. We hope more improvements can be achieved by better followup competitions.
There are surveillance scenarios where it is important to emit an alarm when a person carrying an... more There are surveillance scenarios where it is important to emit an alarm when a person carrying an object is detected. In order to detect when a person is carrying an object, we build models of naturally-walking and object-carrying persons using topological features. First, a stack of human silhouettes, extracted by background subtraction and thresholding, are glued through their gravity centers, forming a 3D digital image I. Second, different filters (i.e. orderings of the cells) are applied on ∂K(I) (cubical complex obtained from I) which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagrams according to each filter. We build some clusters of persons walking naturally, without carrying object and some clusters of persons carrying bags. We obtain vector prototypes for each cluster. Simple distances to the means are calculated for detecting the presence of carrying object. The measure cosine is used to give a similarity value between topological signatures. The accuracies obtained are 95.7% and 95.9% for naturally-walking and object-carrying respectively.
2013 International Conference on Biometrics (ICB), 2013
ABSTRACT In the last years spatio-temporal representations have shown to be successful for the an... more ABSTRACT In the last years spatio-temporal representations have shown to be successful for the analysis of video sequences in applications such as event detection, action and face recognition in videos. In this paper, we propose the use of local spatio-temporal features for the faces/non-faces classification stage, in the process of face detection in videos. Specifically, the extension of the Local Binary Patterns operator to the spatio-temporal domain is evaluated and compared with other schemes based on the same operator without considering the temporal information. The obtained results in the very challenged YouTube Faces database show that combining local appearance with motion can help to discriminate between faces and non-faces in the context of video applications.
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017
The performance of gait recognition can be adversely affected by many sources of variation such a... more The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. In order to extract invariant gait features, we proposed a method named as GaitGAN which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate invariant gait images that is side view images with normal clothing and without carrying bags. A unique advantage of this approach is that the view angle and other variations are not needed before generating invariant gait images. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN which has only one discriminator in that GaitGAN contains two discriminators. One is a fake/real discriminator which can make the generated gait images to be realistic. Another one is an identification discriminator which ensures that the the generated gait images contain human identification information. Experimental results show that GaitGAN can achieve state-of-the-art performance. To the best of our knowledge this is the first gait recognition method based on GAN with encouraging results. Nevertheless, we have identified several research directions to further improve GaitGAN.
In this paper, a topological approach for monitoring human activities is presented. This approach... more In this paper, a topological approach for monitoring human activities is presented. This approach makes possible to protect the person's privacy hiding details that are not essential for processing a security alarm. First, a stack of human silhouettes, extracted by background subtraction and thresholding, are glued through their gravity centers, forming a 3D digital binary image I. Secondly, different orders of the simplices are applied on a simplicial complex obtained from I, which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagrams according to each order. The measure cosine is used to give a similarity value between topological signatures. In this way, the powerful topological tool known as persistent homology is novelty adapted to deal with gender classification, person identification, carrying bag detection and simple action recognition. Four experiments show the strength of the topological feature used; three of they use the CASIA-B database, and the fourth use the KTH database to present the results in the case of simple actions recognition. In the first experiment the named topological signature is evaluated, obtaining 98.8 % (lateral view) of correct classification rates for gender identification. In the second one are shown results for person identification, obtaining an average of 98.5 %. In the third one the result obtained is 93.8 % for carrying bag detection. And in the last experiment the results were 97.7 % walking and 97.5 % running, which were the actions took from the KTH database.
Desde hace algunos años el desarrollo tecnológico y su abaratamiento, ha ido influenciando notabl... more Desde hace algunos años el desarrollo tecnológico y su abaratamiento, ha ido influenciando notablemente en el aumento del volumen de información a almacenar, si esta información presenta un componente espacial, su procesamiento y análisis se hace aun más complejo, teniéndose que emplear nuevos métodos para obtener la información y el conocimiento que necesitamos del fenómeno que se esta analizando. Todo esto es posible si se emplean técnicas de minería de datos, ya que los métodos analíticos que se emplean para analizar los datos y que tiene su origen en la estadística no son eficientes en datos no estadísticos y no se integran bien con los sistemas de información. Las técnicas de minería de datos pueden ser Predictivas y Descriptivas, abordándose esta última través de las reglas de asociación espacial, la cual describe las relaciones entre los objetos geográficos. En este trabajo se expone el desarrollo de una extensión para gvSIG, que implementa la técnica de minería de datos “Reg...
Abstract. In this paper we resolve the problem of automatically normalize front view photos from ... more Abstract. In this paper we resolve the problem of automatically normalize front view photos from a database that contain images of human faces with different size, angle and position. It was used a template with a standardized inter eye distance and dimensions. We are mapping ...
Graph-based data representations are an important research topic due to the suitability of this k... more Graph-based data representations are an important research topic due to the suitability of this kind of data structure to model entities and the complex relations among them. In computer vision, graphs have been used to model images in order to add some high level information (relations) to the low-level representation of individual parts. How to deal with these representations for specific tasks is not easy due to the complexity of the data structure itself. In this paper we propose to use a graph mining technique for image classification, introducing approximate patterns discovery in the mining process in order to allow certain distortions in the data being modeled. We are proposing to combine a powerful graph-based image representation adapted to this specific task and frequent approximate subgraph (FAS) mining algorithms in order to classify images. In the case of image representation we are proposing to use more robust descriptors than our previous approach in this topic, and we also suggest a criterion to select the isomorphism threshold for the graph mining step. This proposal is tested in two well-known collections to show the improvement with respect to the previous related works.
Autores: Edel B. García Reyes (1), Eduardo Garea Llanos (1), José Luis Gil Rodríguez (1), Gustavo... more Autores: Edel B. García Reyes (1), Eduardo Garea Llanos (1), José Luis Gil Rodríguez (1), Gustavo Martín Morales (1), Luis B. Rivero Ramos (2), Dámaso R. Ponvert Delisles Batista (3) ... 1 GEOCUBA - Investigación y Consultaría, Calle 4 No. 304, Playa, C. Habana, Cuba. Telf: (537) 202 ...
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