Colored Local Invariant Features for Distinct Object Description in Vision-Based Intelligent Systems

Abdel-Hakim, Alaa E.; Farag, Aly A.

doi:10.4018/9781599042497.ch010

Cited by 2 publications

(5 citation statements)

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“…In recent years methods of combining local features and color features [2][6] [15] have become most popular because they have solved a number of object recognition problems. Patwardhan et al [13] presented a novel scheme to generate a graphical model of the appearance and relationship between objects.…”

Section: Introductionmentioning

confidence: 99%

People re-detection using Adaboost with sift and color correlogram

Hu¹,

Jiang²,

Huang

et al. 2008

2008 15th IEEE International Conference on Image Processing

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People re-detection aims at performing re-identification of people who leave the scene and reappear after some time. This is an important problem especially in video surveillance scenarios. In this paper, we present a method of people re-detection within the context of visual sequence in single-camera setup. We consider re-detection as a binary classification problem, where both global and local descriptors are employed for training strong classifier on-line with Adaboost to distinguish a newly detected people as tracked or new occurrence. The strong classifier will be updated while match is ascertained. A predetermined classifier with well-chosen threshold is employed as assistant of training examples collection. We test the performance of our approach on 4 different scenes including 51 video sequences taken from the CAVIAR database and 4 video sequences shot by ourselves. The results show that our re-detection algorithm can robustly handle variations in illumination, pose, scale, and camera-view.

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Section: Introductionmentioning

confidence: 99%

People re-detection using Adaboost with sift and color correlogram

Hu¹,

Jiang²,

Huang

et al. 2008

2008 15th IEEE International Conference on Image Processing

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show abstract

“…Local invariant feature descriptors are used as a signature for each point. A novel approach for building robust and efficient 3D local invariant feature descriptors is used [98]. The obtained results are compared to those obtained using our own implementation of the B-spline based free deformation approach [8].…”

Section: G Conclusionmentioning

confidence: 99%

“…The following sections introduce a brief overview of the scale space theory and how it is used to build stable feature descriptors. The presentation is made in the general three dimensional case (one can see for example [98,99] for more details).…”

Section: B Local Invariant Features For Voxel-based Similarity Measurementioning

confidence: 99%

“…This method was previously used in [98] for 2D medical applications and was proven to be efficient with respect to rotation and affine transformations in other applications [101,102]. The gradient magnitude of each voxel in the neighborhood of an interest point is calculated as…”

Section: Descriptor Building and Matchingmentioning

confidence: 99%

“…According to [98], the feature descriptor in 3D space is built using gradient orientations histograms with 2D polar-coordinate bins for neighboring cells which consist of voxels in the current level-neighborhood of every interest point. This method was previously used in [98] for 2D medical applications and was proven to be efficient with respect to rotation and affine transformations in other applications [101,102].…”

Section: Descriptor Building and Matchingmentioning

confidence: 99%

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Variational methods for shape and image registrations.

Fahmi

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Fahmi, Rachid 1971-, "Variational methods for shape and image registrations." (2008 Thank you for your endless love, support, and sacrifices.I love you more than anything else in this world.iii ACKNOWLEDGMENTS First of all, my deepest thanks are due to Almighty God, the merciful, the compassionate for all the blessings bestowed upon me. Estimating and analysis of deformation, either rigid or non-rigid, is an active area of research in various medical imaging and computer vision applications. Its importance stems from the inherent inter-and intra-variability in biological and biomedical object shapes and from the dynamic nature of the scenes usually dealt with in computer vision research. For instance, quantifying the growth of a tumor, recognizing a person's face, tracking a facial expression, or retrieving an object inside a data base require the estimation of some sort of motion or deformation undergone by the object of interest. To solve these problems, and other similar problems, registration comes into play. This is the process of bringing into correspondences two or more data sets. Depending on the application at hand, these data sets can be for instance gray scale/color images or objects' outlines. In the latter case, one talks about shape registration while in the former case, one talks about image/volume registration. In some situations, the combinations of different types of data can be used complementarily to establish point correspondences.One of most important image analysis tools that greatly benefits from the process of registration, and which will be addressed in this dissertation, is the image segmentation.This process consists of localizing objects in images. Several challenges are encountered in image segmentation, including noise, gray scale inhomogeneities, and occlusions. To cope with such issues, the shape information is often incorporated as a statistical model into the segmentation process. Building such statistical models requires a good and accurate v shape alignment approach. In addition, segmenting anatomical structures can be accurately solved through the registration of the input data set with a predefined anatomical atlas. Furthermore, it can deal with noisy, occluded and missing or corrupted data. The classical way of solving the shape-based segmentation problems within a level set framework is by directly solving the underlying Euler-Lagrange equations using a gradient descent scheme. This is very computationally expensive given the non-linear parabolic nature of the corresponding PDE's. To overcome these difficulties, a fast algorithm is designed and implemented to solve both the two-phase and the multi-phase shape-based segmentation problem. This algorithm exploits the fact that only the sign of the level set function, not its value, is needed to evolve the segmenting interface. The integration of multiple selective shape priors and the segmentation into multiple regions has never been addressed before.Third, a new image/volume non-rigid registration approach based on scale s...

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Colored Local Invariant Features for Distinct Object Description in Vision-Based Intelligent Systems

Cited by 2 publications

References 0 publications

People re-detection using Adaboost with sift and color correlogram

People re-detection using Adaboost with sift and color correlogram

Variational methods for shape and image registrations.

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