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In this paper, we address the problem of gender classification based on facial images. The Speeded Up Robust Feature (SURF) algorithm descriptors are used as features to built dictionaries and a multi-task Sparse Representation Classification (SRC) is used as classifier to determine the gender of an individual face. Our approach uses smaller and compact dictionaries by removing the redundant atoms...
Densely sampled dynamic geophysical data are often modeled using principal components analysis (PCA, a.k.a. empirical orthogonal function or EOF analysis) to provide constraints for their inversion with remote sensing techniques. We show that overcomplete sparsifying dictionaries, generated using dictionary learning, provide a more informative basis for geophysical signal representation. Relative...
The liver shapes are complex, pathological changes severely affect the liver shapes. In order to realize the segmentation of the boundary of liver in CT images, the liver shapes dictionary is built, the input CT images are sparse represented by the angular points in gold standard liver boundary dictionary, and the best matches is selected to be the final segmentation result. Experimental results show...
Facial point detection in real-world conditions presents large variations in shapes and occlusions due to differences in poses, expressions, use of accessories, which may lead to a large difficultly in locating facial points. In this paper, we propose a regression-based sparse coding method for facial point detection. The method combines the regression-based concept with sparse reconstruction methods...
This paper introduces a segmentation approach, where a discriminative dictionary with objects' shape information is learned, followed by a sparse representation based segmentation process. In contrast with state-of-the-art sparse representation classification methods using discriminative dictionary learning, the proposed method learns a discriminative dictionary containing both intensity and shape...
This paper presents a novel pose-indexed based multi-view (PIMV) face alignment framework. Most of the current cascaded regression face alignment methods generally start with a mean shape. However, when the initial shape is far from the ground truth, the performance significantly deteriorates. Our approach aims to obtain a preferable initial shape from a pose-indexed shape searching space. This space...
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In this paper, a novel method of embedding shape information into level set image segmentation is proposed. Our method is based on inferring shape variations by a sparse linear combination of instances in the shape repository. Given a sufficient number of training shapes with variations, a new shape can be approximated by a linear span of training shapes associated with those variations. At each step...
In open-ended domains, autonomous robots must have the ability to continuously process visual information, and execute learning and recognition in a concurrent and interleaved fashion. Because the set of categories to be learned is not predefined, it is not feasible to assume that one can pre-program all object categories required by service robots. Topic modelling approaches usually construct the...
We present a novel method to recover images of faces, particularly when large spatial regions of the face are unavailable due to data losses or occlusions. In contrast with previous work, we do not make assumptions on the data neither during training nor testing (such as assuming that the person was seen before or that all faces are perfectly aligned and have identical head pose, expression, etc.)...
Accurate cell segmentation is an important and long-standing challenge in biomedical image analysis due to large variations in shape and boundary ambiguity. In this paper, we present a segmentation framework for partially overlapping cervical cells. The proposed method starts by cellular clump estimation with morphological reconstruction. Subsequently, the nuclei inside the cellular clumps are located...
Object detection from images is generally achieved through a supervised learning manner. However, in many real applications, to provide instance level label is still costly. Thus, weakly supervised approach is proposed and naturally cast as a Multiple Instance Learning (MIL) problem. Traditional MIL methods typically learn discriminative classifiers from positive and negative training bags. Alternatively,...
Fire detection is one of the most interesting issues for surveillance. The existing approaches for the fire detection suffer from a high false positive ratio. To solve the problems, we present a patch-based fire detection algorithm with online outlier learning. In the proposed algorithm, the candidates of fire are obtained in the form of patch, while the classical candidates have been based on pixels...
Sparse representation-based classification (SRC) has been recently attracted a great interest among the signal processing society. SRC applies a discriminative representation using training samples to separate signals into their classes. In existing SRC methods, the dictionary size, which highly affects the performance, is manually set. Moreover, they are linear classifiers, and thus, they are not...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification...
Since a human face can be represented by a few feature points (FPs) with less redundant information, and calculated by a linear combination of a small number of prototypical faces, we propose a two-step 3D face reconstruction approach including FP depth estimation and shape deformation. The proposed approach can reconstruct a realistic 3D face from a 2D frontal face image. In the first step, a coupled...
We proposed a framework for human action recognition by learning pose dictionary as the human appearance representation. At first, the shape based pose feature is constructed based on the contour points of the human silhouette and invariant to translation and scaling. After the local pose features are extracted from the original videos, the class-specific dictionaries are learned individually on the...
Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, a novel leaf image recognition technique via sparse representation is proposed for automatic plant identification. In order to model leaf images, we learn an overcomplete dictionary for sparsely representing...
We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition...
We propose an approach which allows to localize anatomical landmarks in radiological datasets given only a single manual annotation and set of un-annotated example images. Using top-down image patch regression to obtain potential landmark candidates in the set of training images, a model of the anatomical structure is incrementally enlarged, starting from the single, annotated image, until it encompasses...
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