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The analysis of anti-nuclear antibodies in HEp-2 cells by indirect immunofluorescence (IIF) is fundamental for the diagnosis of important immune pathologies; in particular, classifying the staining pattern of the cell is critical for the differential diagnosis of several types of diseases. Current tests based on human evaluation are time-consuming and suffer from very high variability, which impacts...
Traditional methods based on bag-of-word representation are easily affected by noise, and they also cannot handle the problem when a test distribution differs from the training distribution. In this paper, we propose a novel method for human action recognition by bagging data dependent representation. Different with traditional methods, the proposed method represents each video by several histograms...
This paper proposes a novel nonlinear manifold learning method for addressing the ill-posed problem of occluded human action analysis. As we know, a person can perform a broad variety of movements. To capture the multiplicity of a human action, this paper creates a low-dimensional manifold for capturing the intra-path and inter-path contexts of an event. Then, an action path matching scheme can be...
We propose a high-accuracy human detection method featuring a Haar-like filter expressing the human shape and using depth information obtained by capturing people from above with a time-of-flight (TOF) camera. This method extracts object regions by performing background subtraction against this depth information, and passes these extracted object regions through a Haar-like filter based on a human...
In the literature of human action recognition, despite promising results have been obtained by the traditional bag-of-words model, the relationship among spatiotemporal points has rarely been considered. Furthermore, serious quantization error also exists in this kind of strategy. In this paper, we propose a novel coding strategy named contextual Fisher kernels to overcome these limitations. We add...
We propose a method for estimating 3-D hand postures from 2-D monocular images in real-time. The estimation is based on finding the best matched posture from typical postures whose appearances are learned in advance. For high accuracy, conventional methods require high computational cost for comparing an input with many typical postures. In our method, a tree is automatically generated and trained...
Recognizing actions having similar movements is a challenging problem. Human action understanding task is divided into two issues in this paper. One is a classical action recognition task where we employ a probabilistic model to learn and recognize human actions. The other is action categorization task where we classify actions based on quantized human movement. An approach called Action Trait Code...
In this paper, we present a new method for assessing the quality of degraded document images using unsupervised feature learning. The goal is to build a computational model to automatically predict OCR accuracy of a degraded document image without a reference image. Current approaches for this problem typically rely on hand-crafted features whose design is based on heuristic rules that may not be...
Pedestrian detection problem has been a touchstone of various image feature descriptors. In this paper, we evaluate four kinds of representative local descriptors (HOG, Haar-like, SURF and LBP) for pedestrian representation. Our goal is to find out the best combination of feature descriptors by analyzing and evaluating the complementarities of them. With the cross validation method, we first find...
The security of web services is nowadays one of the major concerns for Internet users. Web services may manage confidential information, monetary transactions, or even health-critical systems, such as those employed in public airports or hospitals. A key problem of web services is that they should work as expected even in the presence of malicious inputs. Unfortunately, with the increasing complexity...
Sparse representation based classification (SRC) has been widely used for face recognition (FR). Although SRC algorithm is also adopted in human action recognition, the evaluations of different regular terms have not been given. In this paper, we will discuss and evaluate the role of different regular terms of SRC in human action recognition, after that, we propose human action recognition algorithm...
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a novel human action recognition approach which learns weights for features is proposed, where each feature is assigned a weight for human action representation. These weights are learned...
This paper proposes an activity-specific 3D human pose tracking system from multiple camera views. Dimensionality reduction is used to represent a single activity in a hierarchy of low dimensional spaces. This hierarchy provides increasing independence between limbs by decoupling them, allowing higher flexibility and adaptability that result in improved accuracy. For every subspace, a deterministic...
The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified...
This paper proposes a method for estimating the quantitative values of some attributes associated with surface qualities of an object, such as glossiness and transparency, from its image. Our approach is to learn functions that compute such attribute values from the input image by using training data given in the form of relative information. To be specific, each sample of the training data represents...
Recognizing collective human activities has gained attention. Collective activities are such as queueing in a line, talking together and waiting by an intersection. It is often hard to differentiate between these activities only by the appearance of the individual. Hence, recent works exploit the contextual information of other people nearby. However, these works do not take enough care of the spacial...
Expression based face recognition has been gaining more and more attentions recently. Most traditional expression based face recognition can perform recognition where the probe and gallery have same expressions. In this paper, we propose to use different expressions for recognition. Our proposal exploits the temporal order in the video and extracts the identity signature from deformation and motion...
This paper presents counting number of points for the P.acne vulgaris using UV Fluorescence and image processing. This proposed method uses a process of image processing as follows. Cropping a UV image is to select a region of interest and, then the cropped image is resized for a suitable size and it (or the color image) is converted to a gray image. Quality of this gray image will be improved for...
This paper proposes a novel decision system that can distinguish the following five phases of a normal gait cycle in real-time — stance, heel-off, swing 1, swing 2, and heel-strike — by using a smart shoe. This decision system employs four force sensors to evaluate forces exerted by a foot on the shoe insole during ambulation and a gyroscope, attached at the back of the shoe, to measure angular velocity...
In this paper, we propose an effective method for frontal face detection. The proposed method uses a template matching with the genetic algorithm (GA). This method uses a template which shows a distribution of average intensities on a human face. The template and some candidate areas which are searched by GA are compared, and theirs similarities are calculated. The proposed method is robust to geometric...
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