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This paper proposes a detection method for electronic parts from electronic board images using HSV color format. In this study, we can pick out the electronic parts images by dividing the fixed section. If the parts have a low level of saturation, we detect only two colors that as black and white. To use this method, the detection rate of other colors is improved. It is possible to detect the region...
This paper presents an effective method for human identification using temporal and wavelet domain features extracted from electrocardiogram (ECG) signal. Instead of directly using the ECG data of a person as the feature, first, it is shown that a few number of reflection coefficients extracted from the autocorrelation function of the data can efficiently perform the recognition task. Next, the discrete...
In order to have a rich representation for human action, we propose to combine two complementary features so that a human posture can be characterized in more details. In particular, the distance signal feature and the width feature are combined in an effective way to enhance each other's discriminating capability. The resulting feature vector is quantized into mid-level features using k-means clustering...
This paper introduces the design of a real time vision-based motion synthesis system. the system requires user to wear the markers in a certain color. Based on that, several novel algorithms were used for feature detection and feature tracking under occlusion by estimating the velocity of missing features based on the prior, smoothness and fitness term. These algorithms ensured the accuracy and low...
Detecting objects in underwater image sequences and video frames automatically, requires the application of selected algorithms in consecutive steps. Most of these algorithms are controlled by a set of parameters, which need to be calibrated for an optimal detection result. Those parameters determine the effectivity and efficiency of an algorithm and their impact is usually well known. There are however...
This paper proposes a system to recognize quasi-periodic human actions from monocular video sequences. First, each input video frame is analyzed and estimated to generate the best 3D human model pose which consists of a set of 3D coordinates of specific human joints. Next, these 3D coordinates for each frame are converted into corresponding 3D geometric relational features (GRFs), which describe the...
The human motion analysis is an attractive topic in biometric research. Common biometrics is usually time-consuming, limited and collaborative. These drawbacks pose major challenges to recognition process. Recent researches indicate people have considerable ability to recognize others by their natural walking. Therefore, gait recognition has obtained great tendency in biometric systems. Gait analysis...
This paper presents a multi-feature approach for detection of key postures by using a MESA SR4000 time-offlight 3D sensor managed by a low-power embedded PC. Acquired data were pre-processed by using a well-established framework including self-calibration, segmentation and tracking functionalities. To accommodate different application scenarios, hierarchical coarse-to-fine features were extracted...
As human action is uncertain and illegible, a human action recognition method basing on fuzzy support vector machine is presented. Fuzzy support vector machine employs the membership function to solve the unclassifiable areas which happens the traditional SVMs' two-class problems extend to the multi-class problems. the method is evaluated on the Weizmann action dataset and received comparative high...
During the past years, face and gait recognition in video have received significant attention. Consequently, their recognition problems have challenged due to largely varying appearances and highly complex pattern distributions. However, the complementary properties of these two biometrics suggest fusion of them. Face recognition is more reliable when the person is close to the camera. On the other...
This paper proposes a markerless video analytic system for quantifying body parts movement while lying. These movements include: hand, leg, both hand & leg and turning to left or right movements. Combination of pixel intensity and area difference of both segmented and the whole parts of each silhouette compared with the following silhouettes would provide a useful cue for detection of different...
Human action recognition is a challenging filed in computer vision. In this paper, a novel probabilistic graphical model, called topic-relative conditional random field(TCRF), is firstly proposed. The model is constructed by adding a topic node and using a triangular-chain structure in the top layer of the linear-chain conditional random field(LCRF) to overcome the drawback of independent and identical...
This paper presents a unified action recognition framework combining harris3D descriptor with 3D SIFT detector. We perform action recognition experiments on the KTH dataset using Support Vector Machines. Experiments apply the leave-one-out and compare our proposed approach with state-of-the-art methods. The result shows that our proposed approach is effective. Compared with other approaches our approach...
The human experience in the analysis of the handwriting of male and female writers indicates that gender affects the appearance of the handwritten text. These differences are usually very difficult to describe numerically. In order to analyze the handwriting differences between male and female writers, several shape description techniques, such as the tangent angle function, curvature function and...
This paper presents a Nonlinear AutoRegressive with eXogenous input (NARX)-based approach for human-emotion recognition from an input video. The dynamics of facial expressions are first captured by performing a temporal-spatial analysis by extracting local and spatial features using a pyramid of histograms of oriented gradients (PHOG) descriptor. Then the temporal phases of facial expressions are...
There is good reason to believe that humans use some kind of recursive grammatical structure when we recognize and perform complex manipulation activities. We have built a system to automatically build a tree structure from observations of an actor performing such activities. The activity trees that result form a framework for search and understanding, tying action to language. We explore and evaluate...
In this paper, we propose an approach to detect scene geometrical structure given only one monocular image. Several typical scene geometries are investigated and corresponding models are built. A scene geometry reasoning system is set up based on image statistical features and scene geometric features. This system is able to find best fitting geometric models for most of the images from the benchmark...
Facial asymmetry is an important characteristic used in a number of applications. It plays a vital role in human perception of attractiveness and as such has been used in psychology including research on facial expressions evaluation as well as in plastic surgery and orthodontics. It has been also recognized as a biometric feature used for identification and has important applications in detection...
Facial expression has an important role for natural interaction among social robots and humans. In this paper, an architecture conceived for imitation of facial expressions is proposed. We describe the computer vision algorithm that was implemented for real-time geometric facial features extraction. It covers face detection, extraction of eyes, eyebrows, nostrils and mouth characteristic points, as...
This work investigates a semantic-driven human detection algorithm, which employs global human template matching to inspire the local features based Adaboosting algorithm. We use distance transform to analyze distances between training samples and human contour template to obtain a classifier based on human outline features. At the training stage, the global outline feature will be coordinated into...
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