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In this paper, a new optimized method with estimation and competition is proposed for false matching in movement estimation based on vision measurement technology. Firstly, the former vector results processed by optimized phase-correlation are used to estimate current matching. Secondly, the estimation results supply important parameters for competition matching. Furthermore, the method can be processed...
This paper proposes an approach for segmenting single actions from continuously captured motion sequences by examining the properties of active limbs. The target motions are related to sporting and dancing. In particular, two types of human sports motions are examined: 1) boxing and 2) hip hop dance. To segment continuous boxing motion sequences into single punches and combo punches, this paper employs...
It is important to protect children from harmful effects of objectionable materials, such as pornography, which are now prevalent on the Internet. In this paper, a new method from the feature porno-sounds recognition point of view is proposed to detect adult video sequences automatically which serves as a complementary approach to the recognition method from image's point of view. To the special of...
This paper proposes an approach to extract motion features from sequences of images of human behavior.A novel algorithm called two-dimensional continuous dynamic programming (2DCDP) is proposed, which can obtain a set of correspondence data for all pixels between sequential images.The 2DCDP algorithm performs segmentation-free detection of objects in an input image from representations in reference...
Trained detectors are the most popular algorithms for the detection of vehicles or pedestrians in video sequences. To speed up the processing time the trained stages build a cascade of classifiers. Thereby the classifiers become more powerful from stage to stage. The most popular classifier for real-time applications is Adaboost applied to rectangular Haar-like features. The processing time of these...
This paper presents a novel technique for depth map estimation using a sequence of images acquired at varying focus. In depth map estimation noise, illumination variations and types of extracted features significantly affect the performance of a focus measure. This paper proposes the use of SUSAN operator, to extract features, because of its structure preserving noise filtering which plays a pivotal...
Topological visual maps contain different abstraction levels of information that can be used by robots to carry out different activities. We propose here a new hierarchical structure in which landmarks extracted from conventional images are grouped creating a graph of planar regions. The new hierarchy improves previous approaches based on images reducing both, the size of the graph and its complexity...
In this paper, we present a new posture classification system to analyze different human activities directly from video sequence. For well recognizing each posture of an activity, we propose an adaptation of Radon transform called R-transform, which is invariant to common geometrical transformations, to represent low-level features. The advantage of the R transform lies in its low computational complexity...
Large motion displacements in image sequences are still a problem for most motion estimation techniques. Progress in feature matching allows to establish robust correspondences between images for a sparse set of points. Recent works have attempted to use this sparse information to guide the dense motion field estimation. We propose to achieve this in an extended motion estimation framework, which...
The quantitative analysis of live cells is a key issue in evaluating biological processes. The current clinical practice involves the application of a tedious and time consuming manual tracking procedure on large amount of data. As a result, automatic tracking systems are currently developed and evaluated. However, problems caused by cellular division, agglomeration, Brownian motion and topology changes...
A novel system for the recognition of spatiotemporal hand gestures used in sign language is presented. While recognition of valid sign sequences is an important task in the overall goal of machine recognition of sign language, recognition of movement epenthesis is an important step towards continuous recognition of natural sign language. We propose a framework for recognizing valid sign segments and...
In this paper we propose a gesture perception algorithm using compact one-dimensional representation of spatio-temporal motion-field patches. At the learning stage, motion-field patches are randomly extracted and stored as templates. When generating feature vectors for video sequences, we compare stored templates with video, calculate maximum similarities and save those values as elements of feature...
Image classification is an important technique for effective content-based multimedia retrieval. Many classifiers have been proposed while frequent patterns based approaches have received many attentions in recent years. In this paper, we proposed an image classification approach utilizing sequential patterns discovered from distinct classes. The image is segmented and low-level features are extracted...
In this paper, an automated video surveillance for crime scene detection using statistical characteristics is presented. The system is named Public Safety System(PSS). If the scene shows some peculiar situation such as purse-snatching, kid napping and fighting on the street, the PSS recognize the situation and automatically report to agency. Localization of moving targets in the scene and human behavior...
Today, elder care demands a greater degree of versatility in healthcare. Automatic monitoring devices and sensors are under development to help senior citizens achieve greater autonomy, and, as situations arise, alert healthcare providers. In this paper, we study gait patterns based on extracted silhouettes from image sequences. Three features are investigated through two different image capture perspectives:...
In order to facilitate the study of neuron migration, we propose a method for 3-D detection and tracking of centrosomes in time-lapse confocal image stacks of live neuron cells. We combine Laplacian-based blob detection, adaptive thresholding, and the extraction of scale and roundness features to find centrosome-like objects in each frame. We link these detections using the joint probabilistic data...
We present an algorithm for slideshow detection in video databases such as YouTube or Blip.TV. Our solution is based around feature tracking to extract movement between sequentially captured frames. This movement is then analysed through the use of the Hough transform and compared against behaviour commonly exhibited by slideshows: still and panning static images. We show experimentally the effectiveness...
Pain is generally measured by patient self-report, normally via verbal communication. However, if the patient is a child or has limited ability to communicate (i.e. the mute, mentally impaired, or patients having assisted breathing) self-report may not be a viable measurement. In addition, these self-report measures only relate to the maximum pain level experienced during a sequence so a frame-by-frame...
We here present a multi-sensor data fusion architecture that takes into account the performance of video sensors in detecting moving targets for video surveillance purposes. Target detection and tracking is performed via classification by an ensemble of classifiers learned online using heterogeneous features for each target. A novel approach is then used to estimate the position of the target on the...
Skeleton has very important applications in objects expression, data compression, computer vision and animation. In the discrete space, the basic skeleton algorithms have two categories: one is thinning, the other is based on the distance transformation, in a high-dimensional space generated from the surface to form the ridge to create a skeleton. The skeleton which is based on the distance transform...
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