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Self-organising neural networks have shown promise in a variety of applications areas. Their massive and intrinsic parallelism makes those networks suitable to solve hard problems in image-analysis and computer vision applications, especially when non-stationary environments occur. Moreover, this kind of neural networks preserves the topology of an input space by using their inherited competitive...
Enlarging or reducing the template size by adding new parts, or removing parts of the template, according to their suitability for tracking, requires the ability to deal with the variation of the template size. For instance, real-time template tracking using linear predictors, although fast and reliable, requires using templates of fixed size and does not allow on-line modification of the predictor...
This paper presents a completely automated 3D facial feature tracking system using 2D+3D image sequences recorded by a real-time 3D sensor. It is based on local feature detectors constrained by a 3D shape model, using techniques that make it robust under pose and partial occlusion. Several experiments conducted under relatively non-controlled conditions demonstrate the accuracy and robustness of the...
We derive a probabilistic framework for robust, realtime, visual tracking of multiple previously unseen objects from a moving camera. This framework models the discrete depth ordering of the objects being tracked in the scene. The method uses the observed image data to compute a posterior over the objects' poses, shapes and relative depths. The poses are group transformations, the shapes are implicit...
Simultaneous registration and segmentation (SRS) provides a powerful framework for tracking an object of interest in an image sequence. The state-of-the-art SRS-based tracking methods assume that the illumination is maintained constant across consecutive frames. However, this assumption does not hold in many natural image sequences due to dynamic light source and shadows. We propose a generalized...
We propose a graph-based approach for semi-automatic tracking of the human tongue in 2D+time ultrasound image sequences. We construct a graph capturing the intra- (spatial) and inter-frame (temporal) relationships between the dynamic contour vertices. Tongue contour tracking is formulated as a graph-labeling problem, where each vertex is labeled with a displacement vector describing its motion. The...
We present a practical approach for surface reconstruction of smooth mirror-like objects using sparse reflection correspondences (RCs). Assuming finite object motion with a fixed camera and un-calibrated environment, we derive the relationship between RC and the surface shape. We show that by locally modeling the surface as a quadric, the relationship between the RCs and unknown surface parameters...
According to the temporal characteristic and the spatial characteristic of video sequence, a novel recognition method of sign language spatio-temporal appearance modeling is introduced for the vision-based multi-features classifier of Chinese sign language recognition. The obvious advantage with such a novel approach is that we can exclude some skin-like object and tracking the moving recognized hand...
Maximally Stable Extremal Region (MSER) has been proved to be a powerful local invariant feature. The original MSER detector only utilizes the intensity space. To get more information from a color image, it is naturally to extract MSERs from H, S and I spaces, or other color spaces. However, the increased MSER set inevitably bring in some unstable MSERs, and this will contaminate the over-all reliability...
Time-lapse microscopy imaging serves as an important method to measure the cycle progression of individual cells in a large population. The lacking of automatic systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper we present an effective computerized method for overlapping cell nuclei detection in time-lapse...
The varying density of the cell culture and the complexity of the cell behavior, shape deformation, mitosis, close contact and partial occlusion pose many challenges to existing tracking techniques. In this paper, a novel automatic multi-cells tracing algorithm is put forward. We implemented the automatic tracking method that proceeds in three steps: Firstly, we detect the individual cells in each...
The convergence and stability of the level set method depend on the speed function. Therefore, it is important to define the speed function in a manner that is suitable for the individual application. In the present paper, we propose a novel speed function of the level set method for moving object extraction from a video sequence with a stationary background. The speed function focuses on the precision...
This paper presents a new behavior classification system that can analyze human behaviors from arbitrary views. Technically, if different viewing angle are used for observing a person, his appearances will change significantly. To freely recognize his behaviors, traditional methods tend to adopt 3-D data for behavior analysis. However, its inherent correspondence process will make it inappropriate...
This paper deals with a new registration method based on a specific level-line grouping. Because of its contrast-change invariance, our approach is an appropriate method for matching outdoor image sequences. Moreover, it does not require any estimation of the unknown transformation between images and handle well the critical cases that usually lead to pairing ambiguities, such as repetitive patterns...
This paper deals with a new registration method based on a specific level-line grouping. Because of its contrast-change invariance, our approach is an appropriate method for matching outdoor image sequences. Moreover, it does not require any estimation of the unknown transformation between images and handle well the critical cases that usually lead to pairing ambiguities, such as repetitive patterns...
In this paper, we propose a novel multi-pattern based search technique, TCon search, for fast block matching motion estimation. It starts with small cross shaped and small triangular shaped patterns. Afterwards, based on the previous step optimal motion vector, the search pattern for next step is selected. Except first and last step, each search step considers only three points thus reducing the number...
In this paper, we attempt to enhance the overall recognition rate for view-invariant gait recognition. We propose a simple but efficient framework for this task with training gait sequences from multiple views. A most important problem in the framework is about the optimal choice for the training views, that is, how many views are enough to ensure a satisfying overall performance and how to combine...
In this paper, we propose a novel approach that combines particle filter tracking and 3D graph cut based segmentation to achieve silhouette tracking against drastic scale change and occlusion. The segmentation module offers particle filter tracking procedure the target shape information to compensate spatial information loss in the histogram based particle filter tracking process. Meanwhile, particle...
This paper proposes a novel neural network approach for human action recognition based on Self Organizing Map (SOM). The SOM acts as a tool to cluster feature data and to reduce data dimensionality. The key poses in action sequences are extracted by the trained SOM. After the mapping of SOM, a human action sequence is represented as a trajectory of map units. For action recognition, a longest common...
The paper focuses on the problem of structure and motion recovery from a monocular image sequence under quasi-perspective projection model. Previous study on this problem adopts singular value decomposition (SVD) to the tracking matrix with rank constraint. The method is time consuming and does not work for incomplete data. In this paper, we propose to adopt power factorization to the problem. The...
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