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To improve the performance of multi-object tracking in the complex scenario with frequent occlusions and cluttered backgrounds, a novel online multi-object tracking algorithm based on fuzzy logic is proposed. In the proposed algorithm, firstly, the similarity measure of multiple features between the objects and the measurements are calculated, including the background-weighted color feature, histogram...
This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina...
Minimally invasive surgical and diagnostic systems rely on endoscopic images of internal organs to assist medical tasks. Specular highlights are common on those images due to the strong reflectivity of the mucus layer on the organs and the relatively high intensity of the light source. This is a significant source of error that can affect the systems' performance. In this paper, we propose a segmentation...
Drift is the most difficult issue in object visual tracking based on framework of “tracking-by-detection”. Due to the self-taught learning, the mis-aligned samples are potentially to be incorporated in learning and degrade the discrimination of the tracker. This paper proposes a new tracking approach that resolves this problem by three multi-level collaborative components: a high-level global appearance...
This paper presented a SIFT based multiple instance learning algorithm to deal with the problem of pose variation in the tracking process. The MIL algorithm learns weak classifiers by using instances in the positive and negative bags. Then, a strong classifier is generated by powerful weak classifiers which are selected by maximizing the inner product between the classifier and the maximum likelihood...
This paper introduces an object following method based on the computational geometry and PTAM for Unmanned Aerial Vehicle(UAV) in unknown environments. Since the object is easy to move out of the field of view(FOV) of the camera, and it is difficult to make it back to the field of camera view just by relative attitude control, we propose a novel solution to re-find the object based on the visual simultaneous...
To overcome the real-time and robust problem of visual tracking, a visual tracking algorithm based on a fusion of multi cue and particle filter was proposed. Firstly, the integrating formula was transmuted on the framework of particle filter base on multi-cues which integrated multi-cues based on cues rather than feature points so that it could be used for parallel computing. Secondly, the EM algorithm...
An efficient gyro-aided iterative Earth Mover's Distance (iEMD) algorithm for visual tracking is proposed in this paper. The Earth Mover's Distance (EMD) is used as the similarity measure to search the optimal template candidates in color-spatial space in a video sequence. The computation of the EMD is formulated as the transportation problem from linear programming. The efficiency of this optimization...
This paper presents a new robustification procedure for nonlinear least-squares optimisation problems. In particular, we focus on the robustness of view-graph SLAM against outlier correspondences in the images and outlier geometries in the graph. Our method utilises revised measurements model linearisation and decision making to detect and remove outliers during data fusion. We utilise innovations...
In this paper, a model based on weighted extreme learning machine (weighted ELM) is proposed for visual tracking. The weighted ELM considers different class distributions both of the positive and negative classes, where extra weights are utilized in the framework. The proposed model simultaneously trains a certain number of weighted ELMs with different feature blocks. Moreover, the weighted multiple...
The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector)...
As traditional visual tracking algorithms based on co-training framework are characterized by poor robustness in numerous real-time scenarios, a robust visual tracking algorithm based on structural multi-scale features adaptive fusion in co-training is thereby proposed. In order to achieve a comprehensive representation, we select structural multi-scale features in gray intensity space and local binary...
Video hashing has attracted increasing attention in the field of video searching. However, there was no technical research on the prediction of hash length, which is extremely important in mobile circumstance. In this paper, a hash length prediction method is proposed for video hashing in the case of video copy detection. The video feature is mapped to video hashes with different lengths via kernel-based...
This paper addresses the problem of aggregating local binary descriptors for large scale image retrieval in mobile scenarios. Binary descriptors are becoming increasingly popular, especially in mobile applications, as they deliver high matching speed, have a small memory footprint and are fast to extract. However, little research has been done on how to efficiently aggregate binary descriptors. Direct...
In the real-word matching, deformation, outliers and other emerging variations are inseparable conditions which make matching increasingly difficult. One way to solve this challenging problem is raising an effective graph matching method which is flexible to non-rigid objects. As a good structure representation, graph can nicely describe objects with meaningful information. Meanwhile, feature pooling...
Infographic is a type of information visualization that uses graphic design to enhance human ability to identify patterns and trends. It is popularly used to support spread of information. Yet, there are few studies that investigate how infographics affect learning and how individual factors, such as learning styles and enjoyment of the information affect infographics perception. In this sense, this...
In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts:...
Developing an effective target appearance model is a challenging task due to the influence of factors such as partial occlusion, illumination variations, fast motion, etc. Existing appearance models usually utilize the tracking results from previous frames as target templates upon which the target appearance model is built by linear combinations of the templates. With such kind of representation,...
In this paper, a new visual tracking approach via the local patches and the contextual information of the target is presented. In the tracking procedure, the target object is decomposed into a set of patches of equal size and each patch is represented by using intensity and gradient histograms. Then, the likelihood of local patches is defined as the weighted sum of reliability and stability indices,...
In this paper, we propose a robust visual tracking method based on a temporal ensemble framework. Different from conventional ensemble-based trackers, which combine weak classifiers into a strong one using AdBoost in spatial fusion manners, our method adopts a powerful and efficient tracker integrated with its snapshots in different temporal windows of online tracking process to construct a temporal...
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