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In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations...
The paper describes a new method of detecting human figures in the video scene in real time. This problem can be found, for example, in the protection of buildings where unauthorized persons have access, surveillance of persons in common areas such as shopping centers, airport lounges, etc. For the detection of the contour of a human figure the HOG algorithm is often used which detects the human figure...
Human action classification is an important task in computer vision. The Bag-of-Words model uses spatio-temporal features assigned to visual words of a vocabulary and some classification algorithm to attain this goal. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have applied this method to the KTH dataset to obtain a vocabulary with...
Pedestrian detection is of much importance for its practical applications. This paper develops a novel pedestrian detection system which consists of three stages: motion region detection based on background modeling, feature extraction in the guidance of prior information, and map-based classification applying support vector machine (SVM) and Adaboost. First of all, an adaptive Gaussian Mixture Model...
To detect human sex from complex background, illumination variations and objects by machine is very difficult but important for adaptive information service. In this research, we present a preliminary design and experimental results of gender recognition from walking movements that utilizes gait energy image(GEI) with denoised energy image(DEI) pre-processing as support vector machine(SVM) classifier...
The paper presents a new online incremental zero-shot learning method for applications in robotics and mobile communications where attribute labeling is obtained via online interaction with users, and where the potential for inconsistency exists. Unique to most previous offline batch learning methods, the proposed method is based on the indirect-attribute-prediction (IAP) model instead of the direct-attribute-prediction...
Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. In this paper, we address such problems by proposing a robust part-based tracking-by-detection framework. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Our approach learns part-based person-specific SVM...
Activity recognition in video is dominated by low- and mid-level features, and while demonstrably capable, by nature, these features carry little semantic meaning. Inspired by the recent object bank approach to image representation, we present Action Bank, a new high-level representation of video. Action bank is comprised of many individual action detectors sampled broadly in semantic space as well...
In this paper, an integrated framework with multiple sensory information for analysing human hand motions is proposed, and it consists of components of system integration, signal preprocessing, correlation study of sensory information and human motion recognition based on manipulation intention. Three types of sensors are employed in the framework to simultaneously capture the finger angle trajectory,...
Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. Majority of models are based on low-level visual features and the importance of top-down factors has not yet been fully explored or modeled. Here, we combine low-level features such as orientation, color, intensity, saliency...
We present a hierarchical model for human activity recognition in entire multi-person scenes. Our model describes human behaviour at multiple levels of detail, ranging from low-level actions through to high-level events. We also include a model of social roles, the expected behaviours of certain people, or groups of people, in a scene. The hierarchical model includes these varied representations,...
In many visual classification tasks the spatial distribution of discriminative information is (i) non uniform e.g. person ‘reading’ can be distinguished from ‘taking a photo’ based on the area around the arms i.e. ignoring the legs and (ii) has intra class variations e.g. different readers may hold the books differently. Motivated by these observations, we propose to learn the discriminative spatial...
Since high-level events in images (e.g. “dinner”, “motorcycle stunt”, etc.) may not be directly correlated with their visual appearance, low-level visual features do not carry enough semantics to classify such events satisfactorily. This paper explores a fully compositional approach for event based image retrieval which is able to overcome this shortcoming. Furthermore, the approach is fully scalable...
Visual reranking has become a widely-accepted method to improve traditional text-based image search results. The main principle is to exploit the visual aggregation property of relevant images among top results so as to boost ranking scores of relevant images, by explicitly or implicitly detecting the confident relevant images, and propagating ranking scores among visually similar images. However,...
The human vision tends to recognize more variants of a distinctive exemplar. This observation suggests that discriminative power of training exemplars could be utilized for shaping a desirable global classifier that generalizes maximally from a few exemplars. We propose to derive classification uncertainty for each exemplar, using a local classification task to separate the exemplar from those in...
Recent studies have shown that the various brain networks over different cognitive states. In contrast to measure a physiological change over a single region, the information flows between brain regions described by effective connectivity provides an informative dynamic over the whole brain. In this study, we proposed a source information flow network based on the combination of Granger causality...
Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the...
It is crucial to get human hand information for hand gesture recognition tasks. However, at present, people can not still get a perfect hand segmentation or localize hand accurately especially under complex conditions. Therefore, it is necessary to develop robust and effective methods for detecting human hand accurately. In this paper, we propose a new method for hand detection. We present an extended...
An important task of aging research is to find genes that regulate lifespan. Wet-lab identification of aging genes is a tedious and labor-intensive activity. Developing an algorithm to predict aging genes for guiding wet-lab experiment should be greatly helpful. In this paper, we systematically analyzed topological features of proteins encoded by Mus musculus aging genes versus those encoded by non-aging...
In this paper we propose the first (to the best of our knowledge) overall quality assessment scheme for facial images based on statistical learning. The overall quality assessment system is trained on the subjective quality scores, and is with a high fidelity to the human vision system (HVS) model. This scheme employs a hierarchical binary decision tree classifier based on support vector machines...
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