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Neural representations for object recognition are difficult to construct because vision operates in highdimensional space. This study aims to develop low-dimensional neural representations (“manifolds”) that could contain either rotation or viewpoint information. In our experiments, four rotating tools were used as visual stimuli and brain activity was recorded using functional magnetic resonance...
Foxmail client is one of the most popular tools to send and receive e-mail, and the mail data files preserved in it become an important target of computer investigation and forensics, from which the useful clues can be mined out and analyzed. In this paper, a visual Foxmail forensics system is designed to extract the information from the mail evidence file and display the association between the contacts...
Local Optima Networks were proposed to understand the structure of combinatorial landscapes at a coarse-grained level. We consider a compressed variant of such networks with features that are meaningful for the study of search difficulty in the context of local search. In particular, we investigate different landscapes of the Permutation Flowshop Scheduling Problem. The insert and 2-exchange neighbourhoods...
According to the influence of the stand spatial structure on the crown shape of Chinese fir, a new method of diversified 3D Chinese fir modeling based on spatial structure was proposed. In this study, spatial structure units that were divided by a reasonable method were selected in Chinese fir stands. And the data of spatial structure and crown shape in different units was surveyed. Two parameters...
Correlation filter (CF) tracker has many advantages in visual tracking. But the update strategies of most CF trackers today such as STC are so simple that it can not handle more complex situations. For this problem, the paper presents a novel CF tracker based on templates, spatio-temporal context template set (STCTS) tracker. The algorithm not only improves the tracking strategy, but also improves...
Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods, such as Canonical Correlation Analysis (CCA) and a number of variants. Despite their effectiveness, due to their strong dependence on the correct calculation of correlations, these methods may prove to be inadequate in front of potential deficiency in the number...
Multi-target stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, a large interval between two neighboring stimulus frequencies is often used to improve classification accuracy. Although recent progresses in stimulus coding and target identification methods that have significantly improved...
Visual brain-computer interfaces (BCIs) have achieved great progress in speed recently. But the problem of visual fatigue caused by intense flashes poses a great challenge in designing practical systems for long-term use. A direct way to improve visual comfort is to reduce the stimulus contrast. But it could also weaken the featured evoked potentials, which would bring a negative impact on system...
Deep convolutional neural networks (CNNs) trained for object classification have a number of striking similarities with the primate ventral visual stream. In particular, activity in early, intermediate, and late layers is closely related to activity in V1, V4, and the inferotemporal cortex (IT). This study further compares activity in late layers of object-classification CNNs to activity patterns...
This paper presents an experimental study on the legibility of Chinese characters printed on one-way vision film. In the study, Chinese characters of different font sizes were viewed from different distances by research subjects with normal eyesight in order to measure the lowest legibility value. The data generated as such failed to pass homogeneity of variance test. As homogeneity of variance is...
Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in brain states classification tasks, reconstructing the details of human visual experience still remains difficult. Two main challenges that hinder the development of effective models are the perplexing fMRI measurement noise...
The shape and color of visual identity are the most important factors in the visualization of trademarks, which exert a far-reaching influence on the establishment of corporate image and brand image. Under the impact of globalization, brands have become a major factor in keeping a foothold in the consumer market, and sales are no longer limited to certain regions. Today's ideological trend of design...
The change of appearance of the target object is one of important issue in visual tracking. It is because some factors such as camera motion, illumination change, motion change, occlusion, and size change are influenced to the object target during tracking. Recently, discriminative correlation filters (DCF) gave good results to handle these problems. Unfortunately, the DCF only works in the single-resolution...
Today, video data, as a powerful multimedia component, is accompanied by some problems with increasing usage in communication, health, education, and social media in particular. Classification and detection of concepts in video data by automatic methods are some of these challenging problems. In this study, we propose a video classification system, which incorporates deep convolutional neural networks...
In this paper, we address the problem of visual tracking by proposing a novel feature learning technique. Recently, correlation filter based methods have dominated the visual tracking community due to various reasons such as efficient dense matching in frequency domain and simple update strategy. Nevertheless, the studies of correlation filters utilize handcrafted or pre-trained deep features of classification...
Wireless visual sensor networks comprise a large number of camera-equipped sensor devices and obtain visual information from field of interest. In a Wireless visual sensor network, there exist visual correlation characteristics among images observed by cameras with overlapped field of views. To describe those characteristics, the conventional method is based on image processing. However, it is too...
Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension...
To achieve more effective solution for large-scale image classification (i.e., classifying millions of images into thousands or even tens of thousands of object classes or categories), a deep multi-task learning algorithm is developed by seamlessly integrating deep CNNs with multi-task learning over the concept ontology, where the concept ontology is used to organize large numbers of object classes...
Background music plays an important role in making user-generated video more colorful and attractive. One of current research on automatic background music recommendation is the correlation-based approach in which the correlation model between visual and music features is discovered from training data and is utilized to recommend background music for query video. While existing correlation-based approaches...
Hashing methods have proven to be useful for a variety of tasks and have attracted extensive attention in recent years. Various hashing approaches have been proposed to capture similarities between textual, visual, and cross-media information. However, most of the existing works use a bag-of-words methods to represent textual information. Since words with different forms may have similar meaning,...
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