• Korean Journal of Cognitive Science
• /
• v.16 no.3
• /
• pp.155-174
• /
• 2005

Oh and Kim (2004) and Woodman and Lurk (2004) demonstrated that spatial working memory (SWM) load Interfered concurrent visual search and that search process also impaired the maintenance of spatial information implying that visual search and SWM task both require access to the same limited-capacity mechanism. Two obvious possibilities have been suggested about what this shared limited-capacity mechanism is: common demand for attention to the locations where the items f9r the two tasks were presented (spatial attention load hypothesis), and common use of working memory to maintain a record of locations have been processed(SWM load hypothesis). To test these two hypothetical explanations, Experiment 1 replicated the mutual interference between visual search and SWM task in spite of difference of procedure with preceding researches; possible areas where the items for two tasks were presented were not separated. In Experiment 2, we presented the items for visual search either in the same quadrants where the items for SWM task had appeared (same-location rendition) or in the different quadrants (different-location condition). As a result, search efficiency was more impaired in the different-location condition than in the same-location condition. The memory accuracy was worse in the different-location rendition than in the same-location rendition. Overall results of study indicate that the mutual interference between SWM and visual search might be related to the overload of spatial attention, but not to that of SWM.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.1
• /
• pp.16-37
• /
• 2022

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.8
• /
• pp.1843-1859
• /
• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.10
• /
• pp.3390-3405
• /
• 2022

Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models. However, features of salient regions and spatial information are not utilized sufficiently in most related works. Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step. In the paper, two innovative approaches are proposed to solve these problems. First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously. SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features. Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information. Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.6
• /
• pp.89-95
• /
• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.

• Therapeutic Science for Rehabilitation
• /
• v.6 no.1
• /
• pp.11-23
• /
• 2017

Spatial neglect is a neurological disorder following stroke, a lesion that usually affects the right hemisphere, fail to process or attention on the contralateral side of body and space. Functional neuroimaging studies report that spatial neglect is associated with lesions of large middle cerebral artery, perisylvian network and attention network. Spatial neglect is associated with a poor outcome. For optimal diagnosis and intervention, Types and theories of spatial neglect should be considered, in addition to clinical assessment with the conventional test and functional test. The treatment for spatial neglect could be consist of top-down approaches and bottom-up approaches. Recent trends in rehabilitation intervention for spatial neglect have reported prism adaptation.

• Korean Journal of Cognitive Science
• /
• v.18 no.3
• /
• pp.285-303
• /
• 2007

Transient attention impairs observers' temporal resolution in a cued location. This detrimental effect of attention was ascribed to inhibitory connections from parvocellular to magnocellular neurons [1]. Alternatively, the difficulty might arise because attention facilitates the temporal summation of two successive stimuli. The current study examined this hypothesis by manipulating the luminance polarity of the stimuli against a background. Attention should not modulate temporal summation of two anti-polar stimuli because these are processed in separate channels. Indeed, observers judged the temporal order of two successive stimuli better in the cued location than in the uncued location when the stimuli were opposite in polarity, but temporal resolution was worse in the cued location when the stimuli had the same polarity. Thus, attentional effects on temporal resolution may be attributed to temporal summation rather than parvocellular inhibition of magnocellular activity.

• Journal of Information Technology Services
• /
• v.16 no.1
• /
• pp.55-72
• /
• 2017

Many of the studies of visual attention that are currently underway are seeking ways to make application systems that can be used in practice, and obtained good results using not only simulated images but also real-world images. However, despite that previous studies of selective visual attention are models intended to implement the human vision, few experiments verified the models with actual humans and there is no standardized data nor standardized experimental method for actual images. Therefore, in this paper, we propose a new performance evaluation techniques necessary for evaluation of visual attention systems. We developed an evaluation method for evaluating the performance of the visual attention system through comparison with the results of the human experiments on visual attention. Human experiments on visual attention is an experiments where human beings are instinctively aware of the unconscious when images are given to humans. So it can be useful for evaluating performance of the bottom-up attention system. Also we propose a new selective attention system that guides the user to effectively detect ROI regions by using spatial and temporal features adaptively selected according to the input image. We evaluated the performance of proposed visual attention system through the developed performance evaluation method, and we could confirm that the results of the visual attention system are similar to those of the human visual attention.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.1
• /
• pp.64-73
• /
• 2015

Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.

• Korean Journal of Cognitive Science
• /
• v.10 no.3
• /
• pp.1-15
• /
• 1999

Based on known physiological and psychophysical results, a neural network model for visual selection, called FeaureGate is proposed. The model consists of a hierarchy of spatial maps. and the flow of information from each level of the hierarchy to the next is controlled by attentional gates. The gates are jointly controlled by a bottom-up system favoring locations with unique features. and a top-down mechanism favoring locations with features designated as target features. The present study focuses on the top-down mechanism of the FeatureGate model that produces results similar to Moran and Desimone's (1985), which many current models have failed to explain, The FeatureGate model allows a consistent interpretation of many different experimental results in visual attention. including parallel feature searches and serial conjunction searches. attentional gradients triggered by cuing, feature-driven spatial selection, split a attention, inhibition of distractor locations, and flanking inhibition. This framework can be extended to produce a model of shape recognition using upper-level units that respond to configurations of features.