• Korean Journal of Cognitive Science
• /
• v.19 no.2
• /
• pp.125-142
• /
• 2008

This study investigated the effects of space- and object-based attention on spatial and visual working memory, by measuring recognition of working memory on the spatial Stroop task including two modalities of attention resource. The similarity condition of stimulus arrangement between working memory task and spatial stroop task was manipulated in order to examine the effects of space-based attention on spatial rehearsal during working memory task, while Stroop rendition was manipulated in order to examine the effects of object-based attention on object rehearsal during working memory task. The results showed that in a condition that stimulus arrangement was highly similar for the spatial working memory task and the spatial Stroop task, recognition accuracy of the spatial working memory was high, but it was not significantly different with the Stroop conditions. In contrast, the recognition accuracy of visual working memory in the incongruent Stroop condition was lower than that in the congruent Stroop condition, but it was not significantly different with the similarity conditions (25% vs. 75%). The results indicated that selective attention has effects on working memory only when resource modality of working memory is the same as that of selective attention.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.4
• /
• pp.36-44
• /
• 1997

An efficient context-free multiple-object segmentation using attention operator based on modified generalized symmetry transform is proposed and implemented by modifying a radial basis function network. By using the difference of intensity gradient, instead of te intensity gradient itself, in generalized symmetry tranform so as to make the attention operator to preserve the edges of the objects shape, an efficient context-free multiple-object segementation is proposed in which no a priori shape informtion on the objects is requried. The attention operator is implemented by using a modified radial basis function network which can reflect symmetry, and by using te edge pyramid of the input image, both of the local and the global symmetry of the objects are reflected simultaneously to make the multiple-object with different sizes be segmented with a singel fixed-size $n\timesm$ can be done with O(n) complexity. The simulaton results show that the proposed algorithm can efficiently be used in context-free multiple-object segmentation even for the low contrast IR images as well as for the images from the camera.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.4
• /
• pp.381-389
• /
• 2022

Detecting partially occluded objects is difficult due to the appearances and shapes of occluders are highly variable. These variabilities lead to challenges of localizing accurate bounding box or classifying objects with visible object parts. To address these problems, we propose a two-stage part-based attention approach for robust object detection under partial occlusion. First, our part attention network(PAN) captures the important object parts and then it is used to generate weighted object features. Based on the weighted features, the re-weighted object features are produced by our reinforced PAN(RPAN). Experiments are performed on our collected military vehicle dataset and synthetic occlusion dataset. Our method outperforms the baselines and demonstrates the robustness of detecting objects under partial occlusion.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.1
• /
• pp.50-58
• /
• 2007

In this paper, we propose a query-based visual attention algorithm for effective object finding of a vision-based mobile robot. This algorithm is developed by extending conventional bottom-up visual attention algorithms. In our proposed algorithm various conspicuity maps are merged to make a saliency map, where weighting values are determined by query-dependent object properties. The saliency map is then used to find possible attentive location of queried object. To show the validities of our proposed algorithm, several objects are employed to compare performances of our proposed algorithm with those of conventional bottom-up approaches. Here, as one of exemplar query-dependent object property, color property is used.

• Journal of Information Processing Systems
• /
• v.9 no.4
• /
• pp.592-601
• /
• 2013

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the appropriate frequency. Secondly, we applied the Gaussian Mixture Model (GMM) to locate the object region. By incorporating the visual attention analysis into object segmentation, our proposed approach is able to narrow the search region for object segmentation, so that the accuracy is increased and the computational complexity is reduced. The experimental results indicate that our proposed approach is efficient for object segmentation in natural images, especially for small objects. Our proposed method significantly outperforms traditional GMM based segmentation.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.306-308
• /
• 2006

We propose an attention based object recognition system, to recognize object fast and robustly. For this we calculate visual stimulus degrees and make saliency maps. Through this map we find a strongly attentive part of image by stimulus degrees, where local features are extracted to recognize objects.

• Journal of the Korea Convergence Society
• /
• v.11 no.12
• /
• pp.39-47
• /
• 2020

In this paper, we proposed a deep learning-based detection method using Saliency Attention to detect salient objects in images. The salient object detection separates the object where the human eye is focused from the background, and determines the highly relevant part of the image. It is usefully used in various fields such as object tracking, detection, and recognition. Existing deep learning-based methods are mostly Autoencoder structures, and many feature losses occur in encoders that compress and extract features and decoders that decompress and extend the extracted features. These losses cause the salient object area to be lost or detect the background as an object. In the proposed method, Saliency Attention is proposed to reduce the feature loss and suppress the background region in the Autoencoder structure. The influence of the feature values was determined using the ELU activation function, and Attention was performed on the feature values in the normalized negative and positive regions, respectively. Through this Attention method, the background area was suppressed and the projected object area was emphasized. Experimental results showed improved detection results compared to existing deep learning methods.

• Electronics and Telecommunications Trends
• /
• v.37 no.1
• /
• pp.73-83
• /
• 2022

Visual object tracking can be utilized in various applications and has attracted considerable attention in the field of computer vision. Visual object tracking technology is classified in various ways based on the number of tracking objects and the methodologies employed for tracking algorithms. This report briefly introduces the visual object tracking challenge that contributes to the development of single object tracking technology. Furthermore, we review ten Siamese network-based algorithms that have attracted attention, owing to their high tracking speed (despite the use of neural networks). In addition, we discuss the prospects of the Siamese network-based object tracking algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.12
• /
• pp.4439-4455
• /
• 2021

Compared with two-stage object detection algorithms, one-stage algorithms provide a better trade-off between real-time performance and accuracy. However, these methods treat the intermediate features equally, which lacks the flexibility to emphasize meaningful information for classification and location. Besides, they ignore the interaction of contextual information from different scales, which is important for medium and small objects detection. To tackle these problems, we propose an image pyramid network based on dual attention mechanism (DAIPNet), which builds an image pyramid to enrich the spatial information while emphasizing multi-scale informative features based on dual attention mechanisms for one-stage object detection. Our framework utilizes a pre-trained backbone as standard detection network, where the designed image pyramid network (IPN) is used as auxiliary network to provide complementary information. Here, the dual attention mechanism is composed of the adaptive feature fusion module (AFFM) and the progressive attention fusion module (PAFM). AFFM is designed to automatically pay attention to the feature maps with different importance from the backbone and auxiliary network, while PAFM is utilized to adaptively learn the channel attentive information in the context transfer process. Furthermore, in the IPN, we build an image pyramid to extract scale-wise features from downsampled images of different scales, where the features are further fused at different states to enrich scale-wise information and learn more comprehensive feature representations. Experimental results are shown on MS COCO dataset. Our proposed detector with a 300 × 300 input achieves superior performance of 32.6% mAP on the MS COCO test-dev compared with state-of-the-art methods.

• Korean Journal of Cognitive Science
• /
• v.19 no.3
• /
• pp.311-330
• /
• 2008

The effect of spatial attention gradually decreases as a function of the distance between the locus of attention and a target. According to this hypothesis, we tested the spatial-effect profile of visual attention when it operates on perception and memory. Experiment 1 measured accuracy of discriminating the color of a simultaneously masked target after presenting a pre-cue to either at the target location or away from the target (perception-intensive task). Experiment 2 measured accuracy of recognizing the color of several items at and around the pre-cued location (memory-intensive task). In the perception-intensive condition, the accuracy gradually dropped as the distance between the cue and target location increases. However, in the memory-intensive condition, subjects remembered only the item at the cued location. This suggests spatial attention in a memory-intensive process would operate on object-based representations. Experiment 2 showed the object-based effect observed in Experiment 1 can be also present in perception under a special circumstance. The results indicate that spatial attention can operate on object-based representations in a memory-intensive process whereas it flexibly can operate either on location-based or object-based representations in a perception-intensive process.