• Science of Emotion and Sensibility
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• v.23 no.3
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• pp.47-62
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• 2020

The snake detection theory posits that, due to competition with snakes, the primate visual system has been evolved to detect camouflaged snakes. Specifically, one of its hypotheses states that the subcortical visual pathway mainly consisting of koniocellular cells enables humans to automatically detect the threat of snakes without consuming mental resources. Here we tested the hypothesis by comparing human participants' responses to snakes with those to fearful faces and flowers. Participants viewed either original images or converted ones, which lacked the differences in color, luminance, contrast, and spatial frequency energies between categories. While participants in Experiment 1 produced valence and arousal ratings to each image, those in Experiment 2 detected target images in the breaking continuous flash suppression (bCFS) paradigm. As a result, visual factors influenced the responses to snakes most strongly. After minimizing visual differences, snakes were rated as being less negative and less arousing, and detected more slowly from suppression. In contrast, the images of the other categories were less affected by image conversion. In particular, fearful faces were rated as greater threats and detected more quickly than other categories. In addition, for snakes, changes in arousal ratings and those in bCFS response times were negatively correlated: Those snake images, the arousal ratings of which decreased, produced increased detection latency. These findings suggest that the influence of snakes on human responses to threat is limited relative to fearful faces, and that detection responses in bCFS share common processing mechanisms with conscious ratings. In conclusion, the current study calls into question the assumption that snake detection in humans is a product of unconscious subcortical visual processing.

• Journal of IKEEE
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• v.14 no.4
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• pp.291-296
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• 2010

This paper handles, pitching and rolling posture change detection using the visual image changes due to the road slope conditions. When the moving vehicle is slanted to a direction, the objects in the visual images of the vehicle are moving to up or down and right or left. This is similar to the human's balancing behavior depending on the visual image change detection as well as the vestibular organs and semicircular canal in the ear. The proposes method shows the visual image through the camera can be used for the image information itself and for the posture change detection through the experiments.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1284-1292
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• 2015

As visual surveillance systems become more and more common in human lives, approaches based on these systems to solve security problems in practice are boosted, especially in railway applications. In this paper, we first propose a robust snag detection algorithm and then present a railway security system by using a combination of multiple sensors and the vision based snag detection algorithm. The system aims safety at several repeatedly occurred situations including slope protection, inspection of the falling-object from bridges, and the detection of snags and foreign objects on the rail. Experiments demonstrate that the snag detection is relatively robust and the system could guarantee the security of the railway through these real-time protections and detections.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1183-1195
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• 2020

Visual saliency detection is an essential task because it is an important part of various vision-based applications. There are many techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is limited. In this paper, we introduce a simple approach for saliency detection in infrared images based on the thresholding technique. The input image is thresholded into several Boolean maps, and an initial saliency map is calculated as a weighted sum of the created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and a Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method has high performance when applied to real-life data.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.57-61
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• 2013

This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1094-1098
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• 2013

AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. Defect detection is not an easy process because of noises from various sources and optical distortion. In this paper the acquired images from a TFT panel are enhanced with the adoption of an HVS (Human Visual System). A human visual system is more sensitive on the defect area than the illumination components because it has greater sensitivity to variations of intensity. In this paper we modified an MTF (Modulation Transfer Function) in the Wavelet domain and utilized the characteristics of an HVS. The proposed algorithm flattens the inner illumination components while preserving the defect information intact.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.573-586
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• 2022

Corrosion can cause dangerous and expensive damage and failures of ship hulls and equipment. Therefore, it is necessary to maintain the vessel by periodic corrosion inspections. During visual inspection, many corrosion locations are inaccessible for many reasons, especially safety's point of view. Including subjective decisions of inspectors is one of the issues of visual inspection. Automation of visual inspection is tried by many pieces of research. In this study, we propose image preprocessing methods by image patch segmentation and thresholding. YOLOv5 was used as an object detection model after the image preprocessing. Finally, it was evaluated that corrosion detection performance using the proposed method was improved in terms of mean average precision.

• International Journal of Highway Engineering
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• v.15 no.5
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• pp.177-185
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• 2013

PURPOSES : This study attempts to design and establish the road surface condition detection system by using the image processing that is expected to help implement the low-cost and high-efficiency road information detection system by examining technology trends in the field of road surface condition information detection and related case studies. METHODS : Adapted visual information collecting method(setting a stereo camera outside of the vehicle) and visual information algorithm(transform a Wavelet Transform, using the K-means clustering) Experiments and Analysis on Real-road, just as four states(Dry, Wet, Snow, Ice). RESULTS : Test results showed that detection rate of 95% or more was found under the wet road surface, and the detection rate of 85% or more in snowy road surface. However, the low detection rate of 30% was found under the icy road surface. CONCLUSIONS : As a method to improve the detection rate of the mobile road surface condition information detection system developed in this study, more accurate phase analysis in the image processing process was needed. If periodic synchronization through automatic settings of the camera according to weather or ambient light was not made at the time of image acquisition, a significant change in the values of polarization coefficients occurs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1193-1202
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• 2004

While the management of digital contents is getting more and more important, many researchers have studied about scene change detection algorithms to reduce similar scenes in the video contents and to efficiently summarize video data. The algorithms using histogram and pixel information are found out as being sensitive to light changes and motion. Therefore, visual rhythm gets used in recent work to solve this problem, which shows some characteristics of scenes and requires even less computational power. In this paper, a new scene detection algorithm using visual rhythm by direction is proposed. The proposed algorithm needs less computational power and is able to keep good performance even in the scenes with motion. Experimental results show the performance improvement of about 30% comparing with conventional methods with histogram. They also show that the proposed algorithm is able to keep the same performance even to music video contents with lots of motion.