RETRACTED: An Infrared Small Target Detection Method Based on a Weighted Human Visual Comparison Mechanism for Safety Monitoring
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Infrared small target detection is a crucial technology in both military and civilian applications, including surveillance, security, defense, and combat. However, accurate infrared detection of small targets in real-time is challenging due to their small size and similarity in gray level and texture with the surrounding environment, as well as interference from the infrared imaging systems in unmanned aerial vehicles (UAVs). This article proposes a weighted local contrast method based on the contrast mechanism of the human visual system. Initially, a combined contrast ratio is defined that stems from the pixel-level divergence between the target and its neighboring pixels. Then, an improved regional intensity level is used to establish a weight function with the concept of ratio difference combination, which can effectively suppress complex backgrounds and random noise. In the final step, the contrast and weight functions are combined to create the final weighted local contrast method (WRDLCM). This method does not require any preconditioning and can enhance the target while suppressing background interference. Additionally, it is capable of detecting small targets even when their scale changes. In the experimental section, our algorithm was compared with some popular methods, and the experimental findings indicated that our method showed strong detection capability based on the commonly used performance indicators of the ROC curve, SCRG, and BSF, especially in low signal-to-noise ratio situations. In addition, unlike deep learning, this method is appropriate for small sample sizes and is easy to implement on FPGA hardware.Keywords:
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This appendix explains how to group pixels in a given 2D array into super-pixels, each containing multiple pixels, while maintaining the proper ordering of pixels. Creating super-pixels should group adjacent pixels in both the horizontal and vertical dimensions. If we grouped pixels in super-pixels only along the axis row-by-row, this would be analogous to having a detector with elements that are long horizontally and thin vertically. Our goal is to properly group the pixels for square detector elements.
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Neurophysiological findings and recent theorizing suggest that contrast may influence the ease of attentional selection, with high-contrast stimuli easy to select and hard to ignore. We tested this in four experiments. In Exp 1, subjects searched for a target (an “8” or a “9”) in a display of digits. In separate blocks, subjects searched a display of: (A) low-contrast digits, (B) high-contrast digits, (C) half low- and half high-contrast digits, with the target appearing among the low-contrast digits, or (D) half low- and half high-contrast digits, with the target appearing among the high-contrast digits. In conditions C and D, subjects were told the contrast of the target, potentially allowing them to select based on contrast. Subjects performed significantly better in condition C than in A (and better in condition D than in B), indicating that contrast differences between relevant and irrelevant stimuli improves search even when the irrelevant stimuli are of higher contrast. In Exps 2 and 3, subjects searched for a target among digits within half of the items, which was defined by color (red vs. green) or location, respectively. The contrast of these “relevant” and “irrelevant” sets was independently manipulated. When the relevant subset was defined by color, the search was easier whenever the “relevant” and “irrelevant” items had different contrast levels, even when the distractors were high-contrast. However, when the relevant items were in distinct locations, search was harder when the irrelevant subset was of high-contrast. This was not true, however, when the same experiment was repeated with different contrast levels presented in different blocks (Exp 4). Overall, the results suggest that selective attention to either high- or low-contrast is readily achieved, although this capability is not always utilized. The results challenge the most obvious linkage between attentional function and neurophysiological findings concerning contrast and attention.
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K-pass pixel value ordering (PVO) is an effective reversible data hiding (RDH) technique. In k-pass PVO, the complexity measurement may lead to a weak estimation result because the unaltered pixels in a block are excluded to estimate block complexity. In addition, the prediction-error is computed without considering the location relationship of the second largest and largest pixels or the second smallest and smallest pixels. To this end, an improved RDH technique is proposed in this paper to enhance the embedding performance. The improvement mainly lies in the following two aspects. First, some pixels in a block, which are excluded from data hiding in some existing RDH methods, are exploited together with the neighborhood surrounding this block to increase the estimation accuracy of local complexity. Second, the remaining pixels in a block, i.e., three largest and three smallest pixels are involved in data embedding. Taking three largest pixels for example, when the difference between the largest and third largest pixels is relatively large (e.g., > 1), we improve k-pass PVO by considering the location relationship of the second largest and largest pixels. The advantage of doing this is that the difference valued 3 between the maximum and the second largest pixel which is shifted in k-pass PVO, is able to carry 1 bit data in our method. In other words, a larger amount of pixels are able to carry data bits in our scheme compared with k-pass PVO. Abundant experimental results reveal that the proposed method achieves preferable embedding performance compared with the previous work, especially when a larger payload is required.
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Hollingsworth recently showed a posttest contrast for ANOVA situations that, for equal N, had several favorable qualities; the contrast is maximized so that if the overall F test were significant, the contrast would also be significant. The coefficients are chosen such that , which is said to help interpret the resulting contrast. However, for unequal N, the contrast suggested by Hollingsworth fails to achieve status as a maximized contrast; thus the contrast is not insured to be significant when the overall F test is significant, requiring separate testing of the contrast.
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In this paper, a new sub-pixel mapping algorithm is proposed based on sub-pixel/sub-pixel spatial attraction model (SSSAM). Different from the original sub-pixel/pixel spatial attraction model (SPSAM), the SSSAM considers the spatial distribution of each sub-pixel within neighbor pixels, when calculating the spatial attractions for sub-pixels within the centre pixel. Then the attractions are used to determine the class values of these sub-pixels. Two experiments on three artificial images and one real remote sensing image are processed. Both of the results show that compared with traditional SPSAM, the proposed method can produce sub-pixel mapping results with higher accuracy.
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Whether contrast adaptation may enhance contrast discrimination is a question that has remained largely unresolved because of conflicting empirical evidence. Greenlee and Heitger (1988), for example, reported that contrast discrimination may be enhanced after contrast adaptation, while Maattanen and Koenderink (1991) did not. This paper aimed to account for the different conclusions reached by these independent researchers by manipulations of key differences that exist between the two studies. It is shown that contrast discrimination may be enhanced after adaptation, but that these effects can vary markedly across subjects and test conditions. Enhancements in contrast discrimination are reported to be significant when adapting and testing at low levels of contrast, but just significant at higher levels of contrast. For high contrast signals; enhancements are shown to be independent of temporal frequency but dependent upon viewing conditions. Under binocular viewing conditions, enhancements in contrast discrimination thresholds are shown to be significantly higher than under monocular viewing conditions. It is suggested that the different conclusions reached by Greenlee and Heitger and by Maattanen and Koenderink may be explained by their respective differences in viewing conditions. The former study used binocular, while the latter study used monocular viewing with an occluding eyepatch.
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The aim of this paper is to rationalize the idea of constructing a contrast category as one of the semantic categories in Chinese Language,as well as to classify it from different perspectives.There are theoretical supports from cognitive psychology and linguistic that contrast as a semantic category in modern Chinese is the reflection of contrast as part of humankinds' cognitive mechanism.As a semantic category revises a certain relationship,contrast is characterized by highlighting difference.From different perspectives we can classify contrast category into different sub-categories as follows:marked contrast and unmarked contrast,antithetical contrast and non-antithetical contrast,two-thing contrast and two-profile contrast,linear contrast and non-linear contrast,overt contrast and implied contrast,unitary contrast and multiple contrast and etc.
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Although the number of pixels in image sensors is increasing exponentially, production techniques have only been able to linearly reduce the probability that a pixel will be defective. The result is a rapidly increasing probability that a sensor will contain one or more defective pixels. The defect pixel detection and defect pixel correction are operated separately but the former must employ before the latter is in use. Traditional detection scheme, which finds the defect pixels during manufacturing, is not able to discover the spread defect pixels years late. Consequently, the lifetime and robust defect pixel detection technique, which identifies the fault pixels when camera is in use, is more practical and developed. The paper presents a two stages dead pixel detection technique without complicated mathematic computations so that the embedded devices can easily implement it. Using six dead pixel types are tested and the experimental result indicates that it can be accelerated more than four times the detection time.
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