Human Visual Ability Research Articles

Examining the impact of different LED road stud layouts on driving performance and gaze behaviour at night-time

The risks associated with night-time driving on dimly lit roads are substantial and are attributable to the limitations of human visual abilities. As a result, drivers often struggle to distinguish road geometry from ordinary road markings, thus increasing the likelihood of mistakes. These circumstances contribute to a 60 % higher likelihood of road crashes compared to daytime conditions. To mitigate these risks, active LED road studs, which previous studies have shown to have a positive influence on driving performance, can be used. However, there remains a gap in research regarding the optimal arrangement of these studs along road markings for an improvement in driver behaviour and traffic safety. In this study, we assessed the influence of five different LED road stud layouts (unlit, edge, centre, edge-centre, and lane) during night-time driving on two-lane rural highways with curves of different radii (120, 210, 300, 440 m) and directions (left, right). Following a within subject design, thirty-five participants drove in a simulator along a road track with 8 spiralled curves (4 radii × 2 directions) linked to straights. We monitored the longitudinal (i.e., speed), transversal (i.e., lateral position and standard deviation of lateral position) and gaze behaviours.Our findings indicate that the presence of LED road studs promotes safer driving, by helping drivers to adjust their speed when negotiating curves. Transversal behaviour analysis revealed layout-dependent effects on lateral position. The presence of road studs both at the lane centreline and edge allows drivers to maintain centred trajectories and improve steering control. Gaze behaviour analysis uncovered interesting patterns, demonstrating a strong correlation between road stud layout and the driver's focus on specific road targets. Illuminated markings prompt drivers to concentrate their gaze on distinct points, subsequently altering their transversal behaviour.

Data Security using Variant of Hill Cipher

Abstract: Data is first collected, and the Pixel Repetition Technique is used to perform pre-processing, Payload capacity, attack protection, and visual quality. To rise the security of information concealing, The LSB information hiding algorithm of data using secret key has been presented to strengthen the security of information concealing. It increases human visual abilities, integrates information concealment and cryptography, and uses identity identification based on digital signature and encryption technology. It is based on a scheme where the receiver can retrieve secret data by applying the A* algorithm backwards. Last but not least, we tested safety and peak signal-to-noise ratio (PSNR). With stronger security and higher PSNR, the enhanced LSB data steganography technique using encryption technology is superior to the standard LSB data steganography method

Texture-like representation of objects in human visual cortex

The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of the visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real-world objects, that is, by representing visual textures. To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal to noise, as all observer models could predict human performance in image categorization tasks. How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for natural object discrimination is available. Thus, our results suggest that the role of the human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.

Penerapan Machine Learning Untuk Mengategorikan Sampah Plastik Rumah Tangga

Plastic waste is the result of human needs for plastic-based products. Due to high needs, the manufacture and use of plastics as raw materials produced do not use plastics as raw materials for their manufacture. Side effects of this phenomenon cause plastic waste to be transferred to the Final Shelter (TPA). Plastic waste is divided into 7 types, some can be replaced, recycled, and reused. To be able to do better sorting and screening of plastic waste, human labor is needed. This causes humans to have the ability to sort and filter visual objects, but humans are less consistent in various influencing factors. However, if this is applied to a computing system, the plastic waste treatment system will provide consistent results. If this system is applied in various layers of waste treatment, the plastic waste that pollutes the environment will be reduced. With the help of Machine Learning and Deep Learning,we can apply human visual abilities to computer systems.

Metode single image-NDVI untuk deteksi dini gejala mosaik pada Capsicum annuum

Single image-NDVI method for early detection of mosaic symptoms in Capsicum annuum
 Mosaics are a symptom of a disease often found in red chilies (Capsicum annuum) and is generally caused by viral infections such as the Tobacco mosaic virus. Severe infection can cause stunting and significant yield loss. Serological and molecular detection is a common detection method for plant viruses although they are time-consuming, relatively inefficient for large samples, and are destructive to plants. On the other hand, direct symptoms observation is hampered by human visual abilities and latent symptoms in virus infection. Therefore, detection method based on the plant’s ability to absorb and reflect various spectrums of sunlight, such as the normalized difference vegetation index (NDVI), has the potential to be developed. This study aims to evaluate the potential of a single image-NDVI as an NDVI variant for the early detection of mosaic symptoms in red chilies. The main activity involved image recording of chili plants that were not inoculated (V0) and inoculated (V1) by the virus, and given minimal nutrients (M) using an unmodified RGB camera and lens filter to capture blue and Near-Infrared light reflection. Furthermore, image processing is carried out using the Photo Monitoring plugin on the Fiji-ImageJ application. The recording was done one day after inoculation (dai) until the symptoms were visible. The results showed that there was an increasing trend in the integrated NDVI value in all treatments. Howewer, the increasing trend in V1 was not significant compared to V0 and M. The difference in the mean value of integrated NDVI between V1 was very significant compared to V0 (at 5 dai) and M (at 1 dai). This method’s level of sensitivity, specificity, and accuracy ranges from 80–90% at 5 dai.

Hoverflies are imperfect mimics of wasp colouration

Many Batesian mimics are considered to be inaccurate copies of their models, including a number of hoverfly species which appear to be poor mimics of bees and wasps. This inaccuracy is surprising since more similar mimics are expected to deceive predators more frequently and therefore have greater survival. One suggested explanation is that mimics which appear inaccurate to human eyes may be perceived differently by birds, the probable agents of selection. For example, if patterns contain an ultra-violet (UV) component, this would be visible to birds but overlooked by humans. So far, indirect comparisons have been made using human and bird responses to mimetic stimuli, but direct colour measurements of mimetic hoverflies are lacking. We took spectral readings from a wide range of hoverfly and wasp patterns. They show very low reflectance in the UV range, and do not display any human-invisible colour boundaries. We modelled how the recorded spectra would be perceived by both birds and humans. While colour differences between wasps and hoverflies are slightly more distinct according to human visual abilities, bird vision is capable of discriminating the two taxa in almost all cases. We discuss a number of factors that might make the discrimination task more challenging for a predator in the field, which could explain the apparent lack of selection for accurate colour mimicry.

Role of Computer Vision in Automatic Inspection Systems

Computer vision is the process of acquisition, processing and analysis of real world scenes in order to take different decisions based on applications. Computer vision systems are electronic replacement of human visual ability. Applications of computer vision systems are wide. This paper reviews the progress of computer vision in various fields of automatic inspection systems. Since conventional defect detection systems are prone to many disadvantages, computer vision with its efficiency and cost effectiveness is emerging as the latest revolution of technology. At the end of this paper, some advantages and disadvantages of computer vision and a suitable area and method for further research are also discussed.

Large-scale learning for media understanding

1 Editorial The remarkable growth in computational power over the last decade has enabled important advances in machine learning, allowing us to achieve impressive results across all areas of image and video processing. Numerical methods that were once thought to be intractable are now commonly deployed to solve problems as diverse as 3D modeling from 2D data (photo tourism [1]), object recognition (logo detection [2]), human biometrics (face recognition [3]), and video surveillance (automatic threat detection [4]). Despite this tremendous progress, there are many open questions related to understanding visual data—we are still far from matching human visual ability in all of these areas. It is fair to scrutinize this observation in some detail— where are researchers and current methodologies falling short? From our perspective, the practicalities of realworld problems are often obscured by the mischaracterization of good results in limited contexts, theoretical frameworks built around artificial problems, and a deep sea of technical minutiae. Assumptions are a necessary component to problem solving, but poor ones lead our algorithms and subsequent analyses astray. Similarly, good theory is important, but we should not lose sight of the fundamental problemwe are trying to solve by abstracting it away. Such circumstances occur more often than one might think. How should a researcher charged with the design of an algorithm that must actually work outside of the laboratory avoid these dilemmas? Perhaps unsurprisingly, we, as academics, are typically more critical than constructive when evaluating newwork—this is especially true of paper reviews and survey articles. In response to this, we submit the following researcher’s guide to everyday machine learning as a gentle nudge forward:

Visual cryptograms of random grids for threshold access structures

Based on a new model of visual cryptograms of random grids (VCRG), we design novel algorithms to generate a set of threshold (k,n)-VCRG for sharing a secret image P among n participants in such a way that any group of k out of the n encrypted transparencies reveals P to our eyes when superimposed, while any group of less than k transparencies obtains nothing about P. Just like conventional visual cryptographic schemes (VCSs), our designs require none of computing devices but merely human visual ability in the decryption process. Yet, our VCRG approach is much simpler and does not need any extra pixel expansion which is inevitable (actually 1/2k−1 for n=k and even larger for n>k) in VCSs. The correctness of our algorithms is formally proved and experimentally demonstrated. The light contrast in our best algorithm is cautiously analyzed and shown to be as effective as that in a quality threshold VCS when k shares are superimposed. With theoretic and practical interests, our VCRG model exposes new possibilities to the researches of visual secret sharing.

Perancangan Display Visual Kuantitatif Pada Sistem Manusia Mesin

Work system consist of man and machine system, human senses need a tool to respons all kind of stimulus such an information given by machine or outside of system, so that human cancontrol machine well. It means that we need display design based on the characteristic of human sense asbility in catching informationThere are many kind of display that can be made depend with information characteristicsThis research will give quantitative visual display design fixed to information characteristic givenand also consider with human visual ability accepting an information from outside

What Horses and Humans See: A Comparative Review

Adaptations of the mammalian eye have tailored each to its own particular ecological niche. On the one hand, it would appear that the horse is best served by a system that can keep “half an eye” on everything, while the human benefits from focussing on more specific aspects of the visual array. By adapting a range of techniques, originally used to assess human visual ability, it has been possible to compare the human visual experience with that of the horse. In general, the results of the majority of these comparative studies indicate that the visual capabilities of the horse are broadly inferior to the human equivalents in acuity, accommodation, and colour vision. However, both the horse and human abilities to judge distance and depth perception may be quite comparable while equine vision is certainly superior to that of human's under scotopic conditions. Individual variation in visual ability, which is routinely taken for granted in humans, is also likely to occur in the horse. Such variation would undoubtedly affect equine performance, particularly in terms of expectation of athletic competitive outcomes in modern equitation. In addition to such considerations as conformation and athletic ability, a detailed assessment of the visual ability might contribute to a more accurate prediction of future performance characteristics in the horse. Although further investigation is required in order to appreciate fully both the capabilities and limitations of the equine visual system, the information currently available should now be considered and applied more rigorously both in the design of the equine environment and in the implementation of contemporary equine training methods. This need is the greatest in areas of equestrian sport where the outcomes of either or both equine and human visual judgements can be critical, the cost of failure often high and occasionally results in fatal consequences for both parties of the horse-human dyad.

Re-evaluating split-fovea processing in word recognition: A critical assessment of recent research

In recent years, some researchers have proposed that a fundamental component of the word recognition process is that each fovea is divided precisely at its vertical midline and that information either side of this midline projects to different, contralateral hemispheres. Thus, when a word is fixated, all letters to the left of the point of fixation project only to the right hemisphere whereas all letters to the right of the point of fixation project only to the left hemisphere. An informed assessment of research in this area requires an accurate understanding of the nature of the evidence and arguments that have been used to develop this "split-fovea theory" of word recognition (SFT). The purpose of this article is to facilitate this understanding by assessing recent published support for SFT. In particular, we assess (i) the precision with which experiments have been conducted, (ii) the assumptions made about human visual ability, and (iii) the accuracy with which earlier research has been reported. The assessment reveals shortcomings and errors that are likely to impact on an accurate understanding of research in this area and, therefore, on an accurate understanding of the viability of SFT.

Testing Human Visual Detection with Xenon and Halogen Lamps as Used on Forest Machines

The workspace around machines in forest operations is commonly illuminated by either halogen lamps or xenon (HID) lamps. Informal claims have been made that there is a large difference in subjective experience between using halogen lamps and xenon lamps. To obtain an objective quantification, human visual abilities were measured when illumination was provided by either xenon or halogen lamps with the same physical illumination as measured in Lux. Performance was measured as the number of correct identifications of test figures which depended on the color of test displays used, but the difference between lamp types was small. Overall, when differences could be measured between lamp conditions, somewhat better performance was obtained with the xenon lamps than with the halogen lamps. Furthermore, xenon lamps required much less energy: three halogen lamps (3 • 70 W) had an illuminance equal to one xenon lamp (35 W).

A New Model of Computation for Learning Vision Modules from Examples

This paper addresses an important class of mimicry problems, where the goal is to construct a computer program which is functionally equivalent to an observed behaviour. Computer vision research can be considered such a challenge, where a researcher attempts to impart human visual abilities to a computer. Unfortunately this has proved a difficult task, not least because our vision processes occur mostly at a subconscious level. It is therefore useful to study the general mimicry problem in order to develop tools which may assist computer vision research. This paper formalises a mimicry problem as one in which a computer learning system (L) constructs a solution from a given program structure (i.e. template or outline) by posing questions to an Oracle. The latter is an entity which, when given an input value, produces the corresponding output of the function which is to be mimicked. In order to define a program‘s structure, particularly one which can be extracted from any computer program automatically, a new model of computation is developed. Based on this a fast algorithm which determines the best questions to pose to the Oracle is then described. Thus L relieves the human programmer of the difficulties faced in choosing the examples from which to learn. This is important because a human programmer might inadvertently choose biased, redundant or otherwise unhelpful examples. Results are shown which demonstrate the utility of a complete learning system (L) based on this work. This paper represents background theory and initial algorithms which further work will extend into powerful automatic learning systems, examples of which are found in [36] and [38].