Loss Of Positional Information Research Articles

Predicting blur visual discomfort for natural scenes by the loss of positional information

The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment. Thus, since the best possible accuracy of the image patterns localization is indicated by the positional Fisher Information, it is argued that blur discomfort is strictly related to a loss of this information. Following this concept, a receptive field functional model is adopted to predict the visual discomfort. It is a complex-valued operator, orientation-selective both in the space domain and in the spatial frequency domain. Starting from the case of Gaussian blur, the analysis is extended to a generic type of blur by applying a positional Fisher Information equivalence criterion. Out-of-focus blur and astigmatic blur are presented as significant examples. The validity of the proposed model is verified by comparing its predictions with subjective ratings. The model fits linearly with the experiments reported in independent databases, based on different protocols and settings.

A System for the Detection of Polyphonic Sound on a University Campus Based on CapsNet-RNN

In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event from audio information, and the use of audio surveillance systems can greatly improve the robustness and reliability of event detection. In this paper, a novel system for the detection of polyphonic urban noise is proposed for on-campus audio surveillance. The system aggregates different acoustic features to improve the classification accuracy of urban noise. A combination model composed of a capsule neural network (CapsNet) and recurrent neural network (RNN) is employed as the classifier. CapsNet overcomes some limitations of convolutional neural networks (CNNs), such as the loss of position information after max-pooling, and the RNN mainly models the temporal dependency of context information. The combination of these networks further improves the accuracy and robustness of polyphonic sound events detection. Moreover, a monitoring platform is designed to visualize noise maps and acoustic event information. The deployment architecture of the system is used in real environments, and experiments were also conducted on two public datasets. The results demonstrate that the proposed method is superior to existing state-of-art methods for the polyphonic sound event detection task.

WideSegNeXt: Semantic Image Segmentation Using Wide Residual Network and NeXt Dilated Unit

Semantic segmentation is widely applied in autonomous driving, in robotic picking, and for medical purposes. Due to the breakthrough of deep learning in recent years, the fully convolutional network (FCN)-based method has become the de facto standard in semantic segmentation. However, the simple FCN has difficulty in capturing global context information, since the local receptive field is small. Furthermore, there is a problem of low image resolution because of the existence of the pooling layer. In this paper, we address the shortcomings of the FCN by proposing a new architecture called WideSegNeXt, which captures the image context on various spatial scales and is effective in identifying small objects. In addition, there is little loss of position information, since there are no pooling layers in the structure. The proposed method achieves a mean intersection over union (MIoU) of 72.5% and a global accuracy (GA) of 92.4% on the CamVid dataset and achieves higher performance than previous methods without additional input datasets.

Explicit Constructions of Two-Dimensional Reed-Solomon Codes in High Insertion and Deletion Noise Regime

Insertion and deletion (insdel for short) errors are synchronization errors in communication systems caused by the loss of positional information in the message. Reed-Solomon codes have gained a lot of interest due to its encoding simplicity, well structuredness and list-decoding capability in the classical setting. This interest also translates to the insdel metric setting, as the Guruswami-Sudan decoding algorithm can be utilized to provide a deletion correcting algorithm in the insdel metric. Nevertheless, there have been few studies on the insdel error-correcting capability of Reed-Solomon codes. Our main contributions in this article are explicit constructions of two families of 2-dimensional Reed-Solomon codes with insdel error-correcting capabilities asymptotically reaching those provided by the Singleton bound. The first construction gives a family of Reed-Solomon codes with insdel error-correcting capability asymptotic to its length. The second construction provides a family of Reed-Solomon codes with an exact insdel error-correcting capability up to its length. Both our constructions improve the previously known construction of 2-dimensional Reed-Solomon codes whose insdel error-correcting capability is only logarithmic on the code length.

Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

BackgroundWheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations.ResultsIn this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress.ConclusionsBeing able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

Loss of positional information when tracking multiple dots: The role of memory

Loss of positional information when tracking multiple dots: The role of memory

On the nature of perceptual limits in vision. A new look at lateral masking.

Studies of attention have generally assumed that the principal limit to visual perception involves the capacity to identify a stimulus. Analyses of responses in visual tasks, however, suggest that errors can arise from a loss of position information, as well as a loss of identity information, and that these two types of errors may be affected by different variables. Based on this, we have proposed that the difficult aspect of perception is not that of recognizing the form of the stimulus but rather determining its position relative to other items in the field. To demonstrate this, we conducted a series of experiments on lateral masking that showed that the failure to perceive a parafoveal stimulus surrounded by other items is due to two quite separate components: a loss of identity information induced by the immediately adjacent items and a loss of position information induced by both adjacent and non-adjacent items.

Amblyopic processing of positional information. Part I: Vernier acuity.

Psychometric functions for the recognition of vernier displacements have been measured in 6 strabismic amblyopes, 1 anisometropic amblyope, and 1 bilateral amblyope of unknown aetiology. In the non-amblyopic eyes of the 7 unilateral amblyopes the mean threshold vernier offset was about half of that of a group of 7 experienced normal observers. No correlation was found between vernier acuities and Snellen acuities in the abnormal eyes of squinters. These eyes also displayed massive distortions of horizontal spatial values causing perceptual distortions. In all but one of the amblyopic eyes tested hyperacuity virtually disappeared at the brief exposure duration of 50 ms. None of the observed effects could be predicted from the amblyopic contrast sensitivities, grating resolutions, or letter acuities. This suggests that the loss of positional information is an independent and most significant feature of amblyopic visual dysfunction.

The retention of incidentally acquired order information

After subjects studied word pairs, they were asked to recall the ordinal position of each pair in the list, both immediately after study and 10 min, 48 h, or 1 week later. On the immediate test, they could remember the positions of some of the pairs at the beginning and end of the list, but memory for pairs in the interior of the list was poor. Position information was lost over time. There was no evidence that associative recall depended on the availability of position information, or that the forgetting of item information depended upon the loss of position information.