scholarly journals A stimulus artefact undermines the evidence for independent ON and OFF channels in stereopsis

2018 ◽  
Author(s):  
Jenny C. A. Read ◽  
Bruce G. Cumming
Keyword(s):  
Neural Network ◽  
Discrimination Task ◽  
Stereoscopic Vision ◽  
Early Vision ◽  
Linear Filters ◽  
Black And White ◽  
Stimulus Artefact ◽  
Mixed Polarity ◽  
Left And Right ◽  
Random Dot Pattern

AbstractEarly vision proceeds through distinct ON and OFF channels, which encode luminance increments and decrements respectively. It has been argued that these channels also contribute separately to stereoscopic vision. This is based on the fact that observers perform better on a noisy disparity discrimination task when the stimulus is a random-dot pattern consisting of equal numbers of black and white dots (a “mixed-polarity stimulus”, argued to activate both ON and OFF stereo channels), than when it consists of all-white or all-black dots (“same-polarity”, argued to activate only one). However, it is not clear how this theory can be reconciled with our current understanding of disparity encoding. Recently, a binocular convolutional neural network was able to replicate the mixed-polarity advantage shown by human observers, even though it was based on linear filters and contained no mechanisms which would respond separately to black or white dots. Here, we show that the stimuli used in all these experiments contain a subtle artefact. The interocular correlation between left and right images is actually lower for the same-polarity stimuli than for mixed-polarity stimuli with the same amount of disparity noise applied to the dots. Since our current theories suggest stereopsis is based on a correlation-like computation in primary visual cortex, it is then unsurprising that performance was better for the mixed-polarity stimuli. We conclude that there is currently no evidence supporting separate ON and OFF channels in stereopsis.

The Effect of Inducer Polarity and Contrast on the Perception of Illusory Figures

Perception ◽  
1997 ◽  
Vol 26 (11) ◽  
pp. 1431-1443 ◽  
Author(s):  
Nestor Matthews ◽  
Leslie Welch
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Form Perception ◽  
Illusory Figure ◽  
Contrast Level ◽  
Neural Network Models ◽  
Boundary Formation ◽  
Black And White ◽  
Mixed Polarity ◽  
Surface Lightness

A study designed to determine how inducer–surround contrast and inducer polarity affect the contour clarity and the lightness of illusory figures is reported. Using magnitude estimation procedures, ten naive subjects rated both the contour clarity and the lightness of Kanizsa squares. The magnitude of the inducer–surround contrast and the inducer polarity (all-black, all-white, or black-and-white) were varied randomly on each trial. The data indicate that contour clarity increases with contrast at the same rate across polarity conditions but that contour clarity at any given contrast level depends significantly on polarity. Contour clarity judgments were significantly lower when the inducers were all-white than when the inducers were all-black or black-and-white, and significantly greater in the ‘mixed’ polarity case (black-and-white inducers) than in the ‘same’ polarity case (the average of the all-black and all-white inducer conditions). Inducer contrast and polarity significantly affected the lightness of the illusory figure in a manner consistent with simultaneous spatial contrast. Also, for a given increment in contrast, contour clarity altered significantly more than surface lightness, regardless of inducer polarity. The findings suggest that the mechanism which mediates boundary formation is sensitive to the direction of contrast, and that the boundary formation mechanism is more sensitive than the surface lightness mechanism to changes in contrast magnitude. The results are considered within the context of neural network models of form perception.

scholarly journals Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2258
Author(s):  
Madhab Raj Joshi ◽  
Lewis Nkenyereye ◽  
Gyanendra Prasad Joshi ◽  
S. M. Riazul Islam ◽  
Mohammad Abdullah-Al-Wadud ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
User Study ◽  
Mean Squared Error ◽  
Color Image ◽  
Machine Learning Techniques ◽  
Global Features ◽  
Black And White ◽  
Historical Images ◽  
Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

scholarly journals Image Colorization

2019 ◽  
Author(s):  
Дарья Михалина ◽  
Daria Mikhalina ◽  
Александр Кузьменко ◽  
Aleksandr Kuz'menko ◽  
Константин Дергачев ◽  
...  
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Black And White ◽  
Number Of Layers ◽  
The Neural Network ◽  
Natural Landscapes ◽  
Video Card ◽  
Trained Neural Network ◽  
Lab Color Model ◽  
Cie Lab

The article discusses one of the latest ways to colorize a black and white image using deep learning methods. For colorization, a convolutional neural network with a large number of layers (Deep convolutional) is used, the architecture of which includes a ResNet model. This model was pre-trained on images of the ImageNet dataset. A neural network receives a black and white image and returns a colorized color. Since, due to the characteristics of ResNet, an input multiple of 255 is received, a program was written that, using frames, enlarges the image for the required size. During the operation of the neural network, the CIE Lab color model is used, which allows to separate the black and white component of the image from the color. For training the neural network, the Place 365 dataset was used, containing 365 different classes, such as animals, landscape elements, people, and so on. The training was carried out on the Nvidia GTX 1080 video card. The result was a trained neural network capable of colorizing images of any size and format. As example we had a speed of 0.08 seconds and an image of 256 by 256 pixels in size. In connection with the concept of the dataset used for training, the resulting model is focused on the recognition of natural landscapes and urban areas.

Detecting Eyes and Lips Using Neural Networks and SURF Features

2012 ◽  
pp. 338-354 ◽  
Author(s):  
Artem A. Lenskiy ◽  
Jong-Soo Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Success Rate ◽  
Detection Rate ◽  
Facial Feature ◽  
Speed Up ◽  
Surf Features ◽  
Left And Right ◽  
The Right

In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first neural network is applied to segment skin-colors and the second to detect facial features. As for input vectors, for the second network the authors apply speed-up robust features (SURF) that are not subject to scale and brightness variations. The authors carried out the detection of eyes and lips on two well-known facial feature databases, Caltech. and PICS. Caltech gave a success rate of 92.4% and 92.2% for left and right eyes and 85% for lips, whereas the PCIS database gave 96.9% and 95.3% for left and right eyes and 97.3% for lips. Using videos captured in real environment, among all videos, the authors achieved an average detection rate of 94.7% for the right eye and 95.5% for the left eye with a 86.9% rate for the lips

scholarly journals Psychological Features of Extreme Political Ideologies

2019 ◽  
Vol 28 (2) ◽  
pp. 159-163 ◽  
Author(s):  
Jan-Willem van Prooijen ◽  
André P. M. Krouwel
Keyword(s):  
Psychological Distress ◽  
Social World ◽  
Right Wing ◽  
Psychological Perspective ◽  
Black And White ◽  
Political Ideologies ◽  
The Social ◽  
Political Left ◽  
Left And Right ◽  
Psychological Features

In this article, we examine psychological features of extreme political ideologies. In what ways are political left- and right-wing extremists similar to one another and different from moderates? We propose and review four interrelated propositions that explain adherence to extreme political ideologies from a psychological perspective. We argue that (a) psychological distress stimulates adopting an extreme ideological outlook; (b) extreme ideologies are characterized by a relatively simplistic, black-and-white perception of the social world; (c) because of such mental simplicity, political extremists are overconfident in their judgments; and (d) political extremists are less tolerant of different groups and opinions than political moderates. In closing, we discuss how these psychological features of political extremists increase the likelihood of conflict among groups in society.

scholarly journals Outdoor Human Detection with Stereo Omnidirectional Cameras

2020 ◽  
Vol 32 (6) ◽  
pp. 1193-1199
Author(s):  
Shunya Tanaka ◽  
◽  
Yuki Inoue
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Object ◽  
Azimuth Angle ◽  
Human Detection ◽  
Target Person ◽  
Image Information ◽  
Stereo Camera ◽  
Omnidirectional Camera ◽  
Left And Right

An omnidirectional camera can simultaneously capture all-round (360°) environmental information as well as the azimuth angle of a target object or person. By configuring a stereo camera set with two omnidirectional cameras, we can easily determine the azimuth angle of a target object or person per camera on the image information captured by the left and right cameras. A target person in an image can be localized by using a region-based convolutional neural network and the distance measured by the parallax in the combined azimuth angles.

scholarly journals Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

2015 ◽  
Vol 5 (1) ◽  
pp. 92 ◽  
Author(s):  
A. B. M. Aowlad Hossain ◽  
Md. Wasiur Rahman ◽  
Manjurul Ahsan Riheen
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Hand Movement ◽  
Hand Movements ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Beta Band ◽  
Right Hand ◽  
Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features.  The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

scholarly journals Morphometric characterization of Black Bengal Goat under farming condition in Bangladesh

2018 ◽  
Vol 4 (1) ◽  
pp. 95-104 ◽  
Author(s):  
MA Jalil ◽  
MP Choudhury ◽  
MM Kabir ◽  
MA Habib
Keyword(s):  
Coat Color ◽  
Age Groups ◽  
Tail Length ◽  
Adult Males ◽  
Black And White ◽  
Left And Right ◽  
Black Bengal Goat ◽  
Morphometric Characterization ◽  
Average Adult

The study was undertaken to characterize of Black Bengal Goat (BBG) under farming condition. Data on the different parameters were collected during July 2006 to June 2013 at BLRI goat research farm. A total of 299 animals of different ages from birth to 36 months of both sexes were included. All type of measurements were taken when goats standing freely. All measurements were taken in metric unit. Data were analyzed by SPSS version 17.0 statistical computer program. In BLRI goat herd, four different types of coat color were observed in BBG i.e. Black, Black and white, Black and brown and completely white. Body length is higher in males than that of females for all generation and age group. Male goats had higher heart girth than that of female goats irrespective of age and generations. Average adult (>24 months age) body weight of male and female goats as 29.9±1.76 and 23.6±0.81 kg, respectively. Wither height was higher in adult males than females for same age. Left and right horn length in both sexes ranged from 3.6 to 13.2 cm. Female goats had higher ear length than male goats. Tail length of Black Bengal goats ranged from 8.0±0.52 to 11.1±0.43 cm and tail breadth ranged from 2.1±0.10 to 3.6±0.43 cm for different age and sex group. Average male foreleg length in >24 months of age possessed higher than that of female. Average udder length and breadth of BBGs were 7.5±0.24, 11.7±0.44 and 14.3±0.46 and 5.9±0.16, 7.4±0.28 and 7.8±0.23 cm, respectively for age groups 6-12, 12-24 and >24 months of age. The average testis length and breadth in adult males were 9.1±0.18, 7.5±0.48 and 9.8±0.49 and 6.8±0.13, 5.2±0.27 and 6.3±0.32, cm respectively for 6-12, 12-24 and >24 months of ages groups.Asian J. Med. Biol. Res. March 2018, 4(1): 95-104

scholarly journals Binocular disparity-based learning is retinotopically specific and independent of sleep

2020 ◽  
Vol 375 (1799) ◽  
pp. 20190463 ◽  
Author(s):  
Jens G. Klinzing ◽  
Lena Herbrik ◽  
Hendrikje Nienborg ◽  
Karsten Rauss
Keyword(s):  
Visual Field ◽  
Discrimination Task ◽  
Binocular Disparity ◽  
Human Subjects ◽  
Stereoscopic Vision ◽  
Memory Encoding ◽  
Retention Period ◽  
Low Level ◽  
Neuronal Ensemble ◽  
Ensemble Activity

Sleep supports the consolidation of recently encoded declarative and procedural memories. An important component of this effect is the repeated reactivation of neuronal ensemble activity elicited during memory encoding. For perceptual learning, however, sleep benefits have only been reported for specific tasks and it is not clear whether sleep targets low-level perceptual, higher-order temporal or attentional aspects of performance. Here, we employed a coarse binocular disparity discrimination task, known to rely on low-level stereoscopic vision. We show that human subjects improve over training and retain the same performance level across a 12-h retention period. Improvements do not generalize to other parts of the visual field and are unaffected by whether the retention period contains sleep or not. These results are compatible with the notion that behavioural improvements in binocular disparity discrimination do not additionally benefit from sleep when compared with the same time spent awake. We hypothesize that this might generalize to other strictly low-level perceptual tasks. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future'.

scholarly journals Neural architectures for stereo vision

2016 ◽  
Vol 371 (1697) ◽  
pp. 20150261 ◽  
Author(s):  
Andrew J. Parker ◽  
Jackson E. T. Smith ◽  
Kristine Krug
Keyword(s):  
Visual Cortex ◽  
Cortical Area ◽  
Binocular Disparity ◽  
Ocular Dominance ◽  
Three Dimensional ◽  
Stereoscopic Vision ◽  
Functional Modules ◽  
Repeat Distance ◽  
Left And Right ◽  
Cortical Architecture

Stereoscopic vision delivers a sense of depth based on binocular information but additionally acts as a mechanism for achieving correspondence between patterns arriving at the left and right eyes. We analyse quantitatively the cortical architecture for stereoscopic vision in two areas of macaque visual cortex. For primary visual cortex V1, the result is consistent with a module that is isotropic in cortical space with a diameter of at least 3 mm in surface extent. This implies that the module for stereo is larger than the repeat distance between ocular dominance columns in V1. By contrast, in the extrastriate cortical area V5/MT, which has a specialized architecture for stereo depth, the module for representation of stereo is about 1 mm in surface extent, so the representation of stereo in V5/MT is more compressed than V1 in terms of neural wiring of the neocortex. The surface extent estimated for stereo in V5/MT is consistent with measurements of its specialized domains for binocular disparity. Within V1, we suggest that long-range horizontal, anatomical connections form functional modules that serve both binocular and monocular pattern recognition: this common function may explain the distortion and disruption of monocular pattern vision observed in amblyopia. This article is part of the themed issue ‘Vision in our three-dimensional world’.

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