Image Color Transfer Approach by Analogy with Taylor Expansion

2013 ◽  
Vol 2 (2) ◽  
pp. 43-54
Author(s):  
Hongbo Liu ◽  
Ye Ji ◽  
Aboul Ella Hassanien
Keyword(s):  
Time Series ◽  
Taylor Expansion ◽  
Image Features ◽  
Second Order ◽  
Image Feature ◽  
Order Information ◽  
Color Transfer ◽  
First Order ◽  
Occurrence Matrix ◽  
Image Expansion

The Taylor expansion has shown in many fields to be an extremely powerful tool. In this paper, the authors investigated image features and their relationships by analogy with Taylor expansion. The kind of expansion could be helpful for analyzing image feature and engraftment, such as transferring color between images. By analogy with Taylor expansion, the image color transfer algorithm is designed by the first and second-order information. The luminance histogram represents the first-order information of image, and the co-occurrence matrix represents the second-order information of image. Some results illustrate the proposed algorithm is effective. In this study, each polynomial in the Taylor analogy expansion of images is considered as one of image features which help in re-understanding images and its features. By using the proposed technique, the features of image, such as color, texture, dimension, time series, would be not isolated but mutual relational based on image expansion.

Correlation analysis of continuous emotional response to music: Correcting for the effects of serial correlation

2001 ◽  
Vol 5 (1_suppl) ◽  
pp. 213-236 ◽  
Author(s):  
Emery Schubert
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Emotional Response ◽  
Serial Correlation ◽  
Time Series Data ◽  
Second Order ◽  
Series Data ◽  
First Order ◽  
The Relationship ◽  
Pearson Correlations

Publications of research concerning continuous emotional responses to music are increasing. The developing interest brings with it a need to understand the problems associated with the analysis of time series data. This article investigates growing concern in the use of conventional Pearson correlations for comparing time series data. Using continuous data collected in response to selected pieces of music, with two emotional dimensions for each piece, two falsification studies were conducted. The first study consisted of a factor analysis of the individual responses using the original data set and its first-order differenced transformation. The differenced data aligned according to theoretical constraints better than the untransformed data, supporting the use of first-order difference transformations. Using a similar method, the second study specifically investigated the relationship between Pearson correlations, difference transformations and the critical correlation coefficient above which the conventional correlation analysis remains internally valid. A falsification table was formulated and quantified through a hypothesis index function. The study revealed that correlations of undifferenced data must be greater than 0.75 for a valid interpretation of the relationship between bivariate emotional response time series data. First and second-order transformations were also investigated and found to be valid for correlation coefficients as low as 0.24. Of the three version of the data (untransformed, first-order differenced, and second-order differenced), first-order differenced data produced the fewest problems with serial correlation, whilst remaining a simple and meaningful transformation.

scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
yateng bai ◽  
xiaoping ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals Prefrontal Cortex Activation Reflects Efficient Exploitation of Higher-order Statistical Structure

2016 ◽  
Vol 28 (12) ◽  
pp. 1909-1922 ◽  
Author(s):  
Christiane Ahlheim ◽  
Anne-Marike Schiffer ◽  
Ricarda I. Schubotz
Keyword(s):  
Action Observation ◽  
Second Order ◽  
Order Structure ◽  
Order Information ◽  
First Order ◽  
Action Observation Network ◽  
Observation Network ◽  
Statistical Regularities ◽  
Posterior Parietal ◽  
Action Step

Because everyday actions are statistically structured, knowing which action a person has just completed allows predicting the most likely next action step. Taking even more than the preceding action into account improves this predictability but also causes higher processing costs. Using fMRI, we investigated whether observers exploit second-order statistical regularities preferentially if information on possible upcoming actions provided by first-order regularities is insufficient. We hypothesized that anterior pFC balances whether or not second-order information should be exploited. Participants watched videos of actions that were structured by first- and second-order conditional probabilities. Information provided by the first and by the second order was manipulated independently. BOLD activity in the action observation network was more attenuated the more information on upcoming actions was provided by first-order structure, reflecting expectation suppression for more predictable actions. Activation in posterior parietal sites decreased further with second-order information but increased in temporal areas. As expected, second-order information was integrated more when less first-order information was provided, and this interaction was mediated by anterior pFC (BA 10). Observers spontaneously used both the present and the preceding action to predict the upcoming action, and integration of the preceding action was enhanced when the present action was uninformative.

scholarly journals Coal quality prediction based on multi-feature fusion of flotation foam images

2020 ◽  
Author(s):  
Yateng Bai ◽  
Xiaoping Ma
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Image Features ◽  
Image Feature ◽  
Coal Preparation ◽  
Coal Quality ◽  
Coal Flotation ◽  
Edge Operator ◽  
Occurrence Matrix

Abstract Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.

scholarly journals The price of being seen to be just: an intention signalling strategy for indirect reciprocity

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160694 ◽  
Author(s):  
Hiroki Tanaka ◽  
Hisashi Ohtsuki ◽  
Yohsuke Ohtsubo
Keyword(s):  
Evolutionarily Stable Strategy ◽  
Second Order ◽  
Order Information ◽  
Stable Strategy ◽  
Indirect Reciprocity ◽  
First Order ◽  
Good Reputation ◽  
Behavioural Experiments ◽  
Evolutionarily Stable ◽  
A Current

Cooperation among strangers is a marked characteristic of human sociality. One prominent evolutionary explanation for this form of human cooperation is indirect reciprocity, whereby each individual selectively helps people with a ‘good’ reputation, but not those with a ‘bad’ reputation. Some evolutionary analyses have underscored the importance of second-order reputation information (the reputation of a current partner's previous partner) for indirect reciprocity as it allows players to discriminate justified ‘good’ defectors, who selectively deny giving help to ‘bad’ partners, from unjustified ‘bad’ defectors. Nevertheless, it is not clear whether people in fact make use of second-order information in indirect reciprocity settings. As an alternative, we propose the intention signalling strategy, whereby defectors are given the option to abandon a resource as a means of expunging their ‘bad’ reputation. Our model deviates from traditional modelling approaches in the indirect reciprocity literature in a crucial way—we show that first-order information is sufficient to maintain cooperation if players are given an option to signal their intention. Importantly, our model is robust against invasion by both unconditionally cooperative and uncooperative strategies, a first step towards demonstrating its viability as an evolutionarily stable strategy. Furthermore, in two behavioural experiments, when participants were given the option to abandon a resource so as to mend a tarnished reputation, participants not only spontaneously began to use this option, they also interpreted others' use of this option as a signal of cooperative intent.

Evidence for Global Motion Interactions between First-Order and Second-Order Stimuli

Perception ◽  
1998 ◽  
Vol 27 (7) ◽  
pp. 761-767 ◽  
Author(s):  
George Mather ◽  
Linda Murdoch
Keyword(s):  
Motion Analysis ◽  
Visual Motion ◽  
Global Analysis ◽  
Image Features ◽  
Second Order ◽  
The Other ◽  
Global Motion ◽  
First Order ◽  
Motion Integration ◽  
Random Block

Recent research indicates that the early stages of visual-motion analysis involve two parallel neural pathways, one conveying information from luminance-defined (first-order) image features, the other conveying information from texture-defined (second-order) features. It is still not clear whether these two pathways converge during later stages of global motion integration. According to one account they remain segregated, and feed separate global analyses. In the alternative account, all responses feed a common stage of global analysis. Two perceptual phenomena are universally held to result from interactions between detector responses during global motion integration—direction repulsion and motion capture. We conducted two psychophysical experiments on these phenomena to test for segregation of first-order and second-order responses during integration. Stimuli contained two components, either two random-block patterns transparently drifting in different directions (repulsion measurements), or a drifting square-wave grating superimposed on an incoherent random-block pattern (capture measurements). Repulsion and capture effects were measured when both stimulus components were the same order, and when one component was first order and the other was second order. Both effects were obtained for all combinations of first-order and second-order patterns. Repulsion effects were stronger with first-order inducing patterns, and capture effects were stronger with second-order inducers. The presence of perceptual interactions regardless of stimulus order strongly suggests that responses in first-order and second-order pathways interact during global motion analysis.

scholarly journals Sensitivity of Image Features to Noise in Conventional and Respiratory-Gated PET/CT Images of Lung Cancer: Uncorrelated Noise Effects

2016 ◽  
Vol 16 (5) ◽  
pp. 595-608 ◽  
Author(s):  
Jasmine A. Oliver ◽  
Mikalai Budzevich ◽  
Dylan Hunt ◽  
Eduardo G. Moros ◽  
Kujtim Latifi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Image Features ◽  
Image Feature ◽  
Emission Tomography ◽  
Gray Level ◽  
Uncorrelated Noise ◽  
Computed Tomography Images ◽  
Positron Emission ◽  
Occurrence Matrix

The effect of noise on image features has yet to be studied in depth. Our objective was to explore how significantly image features are affected by the addition of uncorrelated noise to an image. The signal-to-noise ratio and noise power spectrum were calculated for a positron emission tomography/computed tomography scanner using a Ge-68 phantom. The conventional and respiratory-gated positron emission tomography/computed tomography images of 31 patients with lung cancer were retrospectively examined. Multiple sets of noise images were created for each original image by adding Gaussian noise of varying standard deviation equal to 2.5%, 4.0%, and 6.0% of the maximum intensity for positron emission tomography images and 10, 20, 50, 80, and 120 Hounsfield units for computed tomography images. Image features were extracted from all images, and percentage differences between the original image and the noise image feature values were calculated. These features were then categorized according to the noise sensitivity. The contour-dependent shape descriptors averaged below 4% difference in positron emission tomography and below 13% difference in computed tomography between noise and original images. Gray level size zone matrix features were the most sensitive to uncorrelated noise exhibiting average differences >200% for conventional and respiratory-gated images in computed tomography and 90% in positron emission tomography. Image feature differences increased as the noise level increased for shape, intensity, and gray-level co-occurrence matrix features in positron emission tomography and for gray-level co-occurrence matrix and gray-level size zone matrix features in conventional computed tomography. Investigators should be aware of the noise effects on image features.

Extraction of Image Features on Tooth Flank Pitting and Gluing

2013 ◽  
Vol 760-762 ◽  
pp. 1505-1509
Author(s):  
Peng Fei Cheng ◽  
Guang Hua Nie
Keyword(s):  
Gear Tooth ◽  
Morphological Characteristics ◽  
Image Features ◽  
Image Feature ◽  
Tooth Surface ◽  
Gray Level ◽  
Median Filtering ◽  
Image Feature Extraction ◽  
Occurrence Matrix ◽  
Tooth Flank

Tooth flank pitting and gluing are principal forms of gear defect. The purpose of this research is to extract the image feature of the gear in the different defects by means of image processing technology. Firstly, the image was carried out denoising processing by median filtering and segmentation processing by use of OSTU method. Then, the pixel area was extracted as a feature to distinguish normal gear, tooth surface pitting and gluing, the inertia was extracted as image feature to detect pitting and gluing by Gray level co-occurrence matrix, and the morphological characteristics of the image were extracted. Image feature extraction of different defect form will help to establish an effective image recognition model.

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