The relationship of marital partnership status to husband/wife bargaining mode

The purposes of this research were, to see if 1) type of marital partnership status of couples influenced the husbands' and wives' bargaining mode in joint decision-making; 2) other context factors such as characteristics of the partners and features of their relationship influenced their bargaining mode; and 3) the same context factors related to husbands' bargaining mode as were associated with wives' bargaining mode. The context factors studied were education, marital commitment, perceptions of spouse's behavior during past conflict, degree of love and caring for the spouse, degree of religious devoutness, and locus of control in self, spouse, and fate. Using questionnaire and audiotaped interview data from 188 husbands and wives married to each other but analyzed separately, this research examined the ability of the context factors to discriminate between competitive and cooperative bargaining mode when couples were making decisions about issues concerning wife's own activities, money, and companionship. Hierarchical multiple discriminant analyses were performed forcing partnership status in at the first step and making the other context factors available for entry in stepwise fashion. Stepwise discriminant analyses were performed with the components of partnership status made available for entry separately with the other context variables

Application of the central weighted structural similarity index for the estimation of the face recognition accuracy

In the paper a novel method for the estimation of the face recognition accuracy based on the modified Structural Similarity is presented. A typical application of the Structural Similarity index is related to the full-reference objective image quality assessment. Growing popularity of this metric is caused not only by the fact of its relatively low computational complexity but also by its sensitivity to three common types of distortions: the loss of contrast, luminance distortions and the loss of correlation.Taking into account the output of the SSIM metric as the quality map with the resolution nearly the same as that of the input images, it is possible to use any two-dimensional central weighting function to control the level of importance of each image region. The approach proposed in this article is based on the usage of the Central Weighted SSIM index for the prediction of the face recognition accuracy using the images contaminated by several common types of distortions e.g. salt and pepper noise, lossy compression, filtration etc. The described method is based on the assumption that facial portraits are cropped and centered, which is true for almost all biometric systems. Finally, the results of face recognition by means of PCArc method has been used, as the state-of-the art in this domain. The experiments were conducted on the Olivetti Research Lab database [1]

2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy

2DLDA-based texture recognition in the aspect of objective image quality assessment

The image quality is a crucial property of each image when it comes to successful recognition. There are many methods of image quality assessment which use both objective and subjective measures. The most desirable situation is when we can evaluate the quality of an image prior to recognition.It is well known that most of classical objective image quality assessment methods, mainly based on the Mean Square Error, are poorly correlated with the way humans perceive the quality of digital images. Recently some new methods of full-reference image quality assessment have been proposed based on Singular Value Decomposition and Structural Similarity, especially useful for development of new image processing methods e.g. filtration or lossy compression.Despite the fact that full-reference metrics require the knowledge of original image to compute them their application in image recognition systems can be also useful. In the remote controlled systems where lossy compressed images are transferred using low bandwidth networks, the additional information related to the quality of transmitted image can be helpful for the estimation of recognition accuracy or even the choice of recognition method.The paper presents a problem of recognizing visual textures using two-dimensional Linear Discriminant Analysis. The image features are taken from the FFT spectrum of gray-scale image and then rendered into a feature matrix using LDA. The final part of recognition is performed using distance calculation from the centers of classes. The experiments employ standard benchmark database - Brodatz Textures.Performed investigations are focused on the influence of image quality on the recognition performance and the correlation between image quality metrics and the recognition accuracy

A Study on the Current and Future Impacts of the Political System on Kerala’s Tourism Sector

Tourism is one of the predominant sectors of the economy of Kerala. Tourism in Kerala attracts millions of foreign and domestic visitors and garners revenue that totals to around 20,000 crores.Generating over a million employment opportunities and supporting the skilled and non-skilled labour equally, the tourism and hospitality industry is an inevitable part of the economy. Kerala is a preferred destination for its luxurious Ayurveda spas, backwater resorts, exquisite home stays, hill top bungalows and beach properties

Self-assessed Contrast-Maximizing Adaptive Region Growing

In the context of an experimental virtual-reality surgical planning software platform, we propose a fully self-assessed adaptive region growing segmentation algorithm. Our method successfully delineates main tissues relevant to head and neck reconstructive surgery, such as skin, fat, muscle/organs, and bone. We rely on a standardized and self-assessed region-based approach to deal with a great variety of imaging conditions with minimal user intervention, as only a single-seed selection stage is required. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing regions. Validation based on synthetic images, as well as truly-delineated real CT volumes, is provided for the reader’s evaluation

The Relationship Between Anger Control Problems and Neuropsychological Deficits in Individuals Who Have Sustained a Head Injury.

The present study assessed whether neuropsychological tests could be used to discriminate between groups of CHI individuals with closed head injuries (CHI): those with anger control problems, and those without. The study also assessed whether these groups differed on tests which assess various aspects of neuropsychological functioning; intelligence, memory skills, language functioning, concept formation and set shifting skills, and psychomotor performance. Forty two individuals with CHI were given neuropsychological tests after assignment to one of two groups: problematic anger (P) or nonproblematic anger (NP). Group assignments were made on the basis of information obtained during the course of: (1) a structured interview with the patient and family member; (2) reports from the patient\u27s physician; (3) the patient\u27s score on the Novaco Anger Inventory. Injury severity was estimated from information obtained from the interview and medical records. The groups differed significantly on factors such as educational level (p \u3c .05), injury severity (p \u3c .05), sex ratio (p \u3c .004), and FSIQ (p \u3c .001). The P group (N = 22) was predominantly male, more severely injured, and of lower intelligence as compared to the NP group (N = 20). The groups did not differ on age, time since injury, handedness, or race. These groups differed in memory skills (p \u3c .002), and language functioning (p \u3c .001), with the P group consistently performing at a lower level (MANOVA analysis). After education was used as a covariate (MANCOVA), the P group continued to show relative deficits on measures of memory (p \u3c .015) and language functioning (p \u3c .001). Discriminant analyses indicated that neuropsychological tests discriminate between these two groups (overall classification rate = 79%). The P group could be discriminated from the NP group on the basis of test performance in the following areas: intelligence (p \u3c .0009), memory skills (p \u3c .0009), concept formation and set shifting skills (p \u3c .01), and language functioning (p \u3c .0001). This study suggests that CHI individuals with anger control problems have sustained more severe injuries, are more likely to be male, and have greater neuropsychological deficits as compared to CHI individuals without anger control problems. Further research is needed to ascertain whether individuals with anger management problems are more likely to be of below average intelligence on a premorbid basis and if remediation of the pattern of neuropsychological deficits identified may decrease anger control problems

Entropy-based Iterative Face Classification

Abstract. This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropybased measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem