Social comparison and body image in non or infrequent exercisers and exercisers

Body image refers to an individual's internal representation ofhis/her outer self (Cash, 1994; Thompson, Heinberg, Altabe, & Tantleff-Dunn, 1999). It is a multidimensional construct which includes an individual's attitudes towards hislher own physical characteristics (Bane & McAuley, 1998; Cash, 1994; Cash, 2004; Davison & McCabe, 2005; Muth & Cash, 1997; Sabiston, Crocker, & Munroe-Chandler, 2005). Social comparison is the process of thinking about the self in relation to others in order to determine if one's opinions and abilities are adequate and to assess one's social status (Festinger, 1954; Wood, 1996). Research investigating the role of social comparisons on body image has provided some information on the types and nature of the comparisons that are made. The act of making social comparisons may have a negative impact on body image (van den Berg et ai., 2007). Although exercise may improve body image, the impact of social comparisons in exercise settings may be less positive, and there may be differences in the social comparison tendencies between non or infrequent exercisers and exercisers. The present study examined the nature of social comparisons that female collegeaged non or infrequent exercisers and exercisers made with respect to their bodies, and the relationship of these social comparisons to body image attitudes. Specifically, the frequency and direction of comparisons on specific tal-gets and body dimensions were examined in both non or infrequent exercisers and exercisers. Finally, the relationship between body-image attitudes and the frequency and direction with which body-related social comparisons were made for non or infrequent exercisers and exercisers were examined. One hundred and fifty-two participants completed the study (n = 70 non or ill infrequent exercisers; n = 82 exercisers). Participants completed measures of social physique anxiety (SPA), body dissatisfaction, body esteem, body image cognitions, leisure time physical activity, and social comparisons. Results suggested that both groups (non or infrequent exercisers and exercisers) generally made social comparisons and most frequently made comparisons with same-sex friends, and least frequently with same-sex parents. Also, both groups made more appearance-related comparisons than non-appearance-related comparisons. Further, both groups made more negative comparisons with almost all targets. However, non or infrequent exercisers generally made more negative comparisons on all body dimensions, while exercisers made negative comparisons only on weight and body shape dimensions. MANOV As were conducted to examine if any differences on social comparisons between the two groups existed. Results of the MANOVAs indicated that frequency of comparisons with targets, the frequency of comparisons on body dimensions, and direction of comparisons with targets did not differ based on exercise status. However, the direction of comparison of specific body dimensions revealed a significant (F (7, 144) = 3.26,p < .05; 1]2 = .132) difference based on exercise status. Follow-up ANOVAs showed significant differences on five variables: physical attractiveness (F (1, 150) = 6.33,p < .05; 1]2 = .041); fitness (F(l, 150) = 11.89,p < .05; 1]2 = .073); co-ordination (F(I, 150) = 5.61,p < .05; 1]2 = .036); strength (F(I, dO) = 12.83,p < .05; 1]2 = .079); muscle mass or tone (F(l, 150) = 17.34,p < .05; 1]2 = 1.04), with exercisers making more positive comparisons than non or infrequent exercisers. The results from the regression analyses for non or infrequent exercisers showed appearance orientation was a significant predictor of the frequency of social comparisons N (B = .429, SEB = .154, /3 = .312,p < .01). Also, trait body image measures accounted for significant variance in the direction of social comparisons (F(9, 57) = 13.43,p < .001, R2adj = .68). Specifically, SPA (B = -.583, SEB = .186, /3 = -.446,p < .01) and body esteem-weight concerns (B = .522, SEB = .207, /3 = .432,p < .01) were significant predictors of the direction of comparisons. For exercisers, regressions revealed that specific trait measures of body image significantly predicted the frequency of comparisons (F(9, 71) = 8.67,p < .001, R2adj = .463). Specifically, SPA (B = .508, SEB = .147, /3 = .497,p < .01) and appearance orientation (B = .457, SEB = .134, /3 = .335,p < .01) were significant predictors of the frequency of social comparisons. Lastly, for exercisers, the results for the regression of body image measures on the direction of social comparisons were also significant (F(9, 70) = 14.65,p < .001, R2adj = .609) with body dissatisfaction (B = .368, SEB = .143, /3 = .362,p < .05), appearan.ce orientation (B = .256, SEB = .123, /3 = .175,p < .05), and fitness orientation (B = .423, SEB = .194, /3 = .266,p < .05) significant predictors of the direction of social comparison. The results indicated that young women made frequent social comparisons regardless of exercise status. However, exercisers m,a de more positive comparisons on all the body dimensions than non or infrequent exercisers. Also, certain trait body image measures may be good predictors of one's body comp~son tendencies. However, the measures which predict comparison tendencies may be different for non or infrequent exercisers and exercisers. Future research should examine the effects of social comparisons in different populations (i.e., males, the obese, older adults, etc.). Implications for practice and research were discussed.

The Wolf and the Whale: aesthetic relationships between electroacoustic music and poetry inspired by the Canadian landscape

La version intégrale de cette thèse est disponible uniquement pour consultation individuelle à la Bibliothèque de musique de l’Université de Montréal (www.bib.umontreal.ca/MU).

Bio-chemical changes associated with processing of shell fishes and flavour constituents of body meat and claw meat'of crab

In this thesis all these aspects are taken into consideration. Extensive studies were conducted on all aspects of processing of crabs, mussels and clams. The species taken for studies are commercially used ones namely Scylla sereta, perna viridis, and villorita cyprinoids. In Chapter 4.1 with regard to crab) the following aspects on their handling and processing are reported seasonal variation of chemical constituents, changes taking place during ice storage, freezing, canning etc. In Chapter 4._2 with regard to mussel, the relation between age (size) and chemical constituents, changes taking place during ice storage, freezing, canning etc. are reported and in Chapter 4.3 the changes taking place in clam muscle during icing and freezing are reported and the ame rebility of ice stored clams for canning purpose is reported.The interference of high concentration of glycogen in mussel and clam muscles during the colour development of ribose (Me-jbaum's method) is observed and remedial step are taken to minimise the interference.

k-NN based On-Line Handwritten Character recognition system

On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting recognition of western, Chinese and Japanese scripts, but there are very few related to the recognition of Indic script such as Malayalam. This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using K-NN algorithm. It would help in recognizing Malayalam text entered using pen-like devices. A novel feature extraction method, a combination of time domain features and dynamic representation of writing direction along with its curvature is used for recognizing Malayalam characters. This writer independent system gives an excellent accuracy of 98.125% with recognition time of 15-30 milliseconds

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

Geometric and Algebraic Aspects of 3D Affine and Projective Structures from Perspective 2D Views

We investigate the differences --- conceptually and algorithmically --- between affine and projective frameworks for the tasks of visual recognition and reconstruction from perspective views. It is shown that an affine invariant exists between any view and a fixed view chosen as a reference view. This implies that for tasks for which a reference view can be chosen, such as in alignment schemes for visual recognition, projective invariants are not really necessary. We then use the affine invariant to derive new algebraic connections between perspective views. It is shown that three perspective views of an object are connected by certain algebraic functions of image coordinates alone (no structure or camera geometry needs to be involved).

The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Obect Classes

Most psychophysical studies of object recognition have focussed on the recognition and representation of individual objects subjects had previously explicitely been trained on. Correspondingly, modeling studies have often employed a 'grandmother'-type representation where the objects to be recognized were represented by individual units. However, objects in the natural world are commonly members of a class containing a number of visually similar objects, such as faces, for which physiology studies have provided support for a representation based on a sparse population code, which permits generalization from the learned exemplars to novel objects of that class. In this paper, we present results from psychophysical and modeling studies intended to investigate object recognition in natural ('continuous') object classes. In two experiments, subjects were trained to perform subordinate level discrimination in a continuous object class - images of computer-rendered cars - created using a 3D morphing system. By comparing the recognition performance of trained and untrained subjects we could estimate the effects of viewpoint-specific training and infer properties of the object class-specific representation learned as a result of training. We then compared the experimental findings to simulations, building on our recently presented HMAX model of object recognition in cortex, to investigate the computational properties of a population-based object class representation as outlined above. We find experimental evidence, supported by modeling results, that training builds a viewpoint- and class-specific representation that supplements a pre-existing repre-sentation with lower shape discriminability but possibly greater viewpoint invariance.

Role of color in face recognition

One of the key challenges in face perception lies in determining the contribution of different cues to face identification. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with grayscale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

Component based recognition of objects in an office environment

We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. In experiments we evaluate different sizes and types of components and three standard techniques for component selection. The component classifiers are finally compared to global classifiers on a database of four objects.

Evaluation of sets of oriented and non-oriented receptive fields as local descriptors

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. We propose a performance criterion for a local descriptor based on the tradeoff between selectivity and invariance. In this paper, we evaluate several local descriptors with respect to selectivity and invariance. The descriptors that we evaluated are Gaussian derivatives up to the third order, gray image patches, and Laplacian-based descriptors with either three scales or one scale filters. We compare selectivity and invariance to several affine changes such as rotation, scale, brightness, and viewpoint. Comparisons have been made keeping the dimensionality of the descriptors roughly constant. The overall results indicate a good performance by the descriptor based on a set of oriented Gaussian filters. It is interesting that oriented receptive fields similar to the Gaussian derivatives as well as receptive fields similar to the Laplacian are found in primate visual cortex.

Uncertainty Propagation in Model-Based Recognition

Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.

New medical registration and segmentation techniques using information-theoretic tools

El processament d'imatges mèdiques és una important àrea de recerca. El desenvolupament de noves tècniques que assisteixin i millorin la interpretació visual de les imatges de manera ràpida i precisa és fonamental en entorns clínics reals. La majoria de contribucions d'aquesta tesi són basades en Teoria de la Informació. Aquesta teoria tracta de la transmissió, l'emmagatzemament i el processament d'informació i és usada en camps tals com física, informàtica, matemàtica, estadística, biologia, gràfics per computador, etc. En aquesta tesi, es presenten nombroses eines basades en la Teoria de la Informació que milloren els mètodes existents en l'àrea del processament d'imatges, en particular en els camps del registre i la segmentació d'imatges. Finalment es presenten dues aplicacions especialitzades per l'assessorament mèdic que han estat desenvolupades en el marc d'aquesta tesi.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

Cross validation and segment support for stereo belief propagation

Typically, algorithms for generating stereo disparity maps have been developed to minimise the energy equation of a single image. This paper proposes a method for implementing cross validation in a belief propagation optimisation. When tested using the Middlebury online stereo evaluation, the cross validation improves upon the results of standard belief propagation. Furthermore, it has been shown that regions of homogeneous colour within the images can be used for enforcing the so-called "Segment Constraint". Developing from this, Segment Support is introduced to boost belief between pixels of the same image region and improve propagation into textureless regions.