“REPRESENTASI KEBERANIAN DALAM IKLAN SUSU BONEETO” ( Study Semiotika Representasi Keberanian pada Iklan susu Boneeto di Media Televisi )

The rapid development of technology to improve the flow of information and telecommunications as well as increased knowledge and level of public awareness of the importance of an information enables people affected by a variety of information at any time. As effective communication, effective advertising should be able to build a perception of the consumer society as wanted setup and advertisers. The purpose of this study was to determine the representation for bravery in Boneeto milk advertising Data analysis in this study is based on the sign / mark system that looks at the scene that showed the courage that appears on Boneto milk advertising. Then be analyzed using a model of semiotics put forward by John Fiske, by cutting the image of each scene that has relevance to the scene that showed the courage that appear in milk ads Boneto. The analysis is divided into levels of reality, and the level of representation. From the analysis we can conclude these ads will be full of courage on the charge of self representation model that can be seen from the activities that are challenging and full of risks undertaken as a plaything hanging on in the garden, pulled up to a high place, and tied the hands and feet and drawn on both. Models shown in advertisements of children who actively play or scene activity that triggers the adrenaline inside

Representasi Kasus Korupsi Akil Mochtar dalam Karikatur pada Headline Harian Pagi Riau Pos (Analisis Semiotik Charles Sanders Peirce)

Chairman of the Constitutional Court Akil Mochtar caught red-handed by the Anti-Corruption Commission while accepting bribes related to disputed local elections in several regions in Indonesia. This case is the most horrendous events in the history of fighting corruption and the arrest of the highest state officials have ever done KPK. The judges Jakarta Corruption Court finally decided life sentence due to Akil Mochtar found guilty of corruption and money laundering related to the handling of election disputes in the Constitutional Court. In reporting cases of corruption Akil Mochtar, Daily Morning Post Riau uses caricature to package this information in the headline. As one visual communication, a caricature is an interpretive picture that uses symbols to convey a message quickly and concisely, or something attitude toward people, situations, or specific events. The purpose of this study to determine how the corruption cases Akil Mochtar Representation in caricature on Headline Riau Pos daily morning by looking at the signs contained in caricature.This study used a qualitative descriptive method with semiotic analysis of meaning Charles Sanders Peirce elements Sign, object, and interpretant. Number of caricatures in the analysis of 3 pieces caricature of corruption headline Akil Mochtar edition October 2013 to July 2014 Data was collected using interviews, documentation, and literature.In this study the authors find meaning in the third image analyzing this caricature, sure: First, Abraham Samad Sign dressed in Roman warriors Object Abraham Samad has the courage to solve cases of corruption without fear and trembling interpretant Abraham Samad managed to catch the perpetrators of corruption that exist in Indonesia. Secondly, Sign Akil Mochtar, who is always asking for money in a local election dispute Object Akil Mochtar has damaged public confidence in the Indonesian Constitutional Court interpretant All winning regional head for money in the Constitutional Court. Third, Akil Mochtar Sign lay helpless and bound iron chain in Object Akil Mochtar verdict of life imprisonment and can not do anything else interpretant sentence of life imprisonment was proper because it has damaged the image of the Constitutional Court which had long been believed.Key word : Representation, Corruption Cases, Caricatur

Drosophila type IV collagen mutation associates with immune system activation and intestinal dysfunction

This thesis introduces and explores a new type of representation for low and medium level vision operations called channel representation. The channel representation is a more general way to represent information than e.g. as numerical values, since it allows incorporation of uncertainty, and simultaneous representation of several hypotheses. More importantly it also allows the representation of “no information” when no statement can be given. A channel representation of a scalar value is a vector of channel values, which are generated by passing the original scalar value through a set of kernel functions. The resultant representation is sparse and monopolar. The word sparse signifies that information is not necessarily present in all channels. On the contrary, most channel values will be zero. The word monopolar signifies that all channel values have the same sign, e.g. they are either positive or zero. A zero channel value denotes “no information”, and for non-zero values, the magnitude signifies the relevance. In the thesis, a framework for channel encoding and local decoding of scalar values is presented. Averaging in the channel representation is identified as a regularised sampling of a probability density function. A subsequent decoding is thus a mode estimation technique.' The mode estimation property of channel averaging is exploited in the channel smoothing technique for image noise removal. We introduce an improvement to channel smoothing, called alpha synthesis, which deals with the problem of jagged edges present in the original method. Channel smoothing with alpha synthesis is compared to mean-shift filtering, bilateral filtering, median filtering, and normalized averaging with favourable results. A fast and robust blob-feature extraction method for vector fields is developed. The method is also extended to cluster constant slopes instead of constant regions. The method is intended for view-based object recognition and wide baseline matching. It is demonstrated on a wide baseline matching problem. A sparse scale-space representation of lines and edges is implemented and described. The representation keeps line and edge statements separate, and ensures that they are localised by inhibition from coarser scales. The result is however still locally continuous, in contrast to non-max-suppression approaches, which introduce a binary threshold. The channel representation is well suited to learning, which is demonstrated by applying it in an associative network. An analysis of representational properties of associative networks using the channel representation is made. Finally, a reactive system design using the channel representation is proposed. The system is similar in idea to recursive Bayesian techniques using particle filters, but the present formulation allows learning using the associative networks

Zalihost i neoznačeno u hiperrealizmu

Realism is a stylistic qualification within the discipline of art history. The philosophy of art in the late 1960s has undermined such an understanding in two ways: first by relativising the notion of realism and then introducing the unstable status of the work of art into the place of previously stable aesthetic qualifications. In the same period, hyperrealism emerged as a postmodern version of realism. Using the visual language of his time, hyperrealism goes along with the innovations of philosophy of language and analytical philosophy, exposing semiosis as a production of signs devoid of meaning and ambivalently positioning itself in the aesthetics of hyper-consumerism and hyperproductiveness of postmodern culture. Unlike the realism that has been the answer to the universal, general state of the world, hyperrealism has imposed itself as a peeled off mirror image of the fragment, as a world without context. While in paintings of historical realism the represented scene referred to a context outside of the picture, in hyperrealistic practices the depicted scene was limited to the representation of a frozen event, isolated from context, and therefore from what has been signified by the world. From the theoretical position of philosophy of art, in this article we want to present the thesis of the “redundancy of unsignified content in hyperrealism”: first by presenting the relativisation of realism in Nelson Goodman, and then separating the general from aesthetic meaning of sign information in Max Bense. For both authors, representation is a matter of choice, and every sign shown is information. However, while for Goodman realism is a matter of the recipient‘s habit of understanding the sign, information and semantic structure of the image, for Bense the key is to distinguish life (realism) from aesthetic sign communication. In the second part, we will apply Bense‘s distinction further to the differentiation between realism and hyperrealism

Zalihost i neoznačeno u hiperrealizmu

Realism is a stylistic qualification within the discipline of art history. The philosophy of art in the late 1960s has undermined such an understanding in two ways: first by relativising the notion of realism and then introducing the unstable status of the work of art into the place of previously stable aesthetic qualifications. In the same period, hyperrealism emerged as a postmodern version of realism. Using the visual language of his time, hyperrealism goes along with the innovations of philosophy of language and analytical philosophy, exposing semiosis as a production of signs devoid of meaning and ambivalently positioning itself in the aesthetics of hyper-consumerism and hyperproductiveness of postmodern culture. Unlike the realism that has been the answer to the universal, general state of the world, hyperrealism has imposed itself as a peeled off mirror image of the fragment, as a world without context. While in paintings of historical realism the represented scene referred to a context outside of the picture, in hyperrealistic practices the depicted scene was limited to the representation of a frozen event, isolated from context, and therefore from what has been signified by the world. From the theoretical position of philosophy of art, in this article we want to present the thesis of the “redundancy of unsignified content in hyperrealism”: first by presenting the relativisation of realism in Nelson Goodman, and then separating the general from aesthetic meaning of sign information in Max Bense. For both authors, representation is a matter of choice, and every sign shown is information. However, while for Goodman realism is a matter of the recipient‘s habit of understanding the sign, information and semantic structure of the image, for Bense the key is to distinguish life (realism) from aesthetic sign communication. In the second part, we will apply Bense‘s distinction further to the differentiation between realism and hyperrealism

REPRESENTASI MOTIF POLENG PADA DESAIN KEMASAN ‘AWANI’ (KAJIAN SEMIOTIKA)

Packaging as a medium of communication and information products is characterized by the presence of visual elements of packaging that is capable to build the image of the product in the minds of target consumers. Packaging of visual element consists a logo as the brand and PDP (Main Display Panel) consists of typefaces, colors, shapes, and layout (layout). Communication and information the product presented through packaging visual elements can be creating identity of product in the minds of consumers. Design packaging serves to differentiate one product with another similar product on the market. 'Awani' is a brand of culinary product is offered as typical Balinese souvenirs to tourists. Packaging using the motif Poleng as visual appeal of the packaging elements. This motif is used as a tool to communicate the image of the product 'Awani' in the minds of target consumers. The purpose of this study was to determine the forms of representation of motive poleng as the Awani’s creative packaging design concept and, to know the implied meaning of representation Poleng motif on Awani’s packaging design. The first step is to classify and identify the visual elements in packaging design and then analyze the forms of representation poleng motif in the visual elements of ‘Awani’ packaging. The next stage of analyzing the meaning representation Poleng motif is using qualitative descriptive methods with interpretive semiotic approach. Semiotic approach is to examine the text for the sign and its meaning interpreted through code (decoding) and the sign behind the text. Contribution of this study is to provide theoretical knowledge about the relationship between design concepts in the determination of visual elements in forming the image of product packaging. So it can be a reference for the small industrial sector in communicating the product's image through the minds of the target consumer packaging design

Computational Models of Visual Hyperacuity

The process of visual hyperacuity is described and analyzed in the terms of informative theory. It is shown that in principle, the detection and representation of both luminance and edge features can be performed with a precision commensurate with human abilities. Algorithms are formulated in accord with the different representational methods, and are implemented as distinct computer models, which are tested with vernier acuity tasks. The results indicate that edge information, encoded either in the manner proposed by Marr and his col1eagucs (as zero-crossings in the Laplacian of a Gaussian convolved with the image) or when encoded as a simple filtered difference allows finer spatial localization than does the centroid of the intensity distribution. In particular it is shown that to judge changes of relative positions with a precision of 0.1 sec arc in two and three dimensions, it is sufficient to represent the displacement of an edge by the difference of two Laplacian-Gaussian filters rather than by the difference between interpolated zero-crossings in them. This method entails no loss of relative position information (sign), allows recovery of the magnitude of the change, and provides significant economies of computation

Perceptual Generative Adversarial Networks for Small Object Detection

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts

Cross-convolutional-layer Pooling for Image Recognition

Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive convolutional layers: one layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of convolutional layers, we further develop two schemes to realize this idea. The first one directly uses convolutional layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as convolutional feature activations. We then train another convolutional layer on top of that as the pooling-guidance convolutional layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.Comment: Fixed typos. Journal extension of arXiv:1411.7466. Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc