Active Color Image Analysis for Recognizing Shadows

Many existing computer vision modules assume that shadows in an image have been accounted for prior to their application. In spite of this, relatively little work has been done on recognizing shadows or on recognizing a single surface material when directly lit and in shadow. This is in part because shadows cannot be infallible recognized until a scene\u27s lighting and geometry are known. However, color is a strong cue to the presence of shadows. We present a general color image segmentation algorithm whose output is amenable to the recovery of shadows as determined by an analysis of the physics of shadow radiance. Then, we show how an observer that can cast its own shadows can infer enough information about a scene\u27s illumination to refine the segmentation results to determine where the shadows in the scene are with reasonable confidence. Having an observer that can actively cast shadows frees us from restrictive assumptions about the scene illumination or the reliance on high level scene knowledge. We present results of our methods on images of complex indoor and outdoor scenes

Deep Convolutional Attention based Bidirectional Recurrent Neural Network for Measuring Correlated Colour Temperature from RGB images

Information on the connected colour temperature, which affects the image due to the surrounding illumination, is critical, particularly for natural lighting and capturing images. Several methods are introduced to detect colour temperature precisely; however, the majority of them are difficult to use or may generate internal noise. To address these issues, this research developed a hybrid deep model that properly measures temperature from RGB images while reducing noise. The proposed study includes image collection, pre-processing, feature extraction and CCT evaluation. The input RGB pictures are initially generated in the CIE 1931 colour space. After that, the raw input samples are pre-processed to improve picture quality by performing image cropping and scaling, denoising by hybrid median-wiener filtering and contrast enhancement via Rectified Gamma-based Quadrant Dynamic Clipped Histogram Equalisation (RG_QuaDy_CHE). The colour and texture features are eliminated during pre-processing to obtain the relevant CCT-based information. The Local Intensity Grouping Order Pattern (LIGOP) operator extracts the texture properties. In contrast, the colour properties are extracted using the RGB colour space’s mean, standard deviation, skewness, energy, smoothness and variance. Finally, using the collected features, the CCT values from the submitted images are estimated using a unique Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA_BRNNet) model. The Coati Optimisation Algorithm (COA) is used to improve the performance of a recommended classifier by modifying its parameters. In the Result section, the suggested model is compared to various current techniques, obtaining an MAE value of 529K and an RMSE value of 587K, respectively

A Survey of Algorithms Involved in the Conversion of 2-D Images to 3-D Model

Since the advent of machine learning, deep neural networks, and computer graphics, the field of 2D image to 3D model conversion has made tremendous strides. As a result, many algorithms and methods for converting 2D to 3D images have been developed, including SFM, SFS, MVS, and PIFu. Several strategies have been compared, and it was found that each has pros and cons that make it appropriate for particular applications. For instance, SFM is useful for creating realistic 3D models from a collection of pictures, whereas SFS is best for doing so from a single image. While PIFu can create extremely detailed 3D models of human figures from a single image, MVS can manage complicated situations with varied lighting and texture. The method chosen to convert 2D images to 3D ultimately depends on the demands of the application

Two Methods for Display of High Contrast Images

High contrast images are common in night scenes and other scenes that include dark shadows and bright light sources. These scenes are difficult to display because their contrasts greatly exceed the range of most display devices for images. As a result, the image contrasts are compressed or truncated, obscuring subtle textures and details. Humans view and understand high contrast scenes easily, ``adapting'' their visual response to avoid compression or truncation with no apparent loss of detail. By imitating some of these visual adaptation processes, we developed two methods for the improved display of high contrast images. The first builds a display image from several layers of lighting and surface properties. Only the lighting layers are compressed, drastically reducing contrast while preserving much of the image detail. This method is practical only for synthetic images where the layers can be retained from the rendering process. The second method interactively adjusts the displayed image to preserve local contrasts in a small ``foveal'' neighborhood. Unlike the first method, this technique is usable on any image and includes a new tone reproduction operator. Both methods use a sigmoid function for contrast compression. This function has no effect when applied to small signals but compresses large signals to fit within an asymptotic limit. We demonstrate the effectiveness of these approaches by comparing processed and unprocessed images

Multiple viewpoint rendering for three-dimensional displays

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 159-164).Michael W. Halle.Ph.D

Maneuvering Contested Space and Community An Ethnographic Study of the Underground Electronic Music Scene in Itaewon

This thesis examines the dynamics of the underground electronic music scene in Itaewon, Seoul, South Korea, within the framework of contested space. Building upon the theories of Henri Lefebvre, Anthony Cohen, and Sarah Thornton, this research explores the formation of communities, spatiality, and the impact of the COVID-19 pandemic on Itaewon’s cultural landscape. Drawing from Lefebvre's reflections on spatial contestation, the study investigates how physical and symbolic spaces in Itaewon shape the experiences and interactions within the underground music scene. It delves into the significance of venues such as clubs and bars as cultural hubs, where diverse groups come together to express themselves and forge communities. Informed by Cohen's theory of community, the research sheds light on the social bonds, shared practices, and sense of belonging that emerge within the underground electronic music scene. It explores the collaborative endeavors, mutual support, and navigation of the complexities of the urban environment and the covid pandemic through stories from interlocuters and ethnography. Thornton's work on club culture provides insights into the role of music and cultural practices in shaping the experiences of individuals within the Itaewon underground club scene. It examines the intersections between music, and identity, highlighting the ambiance and social dynamics of the underground electronic music community. Furthermore, the study delves into the impact of the COVID-19 pandemic on the underground music scene in Itaewon. It delves into the myriad challenges artists, organizers, and participants confront as they navigate the constraints imposed by restrictions, strive to sustain connections, explore the quest for safe spaces, and seek out alternative pathways for creative expression. By integrating these theoretical perspectives, this thesis provides a comprehensive understanding of the underground electronic music scene in Itaewon, emphasizing its significance within the LGBTQ+ community. It illuminates the transformative power of inclusive cultural spaces, the role of music in identity formation, and the resilience of communities in the face of adversity. The findings contribute to urban anthropology and our understanding of contested spaces, cultural expressions, and the ongoing evolution of underground scenes.MasteroppgaveSANT350MASV-SAN

Vision-assisted modeling for model-based video representations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 134-145).by Shawn C. Becker.Ph.D

Time and the long take in The Magnificent Ambersons, Ugetsu and Stalker

My thesis is an examination of the formal and textual aspects of the long take, principally as used in The Magnificent Ambersons (1942, Orson Welles), Ugetsil (1954, Kenji Mizoguchi), and Stalker (1979, Andrei Tarkovsky). The thesis begins by defining the long take as a shot of 25 seconds or longer that usually contains one of the following qualities: a sense of completeness or wholeness, 'durational complexity, ' and a 'soft' formal/thematic determinism. This working definition is used as part of a 'philosophical' formal-textual methodological approach to the long take informed by a 'common sense' philosophical understanding of time. An important element of this formal-textual methodology is 'contextual statistical analysis' (CSA) and close, accurate shot description. This 'common sense' philosophical understanding sees time as being expressible by properties that are both outside the self (external time) and by properties that are within the self (internal time). External time becomes the 'measurable' aspects of the long take (duration), which condition and are conditioned by the 'less quantifiable' aspects of a long take's internal time (pertinent formal and textual properties within the shot). Internal and external time combine to express the 'emotional quality' of time in a long take, which I call temporal tonality. By employing this formal-textual methodology to my three case study films, I demonstrate how a dominant use of the long take is an important (though not exclusive) formal component of each film's particular thematic and/or philosophical treatment of time. The long take is also analysed in two other case studies with more general designs: a taxonomy of the long take time and narrative time (Chapter 4), and an analysis of the long take as an expressive narrative agent in popular cinema (Chapter 5). Lastly, the statistical differences concerning long take usage gives rise to an articulation of three long take practices: Dialectical, Synthetic, and Radical. This original observation will lay down a general groundwork for further exploration of long take practice, style, theory, and analysis