Aesthetics in digital photography / / Henri Maître

• Autore: Maître H (Henri)
• Pubblicazione: London, England : , : ISTE Ltd and John Wiley & Sons, Inc., , [2023]; ©2023
• Descrizione fisica: 1 online resource (324 pages)
• Disciplina: 770
• Soggetto topico: Photography - Digital techniques; Digital cameras; Image processing - Digital techniques
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Contents -- Introduction: Image and Gaze -- Chapter 1. The Legacy of Philosophers -- 1.1. The objectivist approach -- 1.1.1. The source: ancient Greece -- 1.1.2. After Greece -- 1.1.3. Kant and modern aesthetics -- 1.1.4. Objectivism after Kant: from pseudo-subjectivism to aesthetic realism -- 1.2. The subjectivist approach -- 1.2.1. From classicism to romanticism -- 1.2.2. The moderns -- 1.2.3. The influence of neurobiology -- 1.3. Subjectivism and objectivism: an ongoing debate -- Chapter 2. Neurobiology or the Arbitrator of Consciousness -- 2.1. fMRI protocols and neuroaesthetics -- 2.2. The fMRI quest for "beauty processes" in the brain -- 2.2.1. The role of the prefrontal cortex -- 2.2.2. The role of the insular cortex -- 2.2.3. The role of the visual areas -- 2.2.4. The role of memory and cognition -- 2.2.5. The role of embodiment -- 2.3. Responses from functional electric encephalography -- 2.4. A global cognitive scheme for aesthetic judgment? -- 2.4.1. J. Petitot's neurogeometric model -- 2.4.2. A. Chatterjee's aesthetic emotion model -- 2.4.3. The model by Brown et al -- 2.4.4. Model proposed by H. Leder -- 2.4.5. The model by C. Redies -- 2.4.6. The emotions model developed by S. Koelsch et al. -- 2.4.7. L.H. Hsu's model of emotions based on A. Damásio -- 2.4.8. Other models -- 2.5. A critique of neuroaesthetic methods -- 2.5.1. Criticism of neuroaesthetic methods -- 2.5.2. Criticisms of the objectives of neuroaesthetics -- Chapter 3. What Are the Criteria For a Beautiful Photo? -- 3.1. Before we enter into the fray -- 3.1.1. What reference books do we have? -- 3.1.2. "Beauty of an image" or "quality of an image"? -- 3.1.3. A glossary of aesthetic appraisal -- 3.1.4. Measuring beauty -- 3.2. Composition -- 3.2.1. Complexity versus simplicity -- 3.2.2. Unity. ; 3.2.3. A specific case in composition: landscapes -- 3.2.4. Using oculometry to analyze composition -- 3.2.5. Format or aspect ratio -- 3.2.6. The rule of thirds (RoT) -- 3.2.7. The center of the image -- 3.2.8. Other rules for composition -- 3.3. Histograms, spectral properties and textures -- 3.3.1. Histograms and gray levels -- 3.3.2. Focus, spectral density, fractals -- 3.3.3. Textures -- 3.4. Color -- 3.4.1. About the concept of color -- 3.4.2. Preferences related to isolated colors -- 3.4.3. Preferences related to color palettes -- 3.5. What behavioral psychosociology has to say -- 3.5.1. Images of nature -- 3.5.2. The aesthetics of faces -- 3.5.3. The role of the signature, title and context -- 3.5.4. Perception and memory: prototypicality -- Chapter 4. Algorithmic Approaches to "Calculate" Beauty -- 4.1. First steps: C. Henry -- 4.2. G.D. Birkhoff's mathematical approach -- 4.3. Those who followed G.D. Birkhoff -- 4.3.1. Beauty according to H.J. Eysenck -- 4.3.2. The Post-War years: the designers, A. Moles and M. Bense -- 4.3.3. A dynamic approach: P. Machado and A. Cardoso -- 4.3.4. Work carried out by J. Rigau, M. Feixas and M. Bert -- 4.4. Algorithmic approach with AI: J. Schmidhuber -- Chapter 5. The Holy Grail of the Digital World: Artificial Intelligence -- 5.1. Which artificial intelligence? -- 5.1.1. The principles -- 5.1.2. Learning algorithms -- 5.2. Why artificial intelligence in aesthetics? -- 5.3. Expert opinions -- 5.4. The database -- 5.4.1. Generalist databases, used for aesthetic judgments -- 5.4.2. Databases that are specialized for aesthetic photography -- 5.4.3. Databases dedicated to artistic judgment -- 5.4.4. Other image databases that are sometimes used -- 5.4.5. Increasing databases -- Chapter 6. Primitive-based Classification Methods -- 6.1. Judging aesthetics. ; 6.1.1. Multimedia primitives: the ACQUINE system (Datta et al.) -- 6.1.2. Edges and chromatic distance: Ke et al. -- 6.1.3. Photography rules: Luo and Tang and Mavridaki and Mezaris -- 6.1.4. High-level primitives: Dhar et al. -- 6.1.5. Generic descriptors of vision: Marchesotti et al. -- 6.2. Help in composing beautiful photos -- 6.2.1. The library of aesthetic primitives developed by Su et al. -- 6.2.2. The OSCAR system by Yao et al. -- 6.2.3. Embedded systems: Lo et al. and Wang et al. -- 6.3. Some specific research related to the evaluation of aesthetics using primitives -- 6.3.1. Color harmony: Lu et al. -- 6.3.2. Group photography: Wang et al. -- 6.3.3. Social networks and crowdsourcing: Schifanella et al. -- 6.3.4. Looking at comments: San Pedro et al. -- Chapter 7. Deep Neural Network Systems -- 7.1. DNNs dedicated to aesthetic evaluation -- 7.1.1. High and low resolutions: the RAPID system, Lu et al. -- 7.1.2. The multi-path DMA-Net architecture: Lu et al. -- 7.1.3. Adapting to the size of the image: Mai et al. -- 7.1.4. Finding beauty on the Web: Redi et al. -- 7.1.5. Siamese and GAN networks: Kong et al. and Deng et al. -- 7.1.6. Paying attention to the image construction: A-Lamp -- 7.2. Variants around the basic DNN architecture -- 7.2.1. Comparing photos between themselves: Schwarz et al. -- 7.2.2. Making use of knowledge of the subject: Kao et al. -- 7.2.3. BDN: halfway between classification and DNN -- 7.2.4. Using the distribution of the evaluations -- 7.2.5. Extracting a "dramatic" image from a panorama: the Creatism system -- 7.3. Written appraisals: analyzing them and formulating new ones -- 7.3.1. Photo critique captioning dataset (PCCD) -- 7.3.2. Neural aesthetic image retriever (NAIR) -- 7.3.3. Semantic processing by Ghosal et al. -- 7.3.4. Aesthetic multi attribute network (AMAN) -- 7.4. Measuring subjective beauty. ; 7.4.1. Recommendation systems -- 7.4.2. Defining the user's psychological profile -- 7.4.3. Learning the user's tastes through tests -- 7.4.4. Multiplying concurrent expertise -- Chapter 8. A Critical Analysis of Machine Learning Techniques -- 8.1. The popularity of studies on aesthetics -- 8.2. A summary of learning methods -- 8.2.1. Which architecture? Which software? -- 8.2.2. What performances? -- 8.3. Questioning the hypotheses -- 8.4. Specific features of beautiful images detected by a computer -- 8.4.1. Some observations on the photos in the AVA database -- 8.4.2. The scores in the AVA database -- Conclusion -- Appendix 1. A Brief Review of Aesthetics -- Appendix 2. Aesthetics in China -- Appendix 3. The Aesthetic of Persian Miniatures -- Appendix 4. Aesthetics in Japan -- References -- Index -- EULA.
• Titolo autorizzato: Aesthetics in digital photography
• ISBN: 1-394-22597-0; 1-394-22595-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830064403321
• Lo trovi qui: Univ. Federico II