Robustness against instability of sensory judgment in a human interface to draw a facial image using a psychometrical space model

Sugimoto, Futoshi; Yoneyama, Mitsutoshi

doi:10.1109/icme.2000.871443

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Generating faces of line drawing was frequently used as a research task of the IEC [8], [9], [118], [119], [130], [131], [184], [185], [54], [186]. One of such researches is autofitness assignment to accelerate EC convergence by estimating fitness values using Euclidian distance from individuals [118], [119] and positional relationship with userselected individuals [130], [131], [184], [185], [54], [186] (c.f. section IV-C).…”

Section: G Face Image Generationmentioning

confidence: 99%

“…One simple idea is to assign a same bias fitness values to the unselected individuals; another is to measure the Euclidian distances among chromosomes for them [54]; the other is to use approximate reasoning to predict their bias fitness values from the selected individuals [130], [131]. Further improvement for the IEC-based cartoon face drawing system is to measure the distances not in a phenotype space, face parameter space, but in a psychological space constructed by the multidimensional scaling method [184], [185], [186].…”

Section: Prediction Of Fitness Valuesmentioning

confidence: 99%

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Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation

2001

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We overview the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on human subjective evaluation. First, the definition and features of the IEC are described. Then, the overview of the IEC research follows. The overview consists of two parts: application and interface researches. The IEC application fields in this survey include graphic art and animation, 3-D CG lighting, music, editorial design, industrial design, face image generation, speech processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, and so on. The interface research to reduce human fatigue in this survey includes interfaces of fitness input and display based on fitness prediction, accelerating EC convergence especially in early EC generations, combinations of interactive and normal ECs, and active user intervention. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. The biggest feature of this paper is to wholly survey them with 200 IEC papers rather than to carefully select representative papers to show the status quo of the IEC researches.

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Section: G Face Image Generationmentioning

confidence: 99%

Section: Prediction Of Fitness Valuesmentioning

confidence: 99%

Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation

2001

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“…There are two principal learning methods for predicting fitness: Euclidean distances in the search space [16,17] and rule systems trained by neural networks [14]. There are other proposals for predicting fitness based on fuzzy logic or hybrid techniques [27,32,33].…”

Section: Introductionmentioning

confidence: 99%

Optimization Using Genetic Algorithms with Micropopulations

Sáez

2008

Optimization Techniques for Solving Complex Problems

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“…Sugimoto and Honda (6) examined and implemented IEC in multiple ways to find a facial image envisioned by the user and claimed that the "picking-some" method, a method according to which only some of the faces provided by the computer are given a fitness value, was most effective. Later, in (7), the convergence rate of the algorithm was improved by introducing fuzzy logic. Such face recognition algorithms have very important applications in forensic science, especially as a criminal's face can easily be drawn by witnesses to a crime.…”

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confidence: 99%

Color Separation in Forensic Image Processing Using Interactive Differential Evolution

Mushtaq

Rahnamayan

Siddiqi

2014

Journal of Forensic Sciences

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Color separation is an image processing technique that has often been used in forensic applications to differentiate among variant colors and to remove unwanted image interference. This process can reveal important information such as covered text or fingerprints in forensic investigation procedures. However, several limitations prevent users from selecting the appropriate parameters pertaining to the desired and undesired colors. This study proposes the hybridization of an interactive differential evolution (IDE) and a color separation technique that no longer requires users to guess required control parameters. The IDE algorithm optimizes these parameters in an interactive manner by utilizing human visual judgment to uncover desired objects. A comprehensive experimental verification has been conducted on various sample test images, including heavily obscured texts, texts with subtle color variations, and fingerprint smudges. The advantage of IDE is apparent as it effectively optimizes the color separation parameters at a level indiscernible to the naked eyes.

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Robustness against instability of sensory judgment in a human interface to draw a facial image using a psychometrical space model

Cited by 7 publications

References 6 publications

Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation

Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation

Optimization Using Genetic Algorithms with Micropopulations

Color Separation in Forensic Image Processing Using Interactive Differential Evolution

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