Image enhancement of ninhydrin-processed and colored fingerprints

Kakuda, Hidetoshi; Akiba, Norimitsu; Kuroki, Kenro; Hibino, Kazuhito; Kurosawa, Kenji; Tsuchiya, Ken'ich; Yokota, Ryo; Imoto, Daisuke; Hirabayashi, Manato; Tanabe, Kosuke; Hawai, Yoshinori

doi:10.3408/jafst.759

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Hidetoshi and others said that the layered algorithm assumes that, in the natural scene, the image is composed of slowly changing brightness information and jumping reflection components, and the more real natural information can be restored by dynamically compressing the brightness component and retaining the reflection component. The algorithm can obtain satisfactory results for the halo phenomenon and diffusion phenomenon [ 4 ]. Hu and others said that the tone mapping algorithm based on gradient domain regards the image as a two-dimensional discrete function.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm

Man

2022

Computational Intelligence and Neuroscience

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With the continuous development and progress of image sensing technology in recent years, the problem of image data acquisition, recovery, and recording has been solved, and people have gradually realized that image data can be better recovered, recorded, and exhibited by collecting image data. It is also used in target recognition, image display, landscape design, and other fields. And the application of color mapping algorithm can more intuitively affect the final quality of the display image. This is extremely important for landscape design. It can be said that the emergence and application of color mapping algorithm provide strong technical support for both dynamic and static image color automatic adjustment and simulation. On this basis, taking the color mapping algorithm as the breakthrough, through the in-depth introduction of the color mapping algorithm, starting from the direction of landscape image color automatic adjustment, a relatively simplified color mapping algorithm model inheriting the positive and negative two-way vision model is proposed. At the same time, a simulation algorithm based on landscape suitcase color automatic adjustment is proposed based on the color mapping algorithm model. Experiments show that automatic color adjustment of landscape images based on color mapping algorithm can achieve more realistic image reproduction, and the color mapping method based on color mapping algorithm has less illumination bias. When the number of experiments is 30, the difference in visibility of the plane is 0.25 cd/m2, the difference of visibility according to the color change segmented by the gradient area is 0.71 cd/m2, and the difference of the illuminance difference of the transition image is 0.71 cd/m2. Gaussian pyramid is 1.25 cd/m2. The proposed method improves color density by reducing image sharpness and compensating for color sharpness of landscape image, and high quality landscape images provide assistance for landscape design.

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Section: Literature Reviewmentioning

confidence: 99%

Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm

Man

2022

Computational Intelligence and Neuroscience

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Smartphone Solutions for Citizen-Centered Risk Monitoring in Environmental Disaster Situations

Ishigaki

Tanaka

2021

Digital Services in Crisis, Disaster, and Emergency Situations

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Through an analysis of three case studies, this chapter proposes a new kind of democratic risk communication that can be realized through environmental sensing by citizens with smartphones, and considers the challenges involved. The three case studies, which the authors have implemented in the society, are as follows: (1) The Pocket Geiger (Pokéga) is a radiation sensor for citizens developed immediately after the Fukushima nuclear accident. More than 100,000 Pokéga units have been produced under an open source license. (2) The Unreal iSOTOPE is a mobile simulator developed for training Japanese law enforcement agencies during radiation disasters. (3) The Pocket PM2.5 Sensor visualizes the distribution of invisible air pollutants indoors and outdoors. It is particularly useful for fieldwork in developing countries where environmental assessments are inadequate.

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Image enhancement of ninhydrin-processed and colored fingerprints

Cited by 2 publications

References 18 publications

Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm

Simulation of Automatic Color Adjustment of Landscape Image Based on Color Mapping Algorithm

Smartphone Solutions for Citizen-Centered Risk Monitoring in Environmental Disaster Situations

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