1. The document presents a study on improving the perceived brightness of virtual objects in optical see-through head-mounted displays (OST-HMDs) by gradually reducing the transparency of liquid crystal (LC) visors.
2. An experiment was conducted where participants observed virtual objects through an OST-HMD equipped with LC visors while the visors' transparency gradually decreased. Participants then evaluated changes in brightness of the real and virtual scenes.
3. Analysis of results found that as the visors darkened, participants were less aware of decreases in real brightness but perceived the virtual objects to grow brighter, supporting the study's hypotheses. Longer dimming periods also reduced perceptions of real brightness changes more.
The document discusses various factors that affect the mapping of light intensity arriving at a camera lens to digital pixel values stored in an image file. It describes the radiometric response function, vignetting, and point spread function, which characterize how light is mapped and degraded by the camera imaging system. Sources of noise during image sensing and processing steps are also outlined. Methods to model and remove vignetting effects as well as deconvolve blur and noise in images using estimated point spread functions and noise levels are presented.
Digital images are formed through a process of image acquisition, sampling and quantization. An image is represented as a 2D array of pixels, with each pixel having an intensity or color value. The spatial resolution and pixel depth determine how much detail can be preserved from the original scene. A higher resolution captures more details but results in larger file sizes. The human eye can distinguish between intensities that differ by around 5% and can detect spatial frequencies up to about 60 cycles/degree. Digital image processing techniques are needed to enhance, analyze and compress digital images for different applications.
Digital image processing deals with manipulating digital images through a computer. It focuses on topics like image formation through analog to digital conversion, pixel representation of images, common image file formats like binary, grayscale, and color. Applications of digital image processing include zooming images, adjusting brightness and contrast, as well as uses in television, medicine, pattern recognition, and more. In conclusion, digital image processing has many applications and its use is growing.
20120417 IMechE YMS Seminar on Daylighting modeling technique in built-enviro...ekwtsang
This document discusses daylight modeling techniques using global illumination programs like RADIANCE. It introduces key terms like daylight factor and glare index used to assess daylighting. It explains the RADIANCE system and common parameters used to control ambient bounces, divisions and accuracy. It also discusses two common misunderstandings - using a large single surface and applying luminance variation to a non-flattened surface. The document provides an overview of global illumination modeling and the RADIANCE software for daylighting studies.
20120328 Technical Seminar on Daylighting Environment in Hong Kongekwtsang
The document summarizes a presentation on daylighting environments in Hong Kong. It discusses the benefits of daylighting, key parameters that affect indoor daylighting like building area and orientation, glass type, window area, shading and external obstructions. It also analyzes the daylighting performances of two commercial buildings in Hong Kong through simulation and compares different assessment criteria used in green building standards like PNAP, LEED and BEAM Plus. The presentation raises questions about spatial daylight autonomy calculations required in the new LEED standards and proposes approaches to address issues like weather data and simulation time.
This document discusses key concepts related to image formation in computer vision. It covers geometric primitives like points, lines, and planes and how they are projected from 3D to 2D. It also discusses image formation in the human eye and digital cameras. The process of capturing digital images involves sampling and quantizing the continuous image function. Factors like spatial resolution, intensity resolution, and image representations like RGB images are also summarized.
The document discusses various factors that affect the mapping of light intensity arriving at a camera lens to digital pixel values stored in an image file. It describes the radiometric response function, vignetting, and point spread function, which characterize how light is mapped and degraded by the camera imaging system. Sources of noise during image sensing and processing steps are also outlined. Methods to model and remove vignetting effects as well as deconvolve blur and noise in images using estimated point spread functions and noise levels are presented.
Digital images are formed through a process of image acquisition, sampling and quantization. An image is represented as a 2D array of pixels, with each pixel having an intensity or color value. The spatial resolution and pixel depth determine how much detail can be preserved from the original scene. A higher resolution captures more details but results in larger file sizes. The human eye can distinguish between intensities that differ by around 5% and can detect spatial frequencies up to about 60 cycles/degree. Digital image processing techniques are needed to enhance, analyze and compress digital images for different applications.
Digital image processing deals with manipulating digital images through a computer. It focuses on topics like image formation through analog to digital conversion, pixel representation of images, common image file formats like binary, grayscale, and color. Applications of digital image processing include zooming images, adjusting brightness and contrast, as well as uses in television, medicine, pattern recognition, and more. In conclusion, digital image processing has many applications and its use is growing.
20120417 IMechE YMS Seminar on Daylighting modeling technique in built-enviro...ekwtsang
This document discusses daylight modeling techniques using global illumination programs like RADIANCE. It introduces key terms like daylight factor and glare index used to assess daylighting. It explains the RADIANCE system and common parameters used to control ambient bounces, divisions and accuracy. It also discusses two common misunderstandings - using a large single surface and applying luminance variation to a non-flattened surface. The document provides an overview of global illumination modeling and the RADIANCE software for daylighting studies.
20120328 Technical Seminar on Daylighting Environment in Hong Kongekwtsang
The document summarizes a presentation on daylighting environments in Hong Kong. It discusses the benefits of daylighting, key parameters that affect indoor daylighting like building area and orientation, glass type, window area, shading and external obstructions. It also analyzes the daylighting performances of two commercial buildings in Hong Kong through simulation and compares different assessment criteria used in green building standards like PNAP, LEED and BEAM Plus. The presentation raises questions about spatial daylight autonomy calculations required in the new LEED standards and proposes approaches to address issues like weather data and simulation time.
This document discusses key concepts related to image formation in computer vision. It covers geometric primitives like points, lines, and planes and how they are projected from 3D to 2D. It also discusses image formation in the human eye and digital cameras. The process of capturing digital images involves sampling and quantizing the continuous image function. Factors like spatial resolution, intensity resolution, and image representations like RGB images are also summarized.
This document discusses fundamentals of imaging and image processing. It describes features of human vision including the retina, cones, and rods. Cones are sensitive to color while rods provide a general picture. Images are formed in the eye based on the height and focal length. The human visual system can adapt to a wide range of light intensities but can only perceive a small sub-range at a time. Electromagnetic waves carry energy proportional to their frequency. Digital images are discrete functions defined over a spatial domain with intensity values. Image formation depends on illumination and reflectance. Sampling digitizes spatial coordinates while quantization digitizes intensities into discrete levels. The sampling theorem establishes that a signal can be reconstructed from samples if the sampling rate exceeds
This document discusses digital image fundamentals, including the structure and function of the human visual system, image formation, sampling and quantization, and spatial and gray-level resolution. It explains how light is sensed by the eyes and processed by the brain, and how continuous-tone images are converted to digital form through sampling and quantization. It also covers the tradeoff between spatial and gray-level resolution and issues like aliasing that can occur from insufficient sampling.
The document discusses image formation in computer vision, including geometric primitives like points, lines, and planes; photometric image formation in the human eye and digital cameras; and different image representations like binary, grayscale, and RGB images. Key topics covered include the electromagnetic spectrum, trichromatic vision, lenses, focal length, sampling and quantization in digital images, and factors affecting the performance of digital cameras.
This document discusses lighting and shading models in computer graphics. It explains that lighting has two main components - the lighting model which calculates intensity at surface points, and surface rendering methods like ray tracing. Common lighting models include ambient, diffuse, and specular components. The diffuse component follows Lambert's cosine law, while the specular component uses Snell's law and the Phong reflection model. Together these components make up the lighting equation, which is approximated using shading techniques like constant, Gouraud, and Phong shading to assign colors to pixels.
This document discusses rendering algorithms and techniques. It begins by defining rendering as the process of generating 2D or 3D images from 3D models. There are two main categories of rendering: real-time rendering used for interactive graphics, and pre-rendering used where image quality is prioritized over speed. The three main computational techniques are ray casting, ray tracing, and shading. Ray tracing simulates physically accurate lighting by tracing the path of light rays. Shading determines an object's shade based on attributes like diffuse illumination and light source contributions.
This document discusses quality assessment of 2D and 3D images. It defines image quality assessment as creating algorithms to gauge the perceived quality of visual stimuli as judged by human observers. There are two types of quality assessment: objective and subjective. Objective algorithms are categorized as full-reference, no-reference, and reduced-reference. The document outlines various parameters for 2D image quality assessment and discusses how 3D images are created using depth cues. Popular 3D quality metrics like SSIM and UQI are also mentioned.
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resol...CSCJournals
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
The document provides an overview of the human visual system and digital cameras. It discusses the key components of the human eye, including the cornea, sclera, choroid, lens, and retina. It also describes how images are formed on the retina through the lens and light receptors. For digital cameras, the document outlines the basic components and image formation process, including the aperture, optical system, and imaging sensor. It also provides equations to convert between camera and image plane coordinates.
The document discusses the four types of image resolution: spatial, spectral, radiometric, and temporal resolution. Spatial resolution refers to the ability of a sensor to identify the smallest details visible in an image. Spectral resolution is the sensor's ability to distinguish between narrow wavelength bands, while radiometric resolution refers to its ability to detect small differences in energy levels. Temporal resolution describes how frequently data is captured for a given location. Higher resolution in all four types provides more detailed, clear images for analysis.
This document discusses sensitometry, which is the quantitative evaluation of how a photographic film responds to radiation and processing. Sensitometry involves producing a sensitometric strip by exposing a film to different levels of radiation and then plotting the characteristic curve. The characteristic curve shows the optical density of the film plotted against the log of relative exposure. Key features of the curve include gross fog, threshold, contrast, latitude, speed/sensitivity and maximum density. Understanding a film's sensitometric properties allows for reproducing an invisible x-ray image with optimal contrast and detail.
X-rays can interact with matter in three ways:
1. They can be absorbed, where all the X-ray's energy is transferred into the patient's tissue.
2. They can be scattered, where the X-ray changes direction and loses some energy.
3. They can be transmitted through the patient without interaction. Most diagnostic X-rays are either absorbed or scattered within the patient.
This document summarizes Juan Pedro López Velasco's thesis work on developing visual attention and perception models for assessing video quality. The work has two main objectives: 1) Predicting visual discomfort in 3D stereoscopic video by analyzing factors like motion, disparity, and parallax changes. 2) Improving 2D video quality metrics by applying visual attention models that weight regions of interest to better correspond to human perception. The work involves conducting subjective testing to determine important quality factors, developing computational models of visual attention, and incorporating these models into new objective metrics to provide more accurate quality assessment.
This document discusses digital image fundamentals including:
- The structure and function of the human eye and vision system.
- How images are represented digitally as matrices of pixel values.
- Factors that determine the resolution of a digital image such as sampling rate, quantization, and number of bits per pixel.
- Basic relationships between pixels such as connectivity and labeling of connected components.
This document provides an overview of a digital image processing lecture given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It includes information about the instructor's contact information and office hours. The document then summarizes the contents of Chapter 2, which covers topics like visual perception, light and the electromagnetic spectrum, image sensing and acquisition, and basic relationships between pixels. Examples and diagrams are provided to illustrate concepts like the structure of the human eye, image formation, brightness adaptation, and the electromagnetic spectrum. Optical illusions are also discussed as examples of how visual perception does not always match physical stimuli.
This document provides an overview of a digital image processing lecture given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It includes information about the instructor's contact information and office hours. The document then summarizes the contents of Chapter 2, which covers topics like visual perception, light and the electromagnetic spectrum, image sensing and acquisition, and basic relationships between pixels. Examples and diagrams are provided to illustrate concepts like the structure of the human eye, image formation, brightness adaptation, and the electromagnetic spectrum. Optical illusions are also discussed as examples of how visual perception does not always match physical light intensities.
This document discusses how humans perceive visual stimuli and images. It covers topics like the anatomy of the eye, luminance, contrast, color perception, and models of visual sensitivity. Some key points:
- The retina detects light and contains photoreceptors like rods and cones that are sensitive to different wavelengths. The fovea provides high spatial resolution.
- Luminance describes the achromatic component of an image and is proportional to energy. Contrast is a better measure of visual differences than linear changes in luminance.
- Weber's law and power law transformations model how visual sensitivity depends on background luminance in a perceptually uniform way.
- The contrast sensitivity function measures sensitivity to spatial frequencies and
Stereo vision allows computing depth from two images captured from different viewpoints. The key is measuring disparity, how much each pixel moves between the two images. Epipolar geometry constrains the possible matches. Basic stereo algorithms match pixels in conjugate epipolar lines. State-of-the-art methods formulate stereo as an energy minimization problem that balances match quality and smoothness in the depth map. Dynamic programming and graph cuts provide good approximations to solve this NP-hard problem. Structured light and laser scanning are alternatives to passive stereo that simplify correspondence.
Comparing the Performance of Different Ultrasonic Image Enhancement Technique...Md. Shohel Rana
Medical ultrasound US images are usually corrupted by speckle noise during their acquisition. De-noising techniques are to remove noises while retaining the important signal features. Preservation of the image sharpness and details while suppressing the speckle noise. A novel restoration scheme has been introduced for ultrasound (US) images for speckle reduction which enhances the signal-to-noise ratio while conserving the edges and lines in the image
The document provides an overview of object detection methods for nighttime surveillance. It discusses two main approaches: (1) a Contrast Change method that detects objects based on changes in local contrast between frames, and (2) a Salient Contrast Analysis method that improves on the first by adding adaptability using machine learning and feedback from trajectory analysis. Experimental results showed the Salient Contrast Analysis method achieved better detection accuracy and lower tracking errors than the original Contrast Change method.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
This document discusses fundamentals of imaging and image processing. It describes features of human vision including the retina, cones, and rods. Cones are sensitive to color while rods provide a general picture. Images are formed in the eye based on the height and focal length. The human visual system can adapt to a wide range of light intensities but can only perceive a small sub-range at a time. Electromagnetic waves carry energy proportional to their frequency. Digital images are discrete functions defined over a spatial domain with intensity values. Image formation depends on illumination and reflectance. Sampling digitizes spatial coordinates while quantization digitizes intensities into discrete levels. The sampling theorem establishes that a signal can be reconstructed from samples if the sampling rate exceeds
This document discusses digital image fundamentals, including the structure and function of the human visual system, image formation, sampling and quantization, and spatial and gray-level resolution. It explains how light is sensed by the eyes and processed by the brain, and how continuous-tone images are converted to digital form through sampling and quantization. It also covers the tradeoff between spatial and gray-level resolution and issues like aliasing that can occur from insufficient sampling.
The document discusses image formation in computer vision, including geometric primitives like points, lines, and planes; photometric image formation in the human eye and digital cameras; and different image representations like binary, grayscale, and RGB images. Key topics covered include the electromagnetic spectrum, trichromatic vision, lenses, focal length, sampling and quantization in digital images, and factors affecting the performance of digital cameras.
This document discusses lighting and shading models in computer graphics. It explains that lighting has two main components - the lighting model which calculates intensity at surface points, and surface rendering methods like ray tracing. Common lighting models include ambient, diffuse, and specular components. The diffuse component follows Lambert's cosine law, while the specular component uses Snell's law and the Phong reflection model. Together these components make up the lighting equation, which is approximated using shading techniques like constant, Gouraud, and Phong shading to assign colors to pixels.
This document discusses rendering algorithms and techniques. It begins by defining rendering as the process of generating 2D or 3D images from 3D models. There are two main categories of rendering: real-time rendering used for interactive graphics, and pre-rendering used where image quality is prioritized over speed. The three main computational techniques are ray casting, ray tracing, and shading. Ray tracing simulates physically accurate lighting by tracing the path of light rays. Shading determines an object's shade based on attributes like diffuse illumination and light source contributions.
This document discusses quality assessment of 2D and 3D images. It defines image quality assessment as creating algorithms to gauge the perceived quality of visual stimuli as judged by human observers. There are two types of quality assessment: objective and subjective. Objective algorithms are categorized as full-reference, no-reference, and reduced-reference. The document outlines various parameters for 2D image quality assessment and discusses how 3D images are created using depth cues. Popular 3D quality metrics like SSIM and UQI are also mentioned.
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resol...CSCJournals
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
The document provides an overview of the human visual system and digital cameras. It discusses the key components of the human eye, including the cornea, sclera, choroid, lens, and retina. It also describes how images are formed on the retina through the lens and light receptors. For digital cameras, the document outlines the basic components and image formation process, including the aperture, optical system, and imaging sensor. It also provides equations to convert between camera and image plane coordinates.
The document discusses the four types of image resolution: spatial, spectral, radiometric, and temporal resolution. Spatial resolution refers to the ability of a sensor to identify the smallest details visible in an image. Spectral resolution is the sensor's ability to distinguish between narrow wavelength bands, while radiometric resolution refers to its ability to detect small differences in energy levels. Temporal resolution describes how frequently data is captured for a given location. Higher resolution in all four types provides more detailed, clear images for analysis.
This document discusses sensitometry, which is the quantitative evaluation of how a photographic film responds to radiation and processing. Sensitometry involves producing a sensitometric strip by exposing a film to different levels of radiation and then plotting the characteristic curve. The characteristic curve shows the optical density of the film plotted against the log of relative exposure. Key features of the curve include gross fog, threshold, contrast, latitude, speed/sensitivity and maximum density. Understanding a film's sensitometric properties allows for reproducing an invisible x-ray image with optimal contrast and detail.
X-rays can interact with matter in three ways:
1. They can be absorbed, where all the X-ray's energy is transferred into the patient's tissue.
2. They can be scattered, where the X-ray changes direction and loses some energy.
3. They can be transmitted through the patient without interaction. Most diagnostic X-rays are either absorbed or scattered within the patient.
This document summarizes Juan Pedro López Velasco's thesis work on developing visual attention and perception models for assessing video quality. The work has two main objectives: 1) Predicting visual discomfort in 3D stereoscopic video by analyzing factors like motion, disparity, and parallax changes. 2) Improving 2D video quality metrics by applying visual attention models that weight regions of interest to better correspond to human perception. The work involves conducting subjective testing to determine important quality factors, developing computational models of visual attention, and incorporating these models into new objective metrics to provide more accurate quality assessment.
This document discusses digital image fundamentals including:
- The structure and function of the human eye and vision system.
- How images are represented digitally as matrices of pixel values.
- Factors that determine the resolution of a digital image such as sampling rate, quantization, and number of bits per pixel.
- Basic relationships between pixels such as connectivity and labeling of connected components.
This document provides an overview of a digital image processing lecture given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It includes information about the instructor's contact information and office hours. The document then summarizes the contents of Chapter 2, which covers topics like visual perception, light and the electromagnetic spectrum, image sensing and acquisition, and basic relationships between pixels. Examples and diagrams are provided to illustrate concepts like the structure of the human eye, image formation, brightness adaptation, and the electromagnetic spectrum. Optical illusions are also discussed as examples of how visual perception does not always match physical stimuli.
This document provides an overview of a digital image processing lecture given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It includes information about the instructor's contact information and office hours. The document then summarizes the contents of Chapter 2, which covers topics like visual perception, light and the electromagnetic spectrum, image sensing and acquisition, and basic relationships between pixels. Examples and diagrams are provided to illustrate concepts like the structure of the human eye, image formation, brightness adaptation, and the electromagnetic spectrum. Optical illusions are also discussed as examples of how visual perception does not always match physical light intensities.
This document discusses how humans perceive visual stimuli and images. It covers topics like the anatomy of the eye, luminance, contrast, color perception, and models of visual sensitivity. Some key points:
- The retina detects light and contains photoreceptors like rods and cones that are sensitive to different wavelengths. The fovea provides high spatial resolution.
- Luminance describes the achromatic component of an image and is proportional to energy. Contrast is a better measure of visual differences than linear changes in luminance.
- Weber's law and power law transformations model how visual sensitivity depends on background luminance in a perceptually uniform way.
- The contrast sensitivity function measures sensitivity to spatial frequencies and
Stereo vision allows computing depth from two images captured from different viewpoints. The key is measuring disparity, how much each pixel moves between the two images. Epipolar geometry constrains the possible matches. Basic stereo algorithms match pixels in conjugate epipolar lines. State-of-the-art methods formulate stereo as an energy minimization problem that balances match quality and smoothness in the depth map. Dynamic programming and graph cuts provide good approximations to solve this NP-hard problem. Structured light and laser scanning are alternatives to passive stereo that simplify correspondence.
Comparing the Performance of Different Ultrasonic Image Enhancement Technique...Md. Shohel Rana
Medical ultrasound US images are usually corrupted by speckle noise during their acquisition. De-noising techniques are to remove noises while retaining the important signal features. Preservation of the image sharpness and details while suppressing the speckle noise. A novel restoration scheme has been introduced for ultrasound (US) images for speckle reduction which enhances the signal-to-noise ratio while conserving the edges and lines in the image
The document provides an overview of object detection methods for nighttime surveillance. It discusses two main approaches: (1) a Contrast Change method that detects objects based on changes in local contrast between frames, and (2) a Salient Contrast Analysis method that improves on the first by adding adaptability using machine learning and feedback from trajectory analysis. Experimental results showed the Salient Contrast Analysis method achieved better detection accuracy and lower tracking errors than the original Contrast Change method.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nucleic Acid-its structural and functional complexity.
BrightView at IEEE VR 2018
1. BrightView
Increasing Perceived Brightness of Optical See-Through Head-
Mounted Displays Through Unnoticeable Incident Light Reduction
Shohei Mori1, Sei Ikeda2, Alexander Plopski3, and Christian Sandor3
1Keio University, Japan
2Ritsumeikan University, Japan
3Nara Institute of Science and Technology, Japan
2. Real-Virtual Brightness Inconsistency
• Our visual system can perceive…
• 10-2 cd/m2 on an asphalt road under moonlight
• 2×105 cd/m2 on a sunlit beach
• Off-the-shelf OST-HMDs’ projector
• approx. 103 cd/m2
OST-HMD
Eyes
Real
object
Virtual Real
Perceived image
(inconsistent) Eyes
Virtual Real
Perceived image
(consistent)
OST-HMD +
Dimming Visors
Real
object
MS HoloLens Epson Moverio BT-300
2
3. Related Work
MS HoloLens Epson Moverio BT-300
FixedVisors
Sony Glasstron
AdjustableVisors
Seiko Transitions
Photochromic
Visors
Products Research Fields
[Lincoln et al., I3D, 2017]
[Hiroi et al., AH, 2017]
Our Approach Psychologically correct rendering 3
4. Bright Environment
Under gradual real light reduction, OST-HMD users will be...
1. Less aware of the real light dimming
2. Still aware of improvement of the virtual light brightness
Idea of Our Study
Virtual teapot
Dark Environment
4
5. Our Goal
• Improve the perceived brightness of virtual objects for OST-HMDs
• Use programable liquid crystal (LC) visors
• Gradually change the LC visors' opacity in an unnoticeable rate
• Demonstrate the following facts
1. Users feel increases in virtual content brightness
while they are less conscious of decreases in the real scene brightness
after a gradual increase in the opaqueness of the LC visors
2. Variations in durations have effects
5
6. Proof-of-Concept Prototype
• OST-HMD: Epson BT-300
• Display: 1,280×720px ×2, Diag. 23° FoV
• Camera: 2,560×1,920px
• Illuminometer
• LC Shutters: Root-R RV-3DGBT1
• Resolution: 1×1px ×2
• Transmittance: 9.0 - 22.7%
(Measured using a linear camera)
• Controller: Arduino Yun mini
Illuminometer
OST-HMD
LC shutter
OST-HMD
LC shutter
R
αR
V
6
7. Dimming Function in Illumination Shedding
1. Linear functions will work adequately for illumination shedding
unless the dimming speed remains at a certain level
[Akashi and Neches, 2004]
2. The longer changes, the less noticeable
• A luminance fluctuation of about seven percent is not detectable [Shikakura et al. 2003]
• As the luminance begins to change, the correct answer rate of the initial brightness decreases
over time [Akashi and Neches, 2004]
• If the luminance fluctuation ranges from several to ten-odd seconds, the detectability of the
change does not depend on the initial luminance [Shikakura et al. 2003]
・Dimming Function Linear
・Duration Longer as possible
Duration
Transmittance
Time
7
10. Procedures
1. The participant looks at virtual
object (= 100 brightness)
2. He/she observes the scene
during the real light dimming
• α changes from 22.7% to 9.0%
• Three durations: 5/10/20s
3. He/she answers the
brightness of the real scene
or the virtual object
10
11. Scenes
Scene 1
Scene 2
Scene 3
Scene 1 (Flat real scene + Virtual dot)
• 14 males + 2 females (age 20 to 24)
• 256 raw magnitudes
= 2 targets (real/virtual) × (1 control + 3 durations) × 2 times × 16 people
Scene 2 (3D scene + Virtual dot)
• 26 males + 5 females (age 21 to 25)
• 248 raw magnitudes
= 2 targets (real/virtual) × (1 control + 3 durations) × 31 people
Scene 3 (3D scene + 3D object)
• 26 males + 5 females (age 21 to 25)
• 248 raw magnitudes
= 2 targets (real/virtual) × (1 control + 3 durations) × 31 people
11
12. Analysis Based on Stevens’ Law
𝑃 = 𝐶𝑆 𝑘
Real Scene
Perceived brightness
Constant
Luminance
Exponent
𝑃𝑠 = 𝐶(𝛼 𝑠 𝑆𝑟) 𝑘 𝑟 Observation
𝑃𝑒 = 𝐶(𝛼 𝑒 𝑆𝑟) 𝑘 𝑟 Evaluation
𝜖 𝑟 =
𝑃𝑒
𝑃𝑠
𝛼 𝑒
𝛼 𝑠
−𝑘r
Criteria
・𝜖 = 1: The evaluation follows Stevens' Law
・𝜖 ≠ 1: The evaluation deviates Stevens' Law
12
0.6 (complex scene)
Stevens' Law
0.31 (dot)Virtual Object
𝑃𝑠 = 𝐶𝑆 𝑣
𝑘 𝑣
𝑃𝑒 = 𝐶𝑆 𝑣
𝑘 𝑣
𝜖 𝑣 =
𝑃𝑒
𝑃𝑠
100
Deviation Criteria 𝜖
100
User’s
evaluation
User’s
evaluation
13. Hypotheses
1. Users feel increases in virtual content brightness while they are less
conscious of decreases in the real scene brightness after a gradual
increase in the opaqueness of the LC visors.
H1 After a gradual increase in the opaqueness of the LC visors,
participants will not notice a significant decrease of the brightness
of the real scene (𝜖 𝑟 > 1)
H2 After a gradual increase in the opaqueness of the LC visors,
participants will perceive the virtual content to be brighter (𝜖 𝑣 > 1
and Pe/Ps > 1)
2. Variations in durations have effects.
H3 If the brightness is adjusted over a longer period, the perceived
deviation values will be larger
13
14. Results
Scene 1
(Flat scene + Virtual dot)
Scene 2
(3D scene + Virtual dot)
Scene 3
(3D scene + 3D virtual object)
14
15. Discussions
• Summary of the experiments
• H1 is supported for every condition
H1: After a gradual increase in the opaqueness of the LC visors, participants will not notice a significant
decrease of the brightness of the real scene.
• H2 is supported for Scene 1 and 2 but in Scene 3
H2: After a gradual increase in the opaqueness of the LC visors, participants will perceive the virtual
content to be brighter
• H3 is supported for real scene in Scene 1
H3: If the brightness is adjusted over a longer period, the perceived deviation values will be larger
• Limitations
• 22.7% to 9.0% transparency of LC visors
• The effects of virtual content’s size are not clear
• etc.
15
16. Summary & Future Work
• OST-HMD with LC visors control
• to preserve real-virtual brightness consistency
• Psychophysical study
• We formulated the deviation rate ε based on Stevens’ Law
• The deviation rates for real and virtual showed that the participants were
• Less noticeable to real light dimming
• Aware of brightness increases in virtual object
• Future work
• Formulation of real-virtual brightness relationship
• Comprehensive study on the effects of dynamic backgrounds
and other virtual contents
16
Editor's Notes
For augmented reality, optical see-through head-mounted displays is considered rather reliable than video see-throughs, since it does not occlude the real environment.
But due to the nature of optical combiners, it suffers from real-virtual brightness inconsistency because the projector is not powerful enough compared to the real environment luminance.
For example, office environment like this room has around 300 to 500 lux but outdoor environment is much brighter.
The latest off the shelf OST-HMDs like Epson BT-300 has only 2,000 Lux and as a result, the virtual object looks rather dim.
So the current OST-HMDs like MS HoloLens and BT-300 has visors to reduce the amount of lights from the real scene to “relatively” increase the amount of the virtual light.
While these two products have fixed visors, we have some alternatives such as adjustable and photochromic visors that can change the opacity manually using liquid crystal visors or automatically using chemical reactions.
In research fields, we have a lot of attempts to solve the real-virtual brightness inconsistency.
For example, Lincoln et al. achieved a physically high dynamic range augmentations by combining sensor arrays and a DMD projector.
Hiroi et al. selectively mask or enhance the pixels based on image space intensity analysis using a scene observing camera.
Our approach is, on the other hand, more focused on psychological aspect.
So our key idea is to reduce real lights in less noticeable amount over time and, at the same time, to present perceptually bright enough virtual object.
This means that the user feels bright enough virtual object without violating the brightness of the perceived real environment.
To simulate the illusion, we took this picture using an auto-gain control camera to mimic the human brightness adaptation.
The virtual teapot is visible in the weakly illuminated environment, although it turns into less visible in the bright environment as I explained before.
So that we reduce the amount of real lights under unconscious rates and then achieve the consistent augmentation.
This is the detail of our proof-of-concept prototype HMD.
Here, we simply attached 3D glasses to BT-300 and controlled the opacity using Arduino.
So that, the users receive virtual light directly from the HMD but the real lights are filtered by the transmittance alpha.
We have several choices for how to control the real light dimming such as non linear curve, step function, but we simply chose a linear function based on the knowledge of the research area called illumination shedding.
Illumination shedding is a research are aiming at efficient reduction of the amount of the office lights for energy saving.
Literatures say first: linear functions will work...
Second: The longer changes will be more acceptable for the users.
So we chose linear dimming function with acceptable durations for AR.
We created two environments.
In scene 1, we used a flat display as real backgrounds and placed a virtual dot in front of the participant.
In scene 2 and 3, we arranged 3D objects as real backgrounds and placed a dot or Utah teapot on the desk.
In this experiment, the user wear a mask to avoid the real lights and given a ten key for their feedback.
First they are asked to look at the virtual object in observation phase after the adaptation.
And observe the real light dimming in randomly selected durations.
Then, answer the brightness of the real or virtual stimuli.
Consequently, we collected around 250 raw magnitudes from 16 to 31 participants.
In this experiment, we collected data named “deviation criteria” formulated in this slide.
First we use stevens’ law to describe the relationships between the luminance and the perceived brightness.
Before the real light dimming, users will perceive Ps brightness regarding the real lights, and after the dimming, they will feel Pe.
So the ratio of these two equations gives us deviation criteria epsilon, which means if epsilon is 1 then the evaluation follows Steven’s law, but if epsilon is not 1, then the evaluation does not follow Stevens’ law.
So we can translate the first hypothesis into two.
For the second one,
These are the results in each scene.
Here, the horizontal axis shows durations of the real light dimming. Control means we did not change the transmittance of the LC visors.
The vertical axis shows the deviation criteria epsilon.
In all scene, the real light dimming becomes less noticeable if we increase the duration, and we confirmed that the participants partially felt the increases in virtual lights.
The duration definitely have effects for the real light dimming although we partially confirm this effect regarding the virtual lights.
Regarding these results we have a lot of discussions.