Human Vision

The five human senses are the result of complex psycho-physiological phenomena (Geldard, 1953) allowing us to perceive our environment. It is important to stress the psychoneurological aspect of biological processes involving our senses. Indeed, without this link between memory and senses, the latter would not be as important. Identifying a fungus in situ is difficult, but remembering it is even more so. When identifying a fungus in the field, four of our five senses are mainly involved: touch, smell, taste, and sight. Hearing does not help in direct identification of the fungus. In this series of articles, the role and importance of these four senses in identifying a fungus are non-exhaustively reviewed.

The subjective experience of color by synesthetes when viewing achromatic letters and numbers supposedly relates to real color experience, as exemplified by the recruitment of the V4 color center observed in some brain imaging studies. Phenomenological reports and psychophysics tests indicate, however, that both experiences are different. Using functional magnetic resonance imaging, we tried to precise the degree of coactivation by real and synesthetic colors, by evaluating each color center individually, and applying adaptation protocols across real and synesthetic colors. We also looked for structural differences between synesthetes and nonsynesthetes. In 10 synesthetes, we found that color areas and retinotopic areas were not activated by synesthetic colors, whatever the strength of synesthetic associations measured objectively for each subject. Voxel-based morphometry revealed no white matter (WM) or gray matter difference in those regions when compared with 25 control subjects. But synesthetes had more WM in the retrosplenial cortex bilaterally. The joint coding of real and synesthetic colors, if it exists, must therefore be distributed rather than localized in the visual cortex. Alternatively, the key to synesthetic color experience might not lie in the color system.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency
and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled
knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A  B. This doctoral project moved from the working hypothesis that SCBIR  computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding  EO image understanding (EO-IU) in operating mode, synonym of GEOSS  ESA EO Level 2 product  human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product.
Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii)
imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based)
feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations.

Abstract—By taking into account the properties and limitations of the human visual system (HVS), images can be more efficiently compressed, colors more accurately reproduced, prints better rendered, to mention a few major advantages. To achieve these goals it is necessary to build a computational model of the HVS. In this paper we give an introduction to the general issue of HVS-modeling and review the specific applications of visual quality assessment and HVS-based image compression, which are closely related. On one hand, ...

In an age characterized by tremendous technological breakthroughs, the world is witnessing overwhelming quantities and types of information. Digital Libraries (DLs) are a result of these breakthroughs, but they have not been spared by the challenges resulting from them. While DLs stakeholders are still struggling to come to terms with the massive quantities and complex types of information, the needs of the digital library as an information/knowledge environment is still evolving including new challenging needs. Information Visualization ( ...

By comparing two-dimensional (2D) chemical animations designed for computer’s desktop with three-dimensional (3D) chemical animations designed for the full immersive virtual reality environment CAVETM we studied how virtual reality environments could raise student’s interest and motivation for learning. By using the 3ds maxTM, we can visualize the chemical phenomena easily and quickly without knowing any special computer language and export the application to files which are compatible with the CAVETM (Object or OpenGL files). After the participation in 3D animations at the CAVETM students comprehended the molecules’ structure and their changes during a chemical reaction better than during the 2D animations on the computer’s desktop, as the limitations of human vision had been overcome. Furthermore, the students were enthusiastic, as they had the feeling that they were inside the chemical reactions and they were facing the 3D molecules as if they were real objects front of them.

A specific form for the internal representation of local surface orientation is proposed, which is similar to Gibson's (1950) “amount and direction of slant”. Slant amount is usually quantifed by the angle σ between the surface normal and the line of sight (0°≦σ≦90°). Slant direction corresponds to the direction of the gradient of distance from the viewer to the surface, and may be defined by the image direction τ to which the surface normal would project (0°≦τ≦360°). Since the direction of slant is specified by the tilt of the projected surface normal, it is referred to as surface tilt (Stevens, 1979; Marr, 1982). The two degrees of freedom of orientation are therefore quantified by slant, an angle measured perpendicular to the image plane, and tilt, an angle measured in the image plane. The slanttilt form provides several computational advantages relative to some other proposals and is consistent with various psychological phenomena. Slant might be encoded by various means, e.g. by the cosine of the angle, by the tangent, or linearly by the angle itself. Experimental results are reported that suggest that slant is encoded by an internal parameter that varies linearly with slant angle, with resolution of roughly one part in 100. Thus we propose that surface orientation is encoded in human vision by two quantities, one varying linearly with slant angle, the other varying linearly with tilt angle.

A new numerical method of image processing suitable for visual-ization of corona images taken during total solar eclipses is presented. This method allows us to study both small-and large-scale coronal structures that remain invisible on original images because of their very high dynamic range of the coronal brightness. The method is based on the use of adaptive filters inspired by human vision and the sensitivity of resulting images is thus very close to that of the human eye during an eclipse. A high precision alignment method for white-light corona images is also discussed. The proposed method highly improves a widely used unsharp masking method employing a radially blurred mask. The results of these numerical image processing techniques are illustrated by a series of images taken during eclipses of the last decade. The method minimizes the risk of processing artifacts.

In the context of machine vision, image recognition refers to the ability of machines and algorithms to identify people, places, objects, gestures, or other subjects in a given image. Self-driving cars, for instance, use machine vision systems to locate road signs, vehicles, pedestrians, and cyclists, to understand the three-dimensional space in which they are located and avoid collisions. Facebook uses facial image recognition systems to identify photographs in which a person is present but not tagged, and to help visually impaired users identify people in a specific video. However, although it is simple for a human being to make sense of an image and identify its content, these operations are still particularly complex for machines and algorithms. In this paper we will investigate how machines and algorithms are trained for image recognition purposes. To start, the tasks performed by human annotators will be discussed. Part two will outline some problems with this process, and part three will present further thoughts and reflections on the subject. The terms ‘machine vision’ and ‘algorithmic vision’, which often appear throughout this paper, replace the term ‘computer vision’ and are used in a broad sense that seems to better reflect contemporary reality.

Fuzz on the fabrics, which is the fibers protruded from the fabric surface, is very important in view of appearance quality, since it causes unpleasant appearance on the fabrics and also leads to pilling which makes fabric appearance and softness worse. However, fuzz on fabric surface is measured mostly by subjective methods (human vision) rather than objective methods. Thus, in this study, objective method using image analysis techniques has been developed for the measurement of fuzz on fabric surface. Fuzz on the fabric has also been ranked and rated by experts in order to see the reliability of the results obtained from the fuzz measurement. It was observed that correlation coefficient (r) between rating value and objective measurement value was 0.9 and this correlation coefficient value confirmed the reliability of this method.

Machine vision has evolved to become a mainstream automation tool, enabling computers to replace human vision in high speed and precision manufacturing techniques. Machine vision systems are used to capture image, extract information using vision sensors and make intelligent decisions. Images usually acquired through modern cameras may be contaminated by a variety of noise sources and decreasing intensity and in most cases the type of noise and state of lighting are also not known apriori .There is a need for design of automation systems with capability to adapt to changing environments automatically. This requires computing architectures that are less complicated, highly flexible and more cost-effective as compared to the traditional ones, which calculates the coefficients of a general-purpose model. . In this context, surface roughness monitoring techniques using non-contact methods based on computer vision technology are becoming popular and cost effective. An evolvable hardware ...

In this work, a multi-channel model for image representation is derived based on the scale-space theory. This model is inspired in biological insights and it includes some important properties of human vision such as the Gaussian derivative model for early vision proposed by Young. The image transform that we propose in this work uses similar analysis operators as the Hermite

The paper presents a unified approach for quality evaluation of food using image processing and machine vision. In this paper basic tool is combination of computer and machine vision for image analysis and processing through which fast and accurate quality is achieved that too with the help of non-destructive method. Machine vision in food has broadened its range of applications from grains, cereals, fruits to vegetables including processed products as well as spices in which there is a high degree of quality achieved as compared to human vision inspection. In this paper we quantify the qualities of various food products and figure out features which are directly or inversely affect the quality of the food product. Based on these features a generalized formula of quality is proposed to be used for quality evaluation of any type of food product.

The color human visual response is driven by specialized cells called cones, which exist in three types, viz. R, G, and B. Software is developed to simulate how color images are displayed for different types of color blindness. Specified the default color deficiency associated with a user, it generates a preview of the rainbow (in the visible range, from red to violet) and shows up, side by side with a colorful image provided as input, the display correspondent colorblind. The idea is to provide an image processing after image acquisition to enable a better perception of colors by the color blind. Examples of pseudo-correction are shown for the case of Protanopia (red blindness). The system is adapted into a screen of an i-pad or a cellphone in which the colorblind observe the camera, the image processed with color detail previously imperceptible by his naked eye. As prospecting, wearable computer glasses could be manufactured to provide a corrected image playback. The approach can also provide augmented reality for human vision by adding the UV or IR responses as a new feature of Google Glass.