2 edition of Texture discrimination research using an IBM PC found in the catalog.
Texture discrimination research using an IBM PC
George A. Geri
by Air Force Systems Command, Air Force Human Resources Laboratory in Brooks Air Force Base, Tex
Written in English
Distributed to depository libraries in microfiche.
|Statement||George A. Geri, Christopher D. Voltz.|
|Series||AFHRL-TR -- 89-43|
|Contributions||Voltz, Christopher D., Air Force Human Resources Laboratory.|
|The Physical Object|
Sensing in the dark is a useful but challenging task both for biological agents and robots. Rats and mice use whiskers for the active exploration of their environment. We have built a robot equipped with two active whisker arrays and tested whether they can provide reliable texture information. While it is relatively easy to classify data recorded at a specified distance and angle to the. IBM denied the claim in an emailed statement: “We make our decisions based on skills, not age," a spokesman writes. "This former employee’s claims are false and inaccurate, and we will defend.
Whisker-mediated texture discrimination has many lessons to teach neuroscientists about sensor mechanisms, central encoding, and the transformation of sensory representations to behavioral output. It is not surprising, then, that whisker touch has become a focus of engineers who look to biology for inspiration in their attempt to endow robots. A new class-action lawsuit accuses Research Triangle Park’s largest employer, IBM, of “systemically laying off older employees in order to build a younger workforce.”.
Matching the texture would be more difficult. If the spots are very small & you wouldn't notice the missing texture from just looking, I'd probably just skip it. But there are techniques to get textures in paint using special paints, or additives, or using sponges or rags or brushes or combs. For working with the GEOID06 model, two FORTRAN programs are provided as source code ( and ) and as compiled executables for both Sun Unix (INTG and XNTG) and IBM PC ( and ) platforms. INTG will interpolate geoid heights from data files using user-provided coordinates.
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Texture discrimination research using an IBM PC. [George A Geri; Christopher D Voltz; University of Dayton. Research Institute.; Air Force Human Resources Laboratory. Operations Training Division.]. TEXTURE DISCRIMINATION RESEARCH USING AN IBM PC I. INTRODUCTION The program GEXPT2.C, which is described here, has been used to conduct form processing research using an IBM PC/AT in conjunction with a commercial video-controller card (Image Action PCVision or Data Translation Model DT) and a standard raster monitor.
The texture-defined letter programme will run only on an IBM PC type or clone with at least 10 MByte of memory and with a ATI VGA "Wonder XL" graphics card.
The motion-defined letter pro- gramme also requires the same graphics card, but needs only KByte of memory. REFERENCES Beck, JSutler, A. & Irvy, R. ().Cited by: The task of texture segmentation is to identify image curves that separate different textures. To segment textured images, one must first be able to discriminate textures.
A segmentation algorithm performs texture-discrimination tests at densely spaced image positions, then interprets the results to localize edges.
This article focuses on the first stage, texture discrimination. Julesz () proposed a theory of texture discrimination, based on an order statistics principle, which states that no two textures can be perceptually discriminated if they have identical second.
Recent developments in modeling image discrimination by feature analytic and frequency selective methods are discussed.
Some issues relating to the design of two-dimensional spatial frequency filters are developed Texture discrimination research using an IBM PC book the context of two experiments on texture discrimination using artificial and naturally occurring textures.
Results of these experiments indicate that, given an adequately. Texture analysis is a technique used for the quantification of image texture. It has been successfully used in many fields, and in the past years it has been applied in magnetic resonance imaging (MRI) as a computer-aided diagnostic tool.
Quantification of the intrinsic heterogeneity of different tissues and lesions is necessary as they are usually imperceptible to the human eye. Texture Discrimination – How I Really Feel About It It’s been awhile since I’ve written what I like to call a think piece.
This topic is something that’s been subconsciously on my mind for years, but only recently did I realise what exactly it was that bothered me about it.
A computer (IBM PC clone) controlled two bit digital to analogue converters (Cambridge Research Systems model D) whose outputs drove the x- and y-axes of an electrostatically driven large-screen (40 cm horizontal × 31 cm vertical) monitor with P31 phosphor (Hewlett-Packard model A), thus allow × 65, screen locations to.
Shape Discrimination Research Using an IBM PC by Christopher D. Voltz and Dr. George A. Geri. Air Force Human Resources Laboratory Final Technical Report for Period Oct. to July Texture Discrimination Research Using an IBM PC by Dr.
George A. Geri and Christopher D. Voltz. texture classiﬁcation and conducted a comparative study of various texture measures with nonparametric classiﬁcation based on distrib utions of single features or joint pairs of fea-tures.
Their experiments showed that a very good texture discrimination can be obtained by simple texture measures,like absolute gray level differences and local.
A texture discrimination task (TDT) was employed that has been used in a number of visual perceptual learning studies –. The stimuli, white (54 cd/m 2) on a uniform black background, were displayed on a inch gamma linearized CRT monitor (× pixels at 85 Hz) at a cm viewing distance.
All of the stimuli were generated by a MATLAB. A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier.
The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is. Human Texture Perception. Textures are important visual cues about surface property, scenic depth, surface orientation, and etc.
Amazingly, human vision system utilises the information effectively in interpreting the scene and performs very efficient texture discrimination and segmentation. A patch of texture A embedded in a background of texture B, is sometimes detected more easily than a same size patch of texture B embedded in a background of texture A.
This phenomenon is called texture discrimination asymmetry. The perceptual texture performance asymmetry was investigated by manipulating the orientation variation of lines, the gradual change of L and + micropatterns and the.
Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research.
The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial. IBM Research and The Michael J. Fox Foundation Develop Modeling Methodology to Help Understand Parkinson’s Disease Using Machine Learning.
In collaboration with The Michael J. Fox Foundation for Parkinson’s Research, our team of researchers at IBM is aiming to develop improved disease progression models that can help clinicians understand how the disease progresses in. A computerized sensory analysis system, based on an IBM-PC compatible local area network, was developed.
Panellist input was simplified through the use of a light pen and interactive questionnaire program. The system was integrated to allow preparation of descriptive, hedonic, triangle, structured and unstructured ballots; registration of.
Margaret Vincent, Hao Tang, Wai Khoo, Zhigang Zhu, Tony Ro, Shape Discrimination Using the Tongue: Implications for a Visual-to-Tactile Sensory Substitution Device, Multisensory Research, /, 29, 8, (), ().Texture discrimination by GLCM features on TCSDI images Begin if energy>= && energytexture image is car ”) else print (“The texture image is Elephant”) End Table GLCM values w90 0, for TDSDI-CM of Car and Elephant images change values Texture discrimination by GLCM features on.texture.
Multiple linear regression analyses revealed that hair texture predicted hiring decision, after controlling for relevant demographic variables. Participant education and vignette hair texture influenced hiring decisions, accounting for % of the variance.