2,466 research outputs found
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica
Automation of the visual inspection for quality control in production of
materials with textures (tiles, textile, leather, etc.) is not widely implemented.
A sophisticated system for image acquisition, as well as a fast and efficient
procedure for texture analysis is needed for this purpose. In this paper the
Surface Failure Detection (SFD) algorithm for quality control in ceramic
tiles production is presented. It is based on Discrete Wavelet Transform
(DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT
provides a multi-resolution analysis, which mimics behavior of a human
visual system and it extracts from the tile image the features important
for failure detection. Neural networks are used for classification of the
tiles with respect to presence of defects. Classification efficiency mainly
depends on the proper choice of the training vectors for neural networks.
For neural networks preparation we propose an automated adaptive
technique based on statistics of the tiles defects textures. This technique
enables fast adaptation of the SFD algorithm to different textures, which
is important for automated visual inspection in the production of a new
tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji
materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena
u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje
slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je
radu predstavljen algoritam za detekciju površinskih oštećenja (SFD)
u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj
transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN)
s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja
oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za
detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica
ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o
odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu
neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja
se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika
omogućava brzu adaptaciju SFD algoritma na različite teksture, što je
posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog
tipa pločica
Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica
Automation of the visual inspection for quality control in production of
materials with textures (tiles, textile, leather, etc.) is not widely implemented.
A sophisticated system for image acquisition, as well as a fast and efficient
procedure for texture analysis is needed for this purpose. In this paper the
Surface Failure Detection (SFD) algorithm for quality control in ceramic
tiles production is presented. It is based on Discrete Wavelet Transform
(DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT
provides a multi-resolution analysis, which mimics behavior of a human
visual system and it extracts from the tile image the features important
for failure detection. Neural networks are used for classification of the
tiles with respect to presence of defects. Classification efficiency mainly
depends on the proper choice of the training vectors for neural networks.
For neural networks preparation we propose an automated adaptive
technique based on statistics of the tiles defects textures. This technique
enables fast adaptation of the SFD algorithm to different textures, which
is important for automated visual inspection in the production of a new
tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji
materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena
u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje
slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je
radu predstavljen algoritam za detekciju površinskih oštećenja (SFD)
u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj
transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN)
s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja
oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za
detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica
ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o
odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu
neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja
se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika
omogućava brzu adaptaciju SFD algoritma na različite teksture, što je
posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog
tipa pločica
Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica
U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete
keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje
keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je
automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta
keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere
kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog
računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti
proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i
površinskim značajkama. Predstavljeni sustav strojnog vida analizira
geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih
pločica na temelju navedenih značajki uporabom klasifikatora s
neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju
izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost
obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za
vizualnu kontrolu kvalitete ljudskim vidom
Inteligentni sustav strojnog vida za automatiziranu kontrolu kvalitete keramičkih pločica
Intelligent system for automated visual quality control of ceramic tiles based
on machine vision is presented in this paper. The ceramic tiles production
process is almost fully and well automated in almost all production stages
with exception of quality control stage at the end. The ceramic tiles quality
is checked by using visual quality control principles where main goal is to
successfully replace man as part of production chain with an automated
machine vision system to increase production yield and decrease the
production costs. The quality of ceramic tiles depends on dimensions and
surface features. Presented automated machine vision system analyzes
those geometric and surface features and decides about tile quality by
utilizing neural network classifier. Refined methods for geometric and
surface features extraction are presented also. The efficiency of processing
algorithms and the usage of neural networks classifier as a substitution for
human visual quality control are confirmed.U članku je prikazan automatizirani sustav za vizualnu kontrolu kvalitete
keramičkih pločica uporabom strojnog računalnog vida. Proces proizvodnje
keramičkih pločica u gotovo svim svojim fazama zadovoljavajuće je
automatiziran, osim u fazi kontrole kvalitete, na kraju procesa. Kvaliteta
keramičkih pločica provjerava se i ocjenjuje postupcima vizualne provjere
kvalitete, gdje se ljudski čimbenik nastoji zamijeniti sustavom strojnog
računalnog vida u funkciji povećanja kvalitete i povećanja efikasnosti
proizvodnje. Kvaliteta keramičkih pločica definirana je dimenzijama i
površinskim značajkama. Predstavljeni sustav strojnog vida analizira
geometrijske i površinske značajke te odlučuje o kvaliteti keramičkih
pločica na temelju navedenih značajki uporabom klasifikatora s
neuronskom mrežom. Predstavljene su također i metode koje poboljšavaju
izdvajanje geometrijskih i površinskih svojstava. Potvrđena je efikasnost
obradnih algoritama i primjena neuronskog klasifikatora kao zamjene za
vizualnu kontrolu kvalitete ljudskim vidom
Representation Learning by Learning to Count
We introduce a novel method for representation learning that uses an
artificial supervision signal based on counting visual primitives. This
supervision signal is obtained from an equivariance relation, which does not
require any manual annotation. We relate transformations of images to
transformations of the representations. More specifically, we look for the
representation that satisfies such relation rather than the transformations
that match a given representation. In this paper, we use two image
transformations in the context of counting: scaling and tiling. The first
transformation exploits the fact that the number of visual primitives should be
invariant to scale. The second transformation allows us to equate the total
number of visual primitives in each tile to that in the whole image. These two
transformations are combined in one constraint and used to train a neural
network with a contrastive loss. The proposed task produces representations
that perform on par or exceed the state of the art in transfer learning
benchmarks.Comment: ICCV 2017(oral
Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering
Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture non-uniformities making their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough and expedited health monitoring of roads. In the pavement monitoring area, well known texture descriptors such as gray-level co-occurrence matrices and local binary patterns are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
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