scholarly journals Two-Dimensional Traffic Rules and the Density Classification Problem

2016 ◽  
pp. 135-148
Author(s):  
Nazim Fatès ◽  
Irène Marcovici ◽  
Siamak Taati
Keyword(s):  
Classification Problem ◽  
Two Dimensional ◽  
Traffic Rules

scholarly journals Automated classification of blood cell neutrophils.

1977 ◽  
Vol 25 (7) ◽  
pp. 633-640 ◽  
Author(s):  
J K Mui ◽  
K S Fu ◽  
J W Bacus
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification Accuracy ◽  
Three Dimensional ◽  
Classification Problem ◽  
Differential Count ◽  
Two Dimensional ◽  
Automated Classification ◽  
Exact Shape

The classification of white blood cell neutrophils into band neutrophils (bands) and segmented neutrophils (segs) is a subproblem of the white blood cell differential count. This classification problem is not well defined for at least two reasons: (a) there are no unique quantitative definitions for bands and segs and (b) existing definitions use the shape of the nucleus as the only discriminating criterion. When cells are classified on a slide, decisions are made from the two-dimensional views of these three-dimensional cells. A problem arises because the exact shape of the nucleus becomes indeterminate when the nucleus overlaps so that the filament is hidden. To assess the importance of this problem, this paper quantitates the classification errors due to overlapped nuclei (ON). The results indicate that, using only neutrophils without ON, the classification accuracy is 89%. For neutrophils with ON, the classification accuracy is 65%. This suggests a classification strategy of first classifying neutrophils into three categories: (a) bands without ON, (b) segs without ON and (c) neutrophils with ON. Category III can then be further classified into segs and bands by other stretegies.

scholarly journals Decentralized Control for Self-driving Cars That can Freely Move on Two-dimensional Plane

2020 ◽  
Vol 5 ◽  
pp. A103
Author(s):  
Takeshi Kano ◽  
Mayuko Iwamoto ◽  
Daishin Ueyama
Keyword(s):  
Decentralized Control ◽  
Force Model ◽  
Two Dimensional ◽  
Pedestrian Flow ◽  
Social Force ◽  
Traffic Lights ◽  
Dimensional Plane ◽  
Traffic Systems ◽  
Self Driving Cars ◽  
Traffic Rules

In the current traffic rules, cars have to move along lanes and to stop at red traffic lights. However, in the future when all cars become completely driverless, these traffic rules may vanish and cars may be allowed to move freely on two-dimensional plane by avoiding others like pedestrian flow. This innovation could greatly reduce traffic jams. In this study, we propose a decentralized control scheme for future self-driving cars that can freely move on two-dimensional plane, based on the social force model widely used as the model of pedestrian flow. The performance of the proposed scheme is validated via simulation. Although this study is still conceptual and does not consider realistic details, we believe that it paves the way to developing novel traffic systems.

On the homomorphisms of sequences

1952 ◽  
Vol 48 (4) ◽  
pp. 521-532 ◽  
Author(s):  
W. H. Cockcroft
Keyword(s):  
Homotopy Theory ◽  
Free Groups ◽  
Algebraic Theory ◽  
Classification Problem ◽  
Two Dimensional ◽  
Homotopy Classification ◽  
Algebraic Classification ◽  
Crossed Modules

The algebraic classification problem with which I shall be concerned in this paper is suggested by the algebraic theory of homomorphisms of free crossed modules whose groups of operators are free groups. This theory arises in the study of the homotopy theory of two-dimensional C.W. complexes*. It is shown in (3) that the homotopy theory of such complexes, including the homotopy classification of mappings, is equivalent to this purely algebraic theory.

scholarly journals Moving objects classification via category-wise two-dimensional principal component analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Falah Alsaqre ◽  
Osama Almathkour
Keyword(s):  
Principal Component Analysis ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Feature Space ◽  
Classification Problem ◽  
Component Analysis ◽  
Classification Model ◽  
Vehicle Classification ◽  
Two Dimensional

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

RADIAL-BASED SIGNAL-PROCESSING COMBINED WITH METHODS OF MACHINE LEARNING

2013 ◽  
Vol 27 (06) ◽  
pp. 1350018
Author(s):  
F. WEICHERT ◽  
M. GASPAR ◽  
M. WAGNER
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
A Priori ◽  
Three Dimensional ◽  
Classification Problem ◽  
Support Vector ◽  
Multiscale Approach ◽  
Two Dimensional ◽  
Dimensional Classification ◽  
Novel Approach

The present paper describes a novel approach to performing feature extraction and classification in possibly layered circular structures, as seen in two-dimensional cutting planes of three-dimensional tube-shaped objects. The algorithm can therefore be used to analyze histological specimens of blood vessels as well as intravascular ultrasound (IVUS) datasets. The approach uses a radial signal-based extraction of textural features in combination with methods of machine learning to integrate a priori domain knowledge. The algorithm in principle solves a two-dimensional classification problem that is reduced to parallel viable time series analysis. A multiscale approach hereby determines a feature vector for each analysis using either a Wavelet-transform (WT) or a S-transform (ST). The classification is done by methods of machine learning — here support vector machines. A modified marching squares algorithm extracts the polygonal segments for the two-dimensional classification. The accuracy is above 80% even in datasets with a considerable quantity of artifacts, while the mean accuracy is above 90%. The benefit of the approach therefore mainly lies in its robustness, efficient calculation, and the integration of domain knowledge.

Spectrophotometric measurements of objective-prism spectra

1966 ◽  
Vol 24 ◽  
pp. 118-119
Author(s):  
Th. Schmidt-Kaler
Keyword(s):  
Progress Report ◽  
Two Dimensional ◽  
Objective Prism ◽  
Made In

I should like to give you a very condensed progress report on some spectrophotometric measurements of objective-prism spectra made in collaboration with H. Leicher at Bonn. The procedure used is almost completely automatic. The measurements are made with the help of a semi-automatic fully digitized registering microphotometer constructed by Hög-Hamburg. The reductions are carried out with the aid of a number of interconnected programmes written for the computer IBM 7090, beginning with the output of the photometer in the form of punched cards and ending with the printing-out of the final two-dimensional classifications.

Introductory paper

1966 ◽  
Vol 24 ◽  
pp. 3-5
Author(s):  
W. W. Morgan
Keyword(s):  
Line Intensity ◽  
Classification Scheme ◽  
Two Dimensional ◽  
Spectral Classification ◽  
Dimensional Array ◽  
Rr Lyrae ◽  
Metallic Line ◽  
Introductory Paper ◽  
Parameter Varying ◽  
Definition Of

1. The definition of “normal” stars in spectral classification changes with time; at the time of the publication of theYerkes Spectral Atlasthe term “normal” was applied to stars whose spectra could be fitted smoothly into a two-dimensional array. Thus, at that time, weak-lined spectra (RR Lyrae and HD 140283) would have been considered peculiar. At the present time we would tend to classify such spectra as “normal”—in a more complicated classification scheme which would have a parameter varying with metallic-line intensity within a specific spectral subdivision.

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

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