Specific Object Recognition and Tracking using Cascade Connection of Different types of CNNs and Time-series filter

2018 ◽  
Vol 2018 (0) ◽  
pp. 1A1-L09
Author(s):  
Takaki NISHIO ◽  
Ryodo TANAKA ◽  
Buwei SHEN ◽  
Thibault BARBIE ◽  
Takeshi NISHIDA
Keyword(s):  
Time Series ◽  
Object Recognition ◽  
Cascade Connection ◽  
Different Types ◽  
Specific Object

METHODS FOR COMBINATION OF EVIDENCE IN FUNCTION-BASED 3-D OBJECT RECOGNITION

1993 ◽  
Vol 07 (03) ◽  
pp. 573-594 ◽  
Author(s):  
LOUISE STARK ◽  
LAWRENCE O. HALL ◽  
KEVIN W. BOWYER
Keyword(s):  
Object Recognition ◽  
Qualitative Reasoning ◽  
Functional Requirements ◽  
Object Category ◽  
Recognition Process ◽  
Different Types ◽  
Specific Object ◽  
Object Shapes ◽  
Representation Scheme

Representation schemes traditionally used in model-based vision are contrasted with the “function-based” representation scheme. A system which utilizes function-based representation has been implemented and tested, using the object category “chair” for case study. Function-based description is used to recognize classes and identify subclasses of known categories of objects, even if the specific object has never been encountered previously. Interpretation of the functionality of an object is accomplished through qualitative reasoning about its 3-D shape. During the recognition process, evidence is gathered as to how well the functional requirements are satisfied by the input shape. An investigation of different types of operators used in the combination of the functional evidence has been made. Three pairs of conjunctive and disjunctive operators have been used in the recognition process of more than 100 object shapes. The results are compared and differences are discussed.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

scholarly journals Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis

2019 ◽  
Author(s):  
Jaqueline Lekscha ◽  
Reik V. Donner
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Nonlinear Filtering ◽  
Ice Cores ◽  
Second Step ◽  
Different Types ◽  
Output Time ◽  
Input Time ◽  
Past Climate Variability ◽  
Input Time Series

Abstract. Analysing palaeoclimate proxy time series using windowed recurrence network analysis (wRNA) has been shown to provide valuable information on past climate variability. In turn, it has also been found that the robustness of the obtained results differs among proxies from different palaeoclimate archives. To systematically test the suitability of wRNA for studying different types of palaeoclimate proxy time series, we use the framework of forward proxy modelling. For this, we create artificial input time series with different properties and, in a first step, compare the time series properties of the input and the model output time series. In a second step, we compare the areawise significant anomalies detected using wRNA. For proxies from tree and lake archives, we find that significant anomalies present in the input time series are sometimes missed in the input time series after the nonlinear filtering by the corresponding models. For proxies from speleothems, we observe falsely identified significant anomalies that are not present in the input time series. Finally, for proxies from ice cores, the wRNA results show the best correspondence with those for the input data. Our results contribute to improve the interpretation of windowed recurrence network analysis results obtained from real-world palaeoclimate time series.

scholarly journals Object recognition using hybrid boosting method

2016 ◽  
Author(s):  
Osama Ashfaq
Keyword(s):  
Object Recognition ◽  
Covariance Matrix ◽  
Local Region ◽  
Computational Time ◽  
Local Descriptor ◽  
Alternative Approach ◽  
Different Types ◽  
Boosting Method ◽  
Mixture Of Gaussian ◽  
Diagonal Covariance Matrix

Li (ICCV, 2005) proposed a novel generative/discriminative way to combine features with different types and use them to learn labels in the images. However, the mixture of Gaussian used in Li’s paper suffers greatly from the curse of dimensionality. Here I propose an alternative approach to generate local region descriptor. I treat GMM with diagonal covariance matrix and PCA as separate features, and combine them as the local descriptor. In this way, we could reduce the computational time for mixture model greatly while score greater 90% accuracies for caltech-4 image sets.

scholarly journals Synchronisation phenomenon in three blades rotor driven by regular or chaotic oscillations

2018 ◽  
Vol 148 ◽  
pp. 06002
Author(s):  
Zofia Szmit ◽  
Jerzy Warmiński
Keyword(s):  
Time Series ◽  
Numerical Calculation ◽  
Duffing Oscillator ◽  
Equations Of Motion ◽  
Composite Beams ◽  
Chaotic Oscillations ◽  
Rotating System ◽  
Chaotic Motions ◽  
Structural Differences ◽  
Different Types

The goal of the paper is to analysed the influence of the different types of excitation on the synchronisation phenomenon in case of the rotating system composed of a rigid hub and three flexible composite beams. In the model is assumed that two blades, due to structural differences, are de-tuned. Numerical calculation are divided on two parts, firstly the rotating system is exited by a torque given by regular harmonic function, than in the second part the torque is produced by chaotic Duffing oscillator. The synchronisation phenomenon between the beams is analysed both either for regular or chaotic motions. Partial differential equations of motion are solved numerically and resonance curves, time series and Poincaré maps are presented for selected excitation torques.

AN ABDUCTIVE REASONING BASED IMAGE INTERPRETATION SYSTEM

1996 ◽  
Vol 10 (06) ◽  
pp. 613-641
Author(s):  
SANTANU CHAUDHURY ◽  
ARBIND GUPTA ◽  
GUTURU PARTHASARATHY ◽  
S. SUBRAMANIAN
Keyword(s):  
Object Recognition ◽  
Domain Knowledge ◽  
A Priori ◽  
Image Interpretation ◽  
Abductive Reasoning ◽  
Recognition Problem ◽  
Aerial Image ◽  
Interpretation System ◽  
Different Types

This paper describes an abductive reasoning based inferencing engine for image interpretation. The inferencing strategy finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori. The inferencing scheme assumes representation of the domain knowledge about the objects in terms of local and/or relational features. The inferencing system can be applied for different types of image interpretation problems like 2-D and 3-D object recognition, aerial image interpretation, etc. In this paper, we illustrate functioning of the system with the help of a 2-D object recognition problem.

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