Color object detection using spatial-color joint probability functions

2006 ◽  
Vol 15 (6) ◽  
pp. 1443-1453 ◽  
Author(s):  
Jiebo Luo ◽  
D. Crandall
Keyword(s):  
Object Detection ◽  
Joint Probability ◽  
Probability Functions ◽  
Joint Probability Functions

Analysis of the Multi-Channel Epileptiform EEG Using the Markov Chains Formalism

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski
Keyword(s):  
Dynamic Behaviour ◽  
Joint Probability ◽  
Drug Effects ◽  
Anticonvulsant Drug ◽  
Channel System ◽  
Markov Network ◽  
Eeg Data ◽  
Multi Level ◽  
Joint Probability Functions ◽  
Dependent State

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.

scholarly journals Stochastic Modeling of Inter & Intra Stage Dependent Cancer Growth under Chemotherapy through Trivariate Poisson Processes

2014 ◽  
Vol 10 ◽  
pp. 31-44
Author(s):  
P. Tirupathi Rao ◽  
B.N. Naveen Kumar ◽  
J. Jayabharathiraj
Keyword(s):  
Mutant Cell ◽  
Three Dimensional ◽  
Joint Probability ◽  
Numerical Data ◽  
Malignant Cell ◽  
Process Approach ◽  
Cancer Growth ◽  
Cell Mutant ◽  
Statistical Measures ◽  
Joint Probability Functions

This study has proposed a Stochastic Model for cancer growth under chemotherapy with the assumptions of the growth, transition and loss parameters of different stages are inter and intra dependent. A trivariate Poisson process approach has been adopted for modeling the three stage cancer growth by considering the stages of cells in the tumor namely normal cell, mutant cell and malignant cell in the presence and absence of chemotherapy during time 't'. Stochastic differential equations were obtained and the three dimensional joint probability functions along with related statistical measures were derived. Model behavior was analysed through numerical data.

scholarly journals SECOND-ORDER RANDOM GRAPHS FOR MODELING SETS OF ATTRIBUTED GRAPHS AND THEIR APPLICATION TO OBJECT LEARNING AND RECOGNITION

2004 ◽  
Vol 18 (03) ◽  
pp. 375-396 ◽  
Author(s):  
ALBERTO SANFELIU ◽  
FRANCESC SERRATOSA ◽  
RENÉ ALQUÉZAR
Keyword(s):  
Random Graphs ◽  
Distance Measure ◽  
Graph Matching ◽  
Joint Probability ◽  
Second Order ◽  
Graph Representation ◽  
Expected Improvement ◽  
Probability Functions ◽  
Object Learning ◽  
Attributed Graphs

The aim of this article is to present a random graph representation, that is based on second-order relations between graph elements, for modeling sets of attributed graphs (AGs). We refer to these models as Second-Order Random Graphs (SORGs). The basic feature of SORGs is that they include both marginal probability functions of graph elements and second-order joint probability functions. This allows a more precise description of both the structural and semantic information contents in a set of AGs and, consequently, an expected improvement in graph matching and object recognition. The article presents a probabilistic formulation of SORGs that includes as particular cases the two previously proposed approaches based on random graphs, namely the First-Order Random Graphs (FORGs) and the Function-Described Graphs (FDGs). We then propose a distance measure derived from the probability of instantiating a SORG into an AG and an incremental procedure to synthesize SORGs from sequences of AGs. Finally, SORGs are shown to improve the performance of FORGs, FDGs and direct AG-to-AG matching in three experimental recognition tasks: one in which AGs are randomly generated and the other two in which AGs represent multiple views of 3D objects (either synthetic or real) that have been extracted from color images. In the last case, object learning is achieved through the synthesis of SORG models.

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Object Detection in Cluttered Range Images Using Edgel Geometry

2010 ◽  
Vol 130 (9) ◽  
pp. 1572-1580
Author(s):  
Dipankar Das ◽  
Yoshinori Kobayashi ◽  
Yoshinori Kuno
Keyword(s):  
Object Detection ◽  
Range Images
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