The proposed algorithm could be applied in applications which require a real time segmentation or efficient identification map such as petroleum geology, petrography and NASA Mars explorations.
The analysis was done using a different time segmentation ranging from 5 to 10 groups (past Nef points) and substitution model (HKY + I) [ 51].
Unlike traditional time segmentation methods requiring the same division on a time period for all genes [ 13] (see Figure 1 (A)), the gene-specific time segmentation is considered in the present work.
The dependence of TSIF on some other parameters, such as normalized time, segmentation crack depth, convection severity as well as material constants, has also been discussed.
It uses cued invitations, time segmentation, anchoring, and facilitations to increase free disclosure of accurate details of the event (Orbach et al., 2000; Lamb et al., 2007; Dion & Cyr, 2008).
The processing speed of the proposed algorithm is very fast and real time segmentation is possible.
But, this model is limited to the analysis on the time segmentation for all genes, by simply using the replicated observations to infer biological processes' temporal coordination.
To overcome the drawbacks of traditional time segmentation and temporal biclustering methods, the causal process model (CPM) was proposed to study the biological phase transitions in a systematic way.
As a similar algorithm, Tateno et al. proposed a real-time segmentation method for RGB-D SLAM [61].
This paper presents a real-time segmentation algorithm to obtain moving objects from the H.264 compressed domain.
First, we identify specific biclusters with linear patterns, and assemble them into temporal blocks representing a group of genes and their time segmentations.
