A Novel Image Semantic Block Clustering Method based on Artificial Visual Cortical Responding Model

2007 ◽  
Author(s):  
Zhiping. Xu ◽  
Shiyong Zhang ◽  
Shengxiang Ma
Keyword(s):  
Clustering Method ◽  
Block Clustering ◽  
Visual Cortical

Block-Based Change Detection in the Presence of Ambient Illumination Variations

2005 ◽  
Vol 9 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Theodoros Alexandropoulos ◽  
◽  
Vassili Loumos ◽  
Eleftherios Kayafas
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Similar Magnitude ◽  
Ambient Illumination ◽  
Clustering Method ◽  
Illumination Variations ◽  
Illumination Changes ◽  
Block Clustering ◽  
Block Based

The efficiency of change detection methods, in terms of content discrimination, is degraded by the presence of noise. Furthermore, illumination changes tend to cause further degradation in change detection accuracy by swamping content changes of similar magnitude. This fact imposes the application of a method which detects illumination variations in the presence of occlusions. This paper proposes the application of a block clustering method which aims to separate content changes from noise-level changes. The algorithm is performed in conjunction with a brightness normalization technique for the correction of ambient illumination variations.

Physical Resource Block clustering method for an OFDMA cognitive femtocell system

2014 ◽  
Vol 11 ◽  
pp. 67-77 ◽  
Author(s):  
Giulio Bartoli ◽  
Romano Fantacci ◽  
Dania Marabissi ◽  
Marco Pucci
Keyword(s):  
Resource Block ◽  
Clustering Method ◽  
Physical Resource ◽  
Physical Resource Block ◽  
Block Clustering ◽  
Cognitive Femtocell

Ultrastructure of developing visual cortical synapses in the cat superior colliculus

1991 ◽  
Vol 49 ◽  
pp. 156-157
Author(s):  
Caroline A. Miller ◽  
Laura L. Bruce
Keyword(s):  
Superior Colliculus ◽  
Phosphate Buffer ◽  
Receptive Fields ◽  
Visual Cortical Area ◽  
Cortical Synapses ◽  
Visual Cortical ◽  
And Function ◽  
Phosphate Buffered Saline ◽  
The Brain ◽  
Cat Superior Colliculus

The first visual cortical axons arrive in the cat superior colliculus by the time of birth. Adultlike receptive fields develop slowly over several weeks following birth. The developing cortical axons go through a sequence of changes before acquiring their adultlike morphology and function. To determine how these axons interact with neurons in the colliculus, cortico-collicular axons were labeled with biocytin (an anterograde neuronal tracer) and studied with electron microscopy.Deeply anesthetized animals received 200-500 nl injections of biocytin (Sigma; 5% in phosphate buffer) in the lateral suprasylvian visual cortical area. After a 24 hr survival time, the animals were deeply anesthetized and perfused with 0.9% phosphate buffered saline followed by fixation with a solution of 1.25% glutaraldehyde and 1.0% paraformaldehyde in 0.1M phosphate buffer. The brain was sectioned transversely on a vibratome at 50 μm. The tissue was processed immediately to visualize the biocytin.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

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