Combination of extraction features based on texture and colour feature for beef and pork classification

The release from proactive inhibition (PI) paradigm has been widely used as a technique for exploring the encoding dimensions of short-term memory for verbal items. PI release data have been used not only to infer particular memory codes but also to index their relative salience. In the present study, the effects of manipulating the colour (red or black) in which the stimulus material is printed were investigated in two separate experiments. No release effect was obtained in the first, where common two-syllable words were presented. In the second, where consonant trigrams were presented, a large effect was found. Since the same colour feature was manipulated in each experiment, it is argued that this pattern of results has serious implications for the use of PI release data as a technique for mapping the encoding dimensions of short-term memory.

Download Full-text

Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2016.04.009 ◽

2016 ◽

Vol 124 ◽

pp. 243-253 ◽

Cited By ~ 34

Author(s):

Chuanyuan Zhao ◽

Won Suk Lee ◽

Dongjian He

Keyword(s):

Citrus Grove ◽

Colour Feature

Download Full-text

Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)

Computers in Industry ◽

10.1016/j.compind.2018.03.014 ◽

2018 ◽

Vol 98 ◽

pp. 183-191 ◽

Cited By ~ 3

Author(s):

Edmund J. Sadgrove ◽

Greg Falzon ◽

David Miron ◽

David W. Lamb

Keyword(s):

Object Detection ◽

Real Time ◽

Extreme Learning Machine ◽

Colour Feature ◽

Learning Machine

Download Full-text

The Influence of Colour Features on Seed Identification Using Machine Vision

Notulae Scientia Biologicae ◽

10.15835/nsb819743 ◽

2016 ◽

Vol 8 (1) ◽

pp. 93-97 ◽

Cited By ~ 2

Author(s):

Sepideh ANVARKHAH ◽

Ali Davari Edalat PANAH ◽

Alireza ANVARKHAH

Keyword(s):

Neural Network ◽

Medicinal Plants ◽

Medicinal Plant ◽

Automatic System ◽

Plant Seed ◽

Average Accuracy ◽

Colour Feature ◽

Colour Features ◽

Seed Identification ◽

Total Average

Little studies have been done on morphology of medicinal plants seeds. This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification. Six colour features (means of red, green and blue colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as network input. Different combinations of colour features (one, two three, four, five and six colour features) were used to find out the most accurate combination for seed identification. Results showed that the six colour feature was the most accurate combination for seed identification (99.184% and 87.719% for training and test of neural network respectively). One colour feature had the lowest average accuracy values for seed identification (3.120% and 2.771%). In general, increasing the number of colour features increased the total average of accuracy values.

Download Full-text