In this study, a novel image proces

In this study, a novel image processing approach based on the combination of Radon transform, pseudo
Fourier–Mellin transform, and Fourier spectrum-based fractal dimension was proposed to exclude the
undesirable effects of samples structural and positional changes on the image texture features. Images
were obtained using a dryer equipped with perpendicular dual-view computer vision system (CVS) at
hot air drying (HAD) temperatures of 50_90 _C and superheated steam drying (SSD) temperatures of
110_120 _C. Three drying medium velocities in the range of 1_2 m/s were adjusted for each drying temperature.
Unlike the drying medium velocity, the drying medium temperature had a significant effect on
the visual texture parameters of the shrimp batch. The zero-order and fractional conversion models along
with a modified Arrhenius model were found as the best models for explaining the kinetics of the visual
texture features and the temperature dependency of their constants, respectively. Eventually, the linear
and cubic regression models satisfactorily correlated the image texture features with the moisture ratio
and geometrical attributes of the samples, respectively.

ผลลัพธ์ (ไทย) 3:

ในการศึกษานี้ เป็นนวนิยายการประมวลผลภาพวิธีการขึ้นอยู่กับการรวมกันของการแปลงเรดอน , หลอก
ฟูเรียร์ฟูเรียร์สำหรับการแปลงเมลลิน และมิติเศษส่วนคลื่นความถี่ตามเสนอแยกผลที่ไม่พึงประสงค์ของการเปลี่ยนแปลงโครงสร้างตำแหน่งตัวอย่างบนพื้นผิวรูปภาพคุณสมบัติ ภาพ
were obtained using a dryer equipped with perpendicular dual-view computer vision system (CVS) at
hot air drying (HAD) temperatures of 50_90 _C and superheated steam drying (SSD) temperatures of
110_120 _C. Three drying medium velocities in the range of 1_2 m/s were adjusted for each drying temperature.
Unlike the drying medium velocity, the drying medium temperature had a significant effect on
the visual texture parameters of the shrimp batch. The zero-order and fractional conversion models along
with a modified Arrhenius model were found as the best models for explaining the kinetics of the visual
texture features and the temperature dependency of their constants, respectively. Eventually, the linear
and cubic regression models satisfactorily correlated the image texture features with the moisture ratio
and geometrical attributes of the samples, respectively.