Analysis of cassava chip image characterization during drying process

Monitoring method during food processing is an indispensable activity in the industry of food processing. A digital image processing technique is one of the methods to process images into information in the form of product physical condition. This study aimed to monitor the changes in cassava chips image characteristics through the images along the drying process. The image characteristic i.e covered color, texture, and area. The images were captured by using Webcam type Logitech C525 8.0 megapixel autofocus per minute. Then, the result of these images was processed to get color data of R, G, B, H, S, I, L, a*, b* and the texture i.e. energy, homogeneity, contrast, entropy, and to identify chips size was processed by the number of pixels of the image. While the data about the mass changes along the drying process were taken per minute from a digital scale. The results of this study showed that the length of drying made the value of R, G, B, H and I decreased, but the value of S contrastively increased. The area or the number of image pixels declined dramatically in 1 hour of drying, later (after one hour of drying) the decline was almost zero.

Pattern Recognition through Dip for Wind Turbine

This paper depicts the realization of DIP (Digital Image Processing) technique for pattern recognition to identify objects in video stream. The proposed model compares the test object with standard model and identifies the missing objects in the test item. The model uses image classifier algorithm as a tool. The simulations are carried out in MATLab Simulink and various test items are compared under different morphological conditions. The model is fabricated to analyze and indicate the omitted components in wind turbine.

Digital image processing technique to measure the range of motion of the elbow

BackgroundMost photography-based arc of motion measurements require human assessment and their accuracy depends on the observer.ObjectivesTo develop a digital image processing technique (DIPT) for measuring elbow range of motion (ROM), and to assess its validity and reliability compared with standard methods.MethodsPhysiotherapists performed digital goniometer and inclinometer ROM measurements bilaterally on healthy volunteer elbows. A photographer took digital images of elbows fully extended and fully flexed 3 times using an 8-megapixel smartphone camera. Extension and flexion angles were calculated using the DIPT. Intra- and inter-rater reliability of all methods was assessed using an intraclass correlation coefficient (ICC). A paired Student's t test and Wilcoxon-signed rank test were used to assess systematic bias. A Bland–Altman plot was used to show possible range of difference between the methods.ResultsWe measured 56 elbows from 28 participants. Intra- and inter-rater ICCs of goniometer and inclinometer showed moderate-to-excellent agreement. Mean extension and flexion angles for the DIPT were greater than those for the goniometer and inclinometer measurements (P < 0.05), but the total ROMs were not significantly different (vs goniometer P = 0.32, vs inclinometer P = 0.53). Limits of agreement were 9.93°–10.05° for extension angle, 9.81°–11.7° for flexion angle, and 13.84°–15.99° for total ROMs.ConclusionsElbow ROM measurement using the current DIPT produces results comparable with goniometer and inclinometer measurements, but the difference from the standard methods was up to 15.99° for total ROM.