Development and application of “The Two Image” method for accurate object recognition and color analysis

Alçiçek, Zayde; Balaban, Murat Ö.

doi:10.1016/j.jfoodeng.2012.01.031

Cited by 36 publications

(15 citation statements)

References 13 publications

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“…For each day, each fish was photographed 6 times, 3 photos on each side. The “two image method” (Alçiçek and Balaban ) was used without moving the fish between the 3 images. Two frontlit images were taken, 1 nonpolarized and 1 polarized.…”

Section: Methodsmentioning

confidence: 99%

Color Change of the Snapper (Pagrus auratus) and Gurnard (Chelidonichthys kumu) Skin and Eyes during Storage: Effect of Light Polarization and Contact with Ice

Balaban

Stewart

Fletcher

et al. 2014

Journal of Food Science

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Ten gurnard and 10 snapper were stored on ice. One side always contacted the ice; the other side was always exposed to air. At different intervals for up to 12 d, the fish were placed in a light box, and the images of both sides were taken using polarized and nonpolarized illumination. Image analysis resulted in average L*, a*, and b* values of skin, and average L* values of the eyes. The skin L* value of gurnard changed significantly over time while that of snapper was substantially constant. The a* and b* values of both fish decreased over time. The L* values of eyes were significantly lower for polarized images, and significantly lower for the side of fish exposed to air only. This may be a concern in quality evaluation methods such as QIM. The difference of colors between the polarized and nonpolarized images was calculated to quantify the reflection off the surface of fish. For accurate measurement of surface color and eye color, use of polarized light is recommended.

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Section: Methodsmentioning

confidence: 99%

Color Change of the Snapper (Pagrus auratus) and Gurnard (Chelidonichthys kumu) Skin and Eyes during Storage: Effect of Light Polarization and Contact with Ice

Balaban

Stewart

Fletcher

et al. 2014

Journal of Food Science

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show abstract

“…Moreover, color correlates well with other physical, chemical and sensory quality indicators in food (Esteve et al 2005;Tiwari et al 2008). In fact, color plays a major role in assessment of internal quality in food industry (Cornforth 1994;Segnini et al 1999;Abdullah et al 2001;Mancini and Hunt 2005;Alcicek and Balaban 2012). The aspect and color of the food surface are the first quality parameter evaluated by consumers and are critical in the acceptance of the product, even before entering in the mouth.…”

Section: Introductionmentioning

confidence: 98%

Effect of frying temperature and time on image characterizations of pellet snacks

2014

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The development of non-destructive methods for the evaluation of food properties has important advantages for the food processing industries. So, the aim of this study was to evaluate the effects of frying temperature (150, 170, and 190°C) and time (0.5, 1.5, 2.5, 3.5 and 4.5 min) on image properties (L*, a* and b*, fractal dimension, correlation, entropy, contrast and homogeneity) of pellet snacks. Textures were computed separately for eight channels (RGB, R, G, B, U, V, H and S). Enhancing the frying time from 0.5 min to 2.5 min increased the fractal dimension; but its increase from 2.5 min to 4.5 min could not expand the samples. Then, the highest volume of pellet snacks was observed at 2.5 min. Features derived from the image texture contained better information than color features. The best result was for U channel which showed that increasing the frying time increased the contrast, entropy and correlation. Developing the frying temperature up to 170°C decreased contrast, entropy and correlation of images; however these factors were increased when frying temperature was 190°C. These results were invert for homogeneity.

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“…If high contrast images are not obtained by transformation of the RGB channel, then another method is available. Alçiçek and Balaban () reported that a “The Two Image” method can produce object contours regardless of the background and object color. They used back‐lighted images to separate objects from background, and subsequently used the front‐lighted images for color analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Complications arise when banana hand images are segmented due to the irregular shape and uneven surfaces of a banana hand. The “The Two Image” method presented by Alçiçek and Balaban () is not a solution for segmentation of banana hand images. The final images of banana hand would be similar to Fig.…”

Section: Discussionmentioning

confidence: 99%

The Potential of Double K‐Means Clustering for Banana Image Segmentation

Dong

Liu

et al. 2013

J Food Process Engineering

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A two-step k-means clustering technique was used to segment banana images in this study. The first k-means clustering image segmentation procedure could segment the contours of a banana finger and a banana hand from the background image. Adding the second k-means clustering could quantify the damage lesions and senescent spots on the banana surface. The result of the validation test showed that the algorithm was suitable for the flaw extraction of banana finger, and the human visual evaluation of comparison among the original, manual separated and automatic segmented images of banana hand demonstrated the potential of this algorithm for banana hand segmentation. Furthermore, the influences of the other special factors, i.e., the specular reflection and the blurry phenomenon, on the segmentation of various banana images were also discussed in this study.

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Development and application of “The Two Image” method for accurate object recognition and color analysis

Cited by 36 publications

References 13 publications

Color Change of the Snapper (Pagrus auratus) and Gurnard (Chelidonichthys kumu) Skin and Eyes during Storage: Effect of Light Polarization and Contact with Ice

Color Change of the Snapper (Pagrus auratus) and Gurnard (Chelidonichthys kumu) Skin and Eyes during Storage: Effect of Light Polarization and Contact with Ice

Effect of frying temperature and time on image characterizations of pellet snacks

The Potential of Double K‐Means Clustering for Banana Image Segmentation

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