Electronic Eye for Blind People with Object Detection and Audio Output Using Image Processing

One of the major issues faced by Blind people is detecting and recognizing an object. The objective of this project is to help the blind people because mobility of blind people is always a great problem. The mobility of blind people in unknown environment seems impossible without external help, because they don’t have any proper idea about their surroundings. So, we are developing a electronic eye which helps them to know about their surroundings and also guide them during travelling. Developing a system based on image processing using DNN algorithm which is able to labeling objects with the help of OpenCV and Tensor flow libraries and converting the labeled text in to speech and producing output in the form of audio to make the blind person aware of the object in front of him or her. The scope of this system is also measuring the distance of the object from the person and reporting the same Object detection using image processing and Machine Learning. It searches the object. We want to innovate our system the possibility of using the hearing sense to understand real time objects. For the security purpose track blind people in real time environment.

Electronic Eye Based on RGB Analysis for the Identification of Tequilas

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image’s pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers’ performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen’s kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

Wheat Bread with Grape Seeds Micropowder: Impact on Dough Rheology and Bread Properties

The current study was designed to enhance the functionality of white bread by replacement of wheat flour with different levels (1%, 2%, 5%, and 8%) of grape seeds micropowder (GSMP) with nanosized particles (10 µm). Chemical composition of GSMP, volume and sensory attributes, evaluated with the panel of evaluators and an electronic nose (e-nose) and an electronic eye (e-eye) were investigated in the tested breads. It has been found out that GSMP contained appreciable amounts of flavonoids including catechin, epicatechin, gallic acid and minerals especially, Ca, K and Mg. The data from rheological analysis showed that the addition of GSMP (mainly at 5% and 8% levels) to the wheat flour had a positive effect on dough manifesting with rheology by increased dough stability. The volume of the experimental breads (above 1% concentration) was demonstrably declined (P < 0.0001) in comparison with the control bread. Sensory rating revealed that the bread fortified with 1% GSMP was judged by the consumer panelists as the most acceptable with the highest scores for all quality attributes which was also confirmed by the data of e-nose and e-eye. Our results suggest for the first time that 1% GSMP addition appears to be a promising functional ingredient to improve bread with required qualitative and sensory properties.

Image-based phenotyping of disaggregated cells using deep learning

The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this approach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average F1-score of 95.3%, tested using separately acquired images. Our results demonstrate the potential to develop an “electronic eye” to phenotype cells directly from microscopy images.

Electronic Eye for Identification of Tequila Samples

The present work reports the potential of a bio-inspired system based on spectrometry, also known as Electronic Eye (EE), capable of detecting different Tequila samples. The reported system analyzes small volumes of Tequila Reposado and Blanco by calculating samples’ absorbances, using a low cost and portable instrumentation employing a CCD camera. The absorbance imaging method consisted of exciting samples with light passes through an 8MP camera connected to a Raspberry Pi Card. The camera’s image data are analyzed using MATLAB 2018b to be represented in Red, Green and Blue (RGB) components for each pixel, in order to get an approximation of the absorbance and the Surface Color Index (Isc) associated with sample concentration. Using the developed EE, it was possible to identify seven different kinds and brands of Tequila. From the obtained results, it was observed that the average absorbance of the Tequila Reposado was greater than the absorbance of the Tequila Blanco. Otherwise, with the Isc, the Tequila Blanco color index is lower concerning the Tequila Reposado’s. Finally, the EE allowed the identification of Tequila samples with reproducibility and repeatability.

Comparison of electronic systems with sensory analysis for the quality evaluation of parenica cheese

The applicability of electronic systems for the quality evaluation of parenica cheese was investigated in fresh smoked and unsmoked cheeses and after seven days of storage. These data were then compared with sensory evaluation results. Fresh samples had stable colour profiles determined by the electronic eye, while the differences in brown colour intensity were confirmed by sensory evaluation. A significant difference in the aroma profiles of samples was recorded by the electronic nose in samples produced in February, April, December (unsmoked cheese) and September (smoked cheese). Based on sensory analysis results using the Wilcoxon nonparametric test, a significant difference was confirmed in February (smoked cheese) and March (unsmoked cheese), when stored cheese had a stronger aroma than fresh cheese (P &lt; 0.05). The suitability of electronic nose and electronic eye for monitoring of parenica cheese quality was confirmed.