Instagram Image Filtration with Computer Vision

Instagram is one of the famous and fast-growing media sharing platforms. Instagram allows users to share photos and videos with followers. There are plenty of ways to search for images on Instagram, but one of the most familiar ways is ’hashtag.’ Hashtag search enables the users to find the precise search result on Instagram. However, there are no rules for using the hashtag; that is why it often does not match the uploaded image, and for this reason, Users are unable to find the relevant search results. This research aims to filter any human face images on search results based on hashtags on Instagram. Our study extends the author’s [2] work by implementing image processing techniques that detect human faces and separate the identified images on search results based on hashtags using the face detection technique.

Spectral Imaging Experiments with Various Optical Schemes Based on the Same AOTF

Spectral image filtration by means of acousto-optical tunable filters (AOTFs) has multiple applications. For its implementation, a few different optical schemes are in use. They differ in image quality, number of coupling components, dimensions and alignment complexity. To choose the optical system of AOTF-based spectral imager properly, many factors have to be considered. Though various schemes of acousto-optic (AO) filtration have been tested and discussed, their comparative analysis has not been reported up to now. In this study, we assembled the four most popular schemes (confocal, collimating, tandem and double-path) using the same AO cells and experimentally compared their main features. Depending on the application, each scheme may be the basis of compact cost-effective spectral imaging devices.