Hot-End Quality Control and Process Monitoring for Glass Packaging Production Using SWIR Imaging
Mehmet Can Türkeş, Nuri Erdoğan, Yiğit Berkay Uslu, Boğaziçi University
Abstract – Hot-end process monitoring and quality control systems for glass packaging industry rely mainly on thermal imaging of freshly formed, hot glass gobs emitting infrared radiation. The strength of this infrared radiation depends on various factors such as the temperature and thickness of the glass as well as the reflected radiation coming from the neighboring products. Those reflections, combined with the radiation of the glass product itself and background illumination, cause high ambient temperature and introduce significant thermal noise to the captured images, thus posing a challenge to product segmentation. In this paper, we propose a robust and real-time multi-stage segmentation method for hot-end glass product images captured by a Short-Wave Infrared (SWIR) line-scan camera under illumination and ambient temperature conditions that are not necessarily ideal. Our method combines multi-stage Otsu thresholding, which is improved by region sampling and histogram tail trimming, and marker-based watershed segmentation to obtain an initial binary mask for the product. Then, an active contour algorithm is implemented with negative distance transform of this mask as the external energy term. Resulting contours, which are observed to be robust in images even at low signal-to-noise ratios (SNR), lay the groundwork for intensity and shape-based analyses.**
Sentiment Analysis and Reporting from Text Data in Film Reviews
Egehan Eralp, Abdullah Oğuz Türk, Kerem Solmaz, Istanbul University Cerrahpaşa
Abstract – Human and communication are inseparable. For this reason, it is very important to be able to express feelings towards the other side in people and their communication. Nowadays, with the digitalizing world, people perform most of their communication in a written form in computer environment. The inability of these text data to express feelings and thoughts is a big problem in terms of communication. In order to eliminate or minimize this problem, it is very important to analyze the sentiments contained in the text data. In this context, many input representation models (Word2Vec, Doc2Vec) and text classification models (Naïve Bayes, Decision Tree, Logistic Regression, LSTM) were developed and evaluated in our study. As a result of the evaluations, the sentiment contained in the text data in the film reviews and the score corresponding to the text were estimated successfully with the most successful models. A report is presented to the user/institution along with the analysis of the user-based data generated as a result of the estimations.