Visual identification of crystals from experimental setups (droplets) underpins the field of structural biology, and there is an increasing use of automation to capture records of crystallization trials  snapshots of the experiments over time  which are then used to identify crystals for subsequent analysis. Here we describe an algorithm that locates differences between images within an image time-course of a crystallization experiment. A user-accessible "front end" to this functionality is provided by tagging images, which have been identified as changed, and using an existing (commercial) viewing software package to display the tagged images. The existence of change appears to be a powerful tool to filter out images that do not need further human examination, as the rate of false negatives is low and the method is general. We identify significant change in a time sequence of crystallization images by using a process of image alignment, drop recognition, masking, filtering, and comparison. We propose that this process might be a powerful way of picking up changes in crystallization experiments which happen after weeks or months.

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This paper presents a novel hybrid method for tea color separation using image processing and artificial intelligence techniques. The objective of this research is to identify the stalk particles which reduce the quality of tea, without removing good particles with the intention of increasing the income of the tea manufacturing process. In order to achieve this goal it is important to identify the positions of the stalk particles. Several methods were tested with the aim of solving this problem including thresholding the image according to pixel values and a fuzzy system. The most accurate outcome was obtained by using the fuzzy system which was developed by using three input variables obtained from the RGB components of each pixel. The stalk particles were identified using threshold values which divided the output in to two sections. Although the fuzzy system gave the most accurate output, the thresholding method was found to be the fastest. However, this thresholding method has a drawback of identifying corners of good tea particles as stalks. The thresholding method provided an accuracy rate of 93% where as the fuzzy system provided an accuracy rate of 99%.

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