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Bacterial colony enumeration is an essential tool for many widely used biomedical assays. However, bacterial colony enumerating is a low throughput, time consuming and labor intensive process since there may exist hundreds or thousands of colonies on a Petri dish, and the counting process is usually manually performed by well-trained technicians. In this paper, we introduce a fully automatic yet cost-effective bacterial colony counter which can not only count but also classify colonies. Our proposed method can recognize chromatic and achromatic images and thus can deal with both color and clear medium. In addition, the proposed method is software-centered and can accept general digital camera images as its input. The counting process includes detecting dish/plate regions, identifying colonies, separating aggregated colonies, and reporting colony counts. In order to differentiate colonies of different species, the proposed counter adopts one-class Support Vector Machine (SVM) with Radial Basis Function (RBF) as the classifier. Our proposed counter demonstrates a promising performance in terms of both precision and recall, and is robust and efficient in terms of labor-and time-savings.

Bacterial colony enumeration is an essential tool for many widely used biomedical assays. However, bacterial colony enumerating is a low throughput, time consuming and labor intensive process since there might exist hundreds or thousands of colonies on a Petri dish, and the counting process is often manually performed by well-trained technicians. In this paper, we introduce a fully automatic yet cost-effective bacterial colony counter. Our proposed method can recognize chromatic and achromatic images and thus can deal with both color and clear medium. In addition, our proposed method can also accept general digital camera images as its input. The whole process includes detecting dish/plate regions, identifying colonies, separating aggregated colonies, and finally reporting consistent and accurate counting results. Our proposed counter has a promising performance in terms of both precision and recall, and is flexible and efficient in terms of labor-and timesavings.

This paper proposes a multimodal framework that clusters spam images so that ones from the same spam source/cluster are grouped together. By identifying the common sources of spam images, we can provide evidence in tracking spam gangs. For this purpose, text recognition and visual feature extraction are performed. Subsequently, a two-level clustering method is applied where images with visually similar illustrations are first grouped together. Then the clustering result from the first level is further refined using the textual clues (if applicable) contained in spam images. Our experimental results show the effectiveness of the proposed framework.

No abstract