Thermal plasma spraying is an important manufacturing technique that creates a thermal barrier coating to protect the surface underneath from wear, erosion, oxidation and corrosion. In this paper, we develop a new microstructure classification and quantification (MCQ) module that could fully automatically classify and quantify two types of microstructures, globular and interlamellar, in the top coat layer of thermal barrier coating images captured from light microscope. The MCQ module utilizes the masks obtained from the Thermal Barrier Coating Porosity Measurement (TBCPM) framework and applies the morphological filtering of erosion to the images to differentiate globular and interlamellar that are physically distinct and are eroded away at a different rate. The microstructures exhibit complicated spatial relations, for example, a segment might contain microstructures of both types. To deal with this difficulty, we build a forest of hierarchical trees of segments when eroding the images and then traverse this forest of the trees in post order following a carefully designed rule to determine if two neighboring segments should be merged. Our experimental results on the synthesized images with non-overlapping and overlapping segments show an average accuracy of 98.77% and 93.12% respectively.

Thermal barrier coating, a widely used advanced manufacturing technique, provides thermal insulation and protection of a part surface from inclement environment. In the coating process, microstructures, such as globular and interlamellar, are naturally formed in the coating layer. The amount and types of microstructures greatly influences the property and quality of the coating. This paper introduces a fully automated and innovative approach for classification of microstructures in the thermal barrier coating, which utilizes morphological image analysis and supervised machine learning techniques to differentiate between globular and interlamellar microstructures. The experimental results demonstrate the robustness and effectiveness of the proposed method.

Existing captcha solutions on the Internet are a major source of user frustration. Game captchas are an interesting and, to date, little-studied approach claiming to make captcha solving a fun activity for the users. One broad form of such captchas -- called Dynamic Cognitive Game (DCG) captchas -- challenge the user to perform a game-like cognitive task interacting with a series of dynamic images. We pursue a comprehensive analysis of a representative category of DCG captchas. We formalize, design and implement such captchas, and dissect them across: (1) fully automated attacks, (2) human-solver relay attacks, and (3) usability. Our results suggest that the studied DCG captchas exhibit high usability and, unlike other known captchas, offer some resistance to relay attacks, but they are also vulnerable to our novel dictionary-based automated attack.

Inaccurate image segmentation often has a negative impact on object-based image retrieval. Researchers have attempted to alleviate this problem by using hierarchical image representation. However, these attempts suffer from the inefficiency in building the hierarchical image representation and the high computational complexity in matching two hierarchically represented images. Existing approaches construct the hierarchical image representation in two steps. The first step is to perform segmentation at different image resolutions, and the second step is to construct a hierarchical representation of the image by associating segments from different resolutions. In this research, an innovative all-in-one run approach is proposed that concurrently performs image segmentation and hierarchical tree construction, producing a hierarchical region tree to represent the image. In addition, an efficient hierarchical region tree matching algorithm is proposed with a reasonably low time complexity and used in multiple object image retrieval. The experimental results demonstrate the efficacy and efficiency of the proposed approach.

In this paper we present a two-phase spam image clustering framework. The proposed framework performs a histogram based projective clustering on visual features in the first phase, followed by a text-based clustering in the second phase. There are several contributions in this study. First, we address the complex nature of spam image obfuscation techniques. Second, a multi-clue framework is developed to profile spam images of common spamming sources which provide evidence for tracking spam gangs. Third, projective clustering eliminates the need to choose among distance metrics for clustering analysis, while systematically exploring subspaces that correspond to clusters.

With the rapid growth of information, efficient and robust information retrieval techniques have become increasingly more important. Multiple object retrieval remains challenging due to the complex nature of this problem. The proposed research, unlike most existing works that are designed for single object retrieval or adopt heuristic multiple object matching scheme, aims at contributing to this field through the development of an image retrieval system that adopts a hierarchical region-tree representation of image, and enables effective and efficient multiple object retrieval and automatic discovery of the objects of interest through users' relevance feedback. We believe this is the first systematic attempt to formulate a comprehensive, intelligent, and interactive framework for multiple object retrieval in image databases that makes use of a hierarchical region-tree representation.

Text mining tools and algorithms are being successfully used for information extraction especially on large corpus like biomedical publications. These tools not only aid in information extraction but also in forming new theories and relationships between various fields of biomedical research. Extraction of gene-gene or gene-disease relationship is one such application. In this paper, we introduce a method to detect coexpressed genes from text, using the grammatical dependencies among the words within sentences and Dynamic Conditional Random Fields (DCRFs). Determining the coexpression relationship between and among genes can help in identifying important concepts such as the functionality of gene(s) involved, their pathogenic mechanism, and in deciphering protein-protein interactions. This work attempts to extract relevant sentences by labeling the genes involved as well as the word representing the relationship, from full-length papers collected from PubMed. The results obtained were compared with that of Support Vector Machine (SVM) and Nearest Neighbor with generalization (NNge), and have been found to outperform both.

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user's retrieval target especially when more than one object of interest is involved in the retrieval. In this paper, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in reflecting users' retrieval interests in object-based image retrieval.

Bacteria colony classification and enumeration is an essential step in clonogenic assay, which is often performed manually in many clinical research laboratories. It is known to be time consuming and labor intensive, but also error-prone since the manual counting tends to have more subjective interpretation and largely relies on persistent practice, especially when a large amount of colonies exist in the plate. In this demonstration, we introduce a fully automatic counter for bacteria colony enumeration and classification. The proposed counter accepts digital camera images as its input and performs colony enumeration and classification based on image mining. The proposed counter shows a robust performance in terms of both precision and recall, and is efficient in terms of labor- and time-savings.

Gridding and spot segmentation are two critical steps in microarray gene expression data analysis. However, the problems of noise contamination and donut-shaped spots often make signal extraction process a laborintensive task. In this paper, we propose a three-step method for automatic gridding and spot segmentation. The method starts with a background removal and noise eliminating step, and then proceeds in two steps. The first step applies a fully unsupervised method to extract blocks and grids from the cleaned data. The second step applies a simple, progressive spot segmentation method to deal with inner holes and noise in spots. We tested its performance on real microarray images against a widely used software GenePix. Our results show that the proposed method deals effectively with poor-conditioned microarray images in both gridding and spot segmentation.