Robert Paasch

Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects

This paper describes a computer vision approach to automated rapid-throughput taxonomic identification of stonefly larvae. The long-term objective of this research is to develop a cost-effective method for environmental monitoring based on automated identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) identification of regions of interest, (b) representation of those regions as SIFT vectors (Lowe, in Int J Comput Vis 60(2):91–110, 2004) (c) classification of the SIFT vectors into learned “features” to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed principal curvature-based region (PCBR) detector. This detector finds stable regions of high curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step. We evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria, are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doro-neuria) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.

Dictionary-free categorization of very similar objects via stacked evidence trees

Current work in object categorization discriminates among objects that typically possess gross differences which are readily apparent. However, many applications require making much finer distinctions. We address an insect categorization problem that is so challenging that even trained human experts cannot readily categorize images of insects considered in this paper. The state of the art that uses visual dictionaries, when applied to this problem, yields mediocre results (16.1% error). Three possible explanations for this are (a) the dictionaries are unsupervised, (b) the dictionaries lose the detailed information contained in each keypoint, and (c) these methods rely on hand-engineered decisions about dictionary size. This paper presents a novel, dictionary-free methodology. A random forest of trees is first trained to predict the class of an image based on individual keypoint descriptors. A unique aspect of these trees is that they do not make decisions but instead merely record evidence-i.e., the number of descriptors from training examples of each category that reached each leaf of the tree. We provide a mathematical model showing that voting evidence is better than voting decisions. To categorize a new image, descriptors for all detected keypoints are “dropped” through the trees, and the evidence at each leaf is summed to obtain an overall evidence vector. This is then sent to a second-level classifier to make the categorization decision. We achieve excellent performance (6.4% error) on the 9-class STONEFLY9 data set. Also, our method achieves an average AUC of 0.921 on the PASCAL06 VOC, which places it fifth out of 21 methods reported in the literature and demonstrates that the method also works well for generic object categorization.

Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks

The application of Bayesian networks for monitoring and diagnosis of a multistage manufacturing process is described. Bayesian network “part models” were designed to represent individual parts in-process. These were combined to form a “process model,” a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the “process network.” Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given.

Automated processing and identification of benthic invertebrate samples

Abstract We present a visually based method for the taxonomic identification of benthic invertebrates that automates image capture, image processing, and specimen classification. The BugID system automatically positions and images specimens with minimal user input. Images are then processed with interest operators (machine-learning algorithms for locating informative visual regions) to identify informative pattern features, and this information is used to train a classifier algorithm. Naïve Bayes modeling of stacked decision trees is used to determine whether a specimen is an unknown distractor (taxon not in the training data set) or one of the species in the training set. When tested on images from 9 larval stonefly taxa, BugID correctly identified 94.5% of images, even though small or damaged specimens were included in testing. When distractor taxa (10 common invertebrates not present in the training set) were included to make classification more challenging, overall accuracy decreased but generally was close to 90%. At the equal error rate (EER), 89.5% of stonefly images were correctly classified and the accuracy of nonrejected stoneflies increased to 96.4%, a result suggesting that many difficult-to-identify or poorly imaged stonefly specimens had been rejected prior to classification. BugID is the first system of its kind that allows users to select thresholds for rejection depending on the required use. Rejected images of distractor taxa or difficult specimens can be identified later by a taxonomic expert, and new taxa ultimately can be incorporated into the training set of known taxa. BugID has several advantages over other automated insect classification systems, including automated handling of specimens, the ability to isolate nontarget and novel species, and the ability to identify specimens across different stages of larval development.

Comparison of Direct-Drive Power Takeoff Systems for Ocean Wave Energy Applications

This paper presents a comprehensive power takeoff (PTO) analysis program conducted as a collaborative research effort between Columbia Power Technologies, Inc., Oregon State University (OSU), and the U.S. Navy. Eighteen different direct-drive technologies were evaluated analytically and down-selected to five promising designs. Each of the five prototypes was simulated, modeled in SolidWorks, and built at the 200-W peak level and tested on OSUs wave energy linear test bed. The simulations were validated with the 200-W experimental results and then scaled up to 100 kW, with full 100-kW designs including costs, maintenance, operations, etc., to estimate the cost of energy (COE) for each PTO buoy system at utility scale.

Large-scale ocean wave energy plant modeling

In order for wave energy conversion to be a commercially viable technology, wave energy researchers, developers, investors and utilities need an estimate of a wave energy converters (WEC) power output at a potential installation site. The wind industry has developed generic turbine models that capture the general dynamics of large-scale proprietary wind turbine designs in order to estimate a turbines power output for a given wind climate. Similar generic models need to be developed for WECs. Current WEC deigns vary significantly in design and technology. The focus of this paper is on developing a generic model structure for one of the prominent WEC designs, the two body point absorber. The model structure is developed by using time domain equations of motion (EOM) to define systems and subsystems as well as their corresponding inputs and outputs. The generic model structure is then extended by developing a hydraulic power take-off (PTO) system model.

Automated Insect Identification through Concatenated Histograms of Local Appearance Features

This paper describes a fully automated stone fly-larvae classification system using a local features approach. It compares the three region detectors employed by the system: the Hessian-affine detector, the Kadir entropy detector and a new detector we have developed called the principal curvature based region detector (PCBR). It introduces a concatenated feature histogram (CFH) methodology that uses histograms of local region descriptors as feature vectors for classification and compares the results using this methodology to that of Opelt [Opelt, A, et.al., 2006.] on three stonefly identification tasks. Our results indicate that the PCBR detector outperforms the other two detectors on the most difficult discrimination task and that the use of all three detectors outperforms any other configuration. The CFH methodology also outperforms the Opelt methodology in these tasks

AN AQUATIC INSECT IMAGING SYSTEM TO AUTOMATE INSECT CLASSIFICATION

Population counts of aquatic insects are a valuable tool for monitoring the water quality of rivers and streams. However, the handling of samples in the lab for species identification is time consuming and requires specially trained experts. An aquatic insect imaging system was designed as part of a system to automate aquatic insect classification and was tested using several species and size classes of stonefly (Plecoptera). The system uses ethanol to transport specimens via a transparent rectangular tube to a digital camera. A small jet is used to position and reorient the specimens so that sufficient pictures can be taken to classify them with pattern recognition. A mirror system is used to provide a split set of images 90° apart. The system is evaluated with respect to engineering requirements developed during the research, including image quality, specimen handling, and system usability.

A structural and behavioral reasoning system for diagnosing large-scale systems

A real-time diagnosis system for the detector used in the heavy ion superconducting spectrometer (HISS) experiments is examined. The system is multileveled, combining a single monitoring level based on statistical methods with two model-based diagnostic levels, one operating on structural information and the other using both structural and behavioral models. The model-based approach mitigates the combinatorial complexity of rule-based systems, and the multilevel nature of the architecture mimics the way an expert would diagnose a large-scale system; it takes a broad, shallow look at all the system data and components, produces a limited set of subject components, and then looks at that limited set plus a reduced set of data to identify the most probable suspects.<<ETX>>

Application of a bayesian network to integrated circuit tester diagnosis

Research efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem-the diagnosis of integrated circuit (IC) testing machines-are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic components interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved