Dah-Jye Lee

Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining

For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can convert the ubiquitous nearest neighbor classifier into an anytime algorithm that can produce an instant classification, or if given the luxury of additional time, can utilize the extra time to increase classification accuracy. We demonstrate the utility of our approach with a comprehensive set of experiments on data from diverse domains.

Review of stereo vision algorithms and their suitability for resource-limited systems

A significant amount of research in the field of stereo vision has been published in the past decade. Considerable progress has been made in improving accuracy of results as well as achieving real-time performance in obtaining those results. This work provides a comprehensive review of stereo vision algorithms with specific emphasis on real-time performance to identify those suitable for resource-limited systems. An attempt has been made to compile and present accuracy and runtime performance data for all stereo vision algorithms developed in the past decade. Algorithms are grouped into three categories: (1) those that have published results of real-time or near real-time performance on standard processors, (2) those that have real-time performance on specialized hardware (i.e. GPU, FPGA, DSP, ASIC), and (3) those that have not been shown to obtain near real-time performance. This review is intended to aid those seeking algorithms suitable for real-time implementation on resource-limited systems, and to encourage further research and development of the same by providing a snapshot of the status quo.

A Spine X-Ray Image Retrieval System Using Partial Shape Matching

In recent years, there has been a rapid increase in the size and number of medical image collections. Thus, the development of appropriate methods for medical information retrieval is especially important. In a large collection of spine X-ray images, maintained by the National Library of Medicine, vertebral boundary shape has been determined to be relevant to pathology of interest. This paper presents an innovative partial shape matching (PSM) technique using dynamic programming (DP) for the retrieval of spine X-ray images. The improved version of this technique called corner-guided DP is introduced. It uses nine landmark boundary points for DP search and improves matching speed by approximately 10 times compared to traditional DP. The retrieval accuracy and processing speed of the retrieval system based on the new corner-guided PSM method are evaluated and included in this paper.

Contour matching for a fish recognition and migration-monitoring system

Fish migration is being monitored year round to provide valuable information for the study of behavioral responses of fish to environmental variations. However, currently all monitoring is done by human observers. An automatic fish recognition and migration monitoring system is more efficient and can provide more accurate data. Such a system includes automatic fish image acquisition, contour extraction, fish categorization, and data storage. Shape is a very important characteristic and shape analysis and shape matching are studied for fish recognition. Previous work focused on finding critical landmark points on fish shape using curvature function analysis. Fish recognition based on landmark points has shown satisfying results. However, the main difficulty of this approach is that landmark points sometimes cannot be located very accurately. Whole shape matching is used for fish recognition in this paper. Several shape descriptors, such as Fourier descriptors, polygon approximation and line segments, are tested. A power cepstrum technique has been developed in order to improve the categorization speed using contours represented in tangent space with normalized length. Design and integration including image acquisition, contour extraction and fish categorization are discussed in this paper. Fish categorization results based on shape analysis and shape matching are also included.

Vision Aided Stabilization and the Development of a Quad-Rotor Micro UAV

Micro Unmanned Air Vehicles are well suited for a wide variety of applications in agriculture, homeland security, military, search and rescue, and surveillance. In response to these opportunities, a quad-rotor micro UAV has been developed at the Robotic Vision Lab at Brigham Young University. The quad-rotor UAV uses a custom, low-power FPGA platform to perform computationally intensive vision processing tasks on board the vehicle, eliminating the need for wireless tethers and computational support on ground stations. Drift stabilization of the UAV has been implemented using Harris feature detection and template matching running in real-time in hardware on the on-board FPGA platform, allowing the quad-rotor to maintain a stable and almost drift-free hover without human intervention.

FPGA-based Real-time Optical Flow Algorithm Design and Implementation

Optical flow algorithms are difficult to apply to robotic vision applications in practice because of their extremely high computational and frame rate requirements. In most cases, traditional general purpose processors and sequentially executed software cannot compute optical flow in real time. In this paper, a tensor-based optical flow algorithm is developed and implemented using field programmable gate array (FPGA) technology. The resulting algorithm is significantly more accurate than previously published FPGA results and was specifically developed to be implemented using a pipelined hardware structure. The design can process 640 × 480 images at 64 fps, which is fast enough for most real-time robot navigation applications. This design has low resource requirements, making it easier to fit into small embedded systems. Error analysis on a synthetic image sequence is given to show its effectiveness. The algorithm is also tested on a real image sequence to show its robustness and limitations. The resulting limitations are analyzed and an improved scheme is then proposed. It is then shown that the performance of the design could be substantially improved with sufficient hardware resources.

Similarity measurement using polygon curve representation and Fourier descriptors for shape-based vertebral image retrieval

Shape-based retrieval of vertebral x-ray images is a challenging task because of high similarity among the vertebral shapes. Most techniques, such as global shape properties or scale space filtering, lose or fail to detect local details. As the result of this shortfall, the number of retrieved images is so high that the retrieval result is sometimes meaningless. To retrieve a small number of best matched images, shape representation and similarity measurement techniques must distinguish shapes with minor variations. The main challenge of shape-based retrieval is to define a shape representation method that is invariant with respect to rotation, translation, scaling, and the curve starting point shift. In this research, a polygon curve evolution technique was developed for smoothing polygon curves and reducing the number of data points while preserving the significant pathology of the shape. The x and y coordinates of the simplified boundary points were then converted into a bend angle versus normalized curvature length function to represent the curve. Finally, the Fourier descriptors of the shape representation were calculated for similarity measurement. This approach meets the invariance requirements and has been proved to be efficient and accurate.

Real-Time Optical Flow Calculations on FPGA and GPU Architectures: A Comparison Study

FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing a variety of computations. Applications successfully implemented on FPGAs have typically contained high levels of parallelism and have often used simple statically-scheduled control and modest arithmetic. Recently introduced computing devices such as coarse grain reconfigurable arrays, multi-core processors, and graphical processing units (GPUs) promise to significantly change the computational landscape for the implementation of high-speed real-time computing tasks. One reason for this is that these architectures take advantage of many of the same application characteristics that fit well on FPGAs. One real-time computing task, optical flow, is difficult to apply in robotic vision applications in practice because of its high computational and data rate requirements, and so is a good candidate for implementation on FPGAs and other custom computing architectures. In this paper, a tensor-based optical flow algorithm is implemented on both an FPGA and a GPU and the two implementations discussed. The two implementations had similar performance, but with the FPGA implementation requiring 12× more development time. Other comparison data for these two technologies is then given for three additional applications taken from a MIMO digital communication system design, providing additional examples of the relative capabilities of these two technologies.

Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping

Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept.

A feature construction method for general object recognition

This paper presents a novel approach for object detection using a feature construction method called Evolution-COnstructed (ECO) features. Most other object recognition approaches rely on human experts to construct features. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, and no limitations to certain types of image sources. We show in our experiments that ECO features perform better or comparable with hand-crafted state-of-the-art object recognition algorithms. An analysis is given of ECO features which includes a visualization of ECO features and improvements made to the algorithm.