Pengcheng Zhan

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.

An automated fish species classification and migration monitoring system

The quantification of abundance, distribution, and movement of fish is critical to ecological and environmental studies of fish communities. To properly manage, regulate, and protect migratory fisheries it is essential to accurately monitor numbers, size, and species of fish at specific fish passages during migratory seasons. Currently, all monitoring is done manually with significant time and financial constraints. An automated fish classification system will simplify data gathering and improve data accuracy. In this research, 22 images of 9 target species were recorded. The contour of each image was extracted to form a closed curve for shape analysis. A new shape analysis algorithm was developed for removing edge noise and redundant data points such as short straight lines. A curvature function analysis was used to locate critical landmark points. The fish contour segments of interest were then extracted based on these landmark points for species classification. By comparing individual contour segments to the curves in the database, accurate pattern matching was achieved.

See and avoidance behaviors for autonomous navigation

Recent advances in many multi-discipline technologies have allowed small, low-cost fixed wing unmanned air vehicles (UAV) or more complicated unmanned ground vehicles (UGV) to be a feasible solution in many scientific, civil and military applications. Cameras can be mounted on-board of the unmanned vehicles for the purpose of scientific data gathering, surveillance for law enforcement and homeland security, as well as to provide visual information to detect and avoid imminent collisions for autonomous navigation. However, most current computer vision algorithms are highly complex computationally and usually constitute the bottleneck of the guidance and control loop. In this paper, we present a novel computer vision algorithm for collision detection and time-to-impact calculation based on feature density distribution (FDD) analysis. It does not require accurate feature extraction, tracking, or estimation of focus of expansion (FOE). Under a few reasonable assumptions, by calculating the expansion rate of the FDD in space, time-to-impact can be accurately estimated. A sequence of monocular images is studied, and different features are used simultaneously in FDD analysis to show that our algorithm can achieve a fairly good accuracy in collision detection. In this paper we also discuss reactive path planning and trajectory generation techniques that can be accomplished without violating the velocity and heading rate constraints of the UAV.

Area and volume measurements of objects with irregular shapes using multiple silhouettes

Surface area and volume measurements provide important information for agriculture and food-processing applications. A machine vision system that uses a nondestructive method to measure volume and surface area of objects with irregular shapes is presented in this paper. The system first takes a series of silhouettes of the object from different directions by rotating the object at a fixed angular interval. The boundary points of each image are then extracted to construct a silhouette. A three-dimensional wire-frame model of the object can be reconstructed by integrating silhouettes obtained from different view angles. Surface area and volume can then be measured by means of surface fitting and approximation on the wire-frame model. System calibration and surface approximation were two major challenges for the design of this machine vision system. A unique centerline calibration method is introduced in this paper. Surface approximation and calculation are also discussed. Examples of applications in agriculture and food processing using this vision system for surface area measurement are included, and its accuracy is verified.

Fast surface approximation for volume and surface area measurements using distance transform

The laser triangulation technique has been widely used to obtain three-dimensional (3-D) information because of its accuracy. It is a fast, noncontact method for 3-D measurement. However, 3-D data obtained from triangulation are not dense and usually not complete for surface reconstruction, especially for objects with irregular shapes. As the result of fitting surfaces with these sparse 3-D data, inaccuracy in measuring object surface area and volume is inevitable. Accurate sur- face reconstruction from incomplete 3-D data points becomes an impor- tant step toward accurate noncontact surface area and volume measure- ments of objects moving at high speed. A novel computer vision technique combining laser triangulation and a distance transform is de- veloped to improve the 3-D measurement accuracy for objects with ir- regular shapes. The 2-D object image boundary points combined with the 3-D data obtained from laser triangulation are used to generate a 3-D wire frame. The distances from each pixel within the object boundary to its nearest boundary point are then used as the constraints for surface approximation. With this additional information from the distance trans- form, more accurate surface approximation can be achieved. This novel surface approximation technique is implemented and the measurement accuracy is compared with the accuracy using other surface interpolation techniques for the volume measurement of moving objects.

Using distance transform to solve real-time machine vision inspection problems

This paper describes novel solutions to two challenging real-time inspection tasks in machine vision. The first is fast surface approximation for volume and surface area measurements of irregularly shaped objects; the second is fast intensity gradient correction for surface inspection and evaluation of spherical objects. Both solutions apply a distance transform (DT) based on the distance of each image pixel from the object boundary. We describe both real-time machine vision inspection tasks and discuss their complexity. We show that the new solutions result in significant improvements in both accuracy and efficiency—despite the relative simplicity of the DT approach.

Shape analysis for an automatic oyster grading system

An overview of the oyster industry in the U. S. with emphasis in Virginia shows oyster grading occurs at harvest, wholesale and processing markets. Currently whole oysters, also called shellstock, are graded manually by screening and sorting based on diameter or weight. The majority of oysters harvested for the processing industry are divided into three to four main grades: small, medium, large, and selects. We have developed a shape analysis method for an automatic oyster grading system. The system first detects and removes poor quality oysters such as banana shape, broken shell, and irregular shapes. Good quality oysters move further into grades of small, medium and large. The contours of the oysters are extracted for shape analysis. Banana shape and broken shell have a specific shape flaw (or difference) compared to the ones with good quality. Global shape properties such as compactness, roughness, and elongation are suitable and useful to measure the shape flaw. Image projection area or length of the major axis measured as global properties for sizing. Incorporating a machine vision system for grading, sorting and counting oysters supports reduced operating costs. The savings produced from reducing labor, increasing accuracy in size, grade and count and providing real time accurate data for accounting and billing would contribute to the profit of the oysters industry.

Solving correspondence problems with 1D signal matching

Signal matching can be applied to many applications, such as shape matching, stereo vision, image registration, and so on. With the development of hardware, 1D signal matching can be implemented with hardware to make fast processing more feasible. This is especially important for many real-time 3D vision applications such as unmanned air vehicles and mobile robots. When lighting variance is not significant in a controlled lighting environment or when the baseline is short, images taken from two viewpoints are quite similar. It is also true for each scan line pair if the attention is drawn to 1D signal. By processing 1D signal line by line, a dense disparity map can be achieved and 3D scene can be reconstructed. In this paper, we present a robust 1D signal matching method, which combines spline representation and genetic algorithm to obtain a dense disparity map. By imposing smoothness constraint implicitly, matching parameters can be solved in terms of their spline representations by minimizing a certain cost function. Genetic algorithm can then be used to perform the optimization task. Reconstruction results of three different scene settings are shown to prove the validity of our algorithm. Due to the similarity of the problem in nature, this algorithm can be easily extended to solve image registration and motion detection problems.