An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images

Chagas disease is a tropical parasitic disease caused by the flagellate protozoan Trypanosoma cruzi (T. cruzi) and currently affecting large portions of the Americas. One of the standard laboratory methods to determine the presence of the parasite is by direct visualization in blood smears stained with some colorant. This method is time-consuming, requires trained microscopists and is prone to human mistakes. In this article we propose a novel algorithm for the automatic detection of T. cruzi parasites, in microscope digital images obtained from peripheral blood smears treated with Wright's stain. Our algorithm achieved a sensitivity of 0.98 and specificity of 0.85 when evaluated against a dataset of 120 test images. Experimental results show the versatility of the method for parasitemia determination.     

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.     

A cellular logic array for image processing

A cellular logic image processor employing 192 cells in a 16 by 12 hexagonal array is described. The processor has been constructed and its performance assessed. The various classes of functions which can be implemented in the cellular array are discussed and sample programs explained in detail.     

MultiSpec—a tool for multispectral–hyperspectral image data analysis

MultiSpec is a multispectral image data analysis software application. It is intended to provide a fast, easy-to-use means for analysis of multispectral image data, such as that from the Landsat, SPOT, MODIS or IKONOS series of Earth observational satellites, hyperspectral data such as that from the Airborne Visible–Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion satellite system or the data that will be produced by the next generation of Earth observational sensors. The primary purpose for the system was to make new, otherwise complex analysis tools available to the general Earth science community. It has also found use in displaying and analyzing many other types of non-space related digital imagery, such as medical image data and in K-12 and university level educational activities.MultiSpec has been implemented for both the Apple Macintosh^® and Microsoft Windows^® operating systems (OS). The effort was first begun on the Macintosh OS in 1988. The GLOBE (http://www.globe.gov) program supported the development of a subset of MultiSpec for the Windows OS in 1995. Since then most (but not all) of the features in the Macintosh OS version have been ported to the Windows OS version.Although copyrighted, MultiSpec with its documentation is distributed without charge. The Macintosh and Windows versions and documentation on its use are available from the World Wide Web at URL:http://dynamo.ecn.purdue.edu/biehl/MultiSpec/MultiSpec is copyrighted (1991–2001) by Purdue Research Foundation, West Lafayette, Indiana 47907.     

On-line recognition (OLREC): A novel approach to visual pattern recognition

A pattern recognition system has been developed which is capable of recognizing high contrast two-dimensional visual patterns and which is insensitive to the translation, rotation and size of a pattern. The unique characteristics of the system are its speed, its low cost and the fact that it is completely self-contained.

A new technique for image recognition, suitable for applications such as industrial robotics, is presented. The technique provides invariance to rotation, translation and magnification of the image. An intelligent camera system is used to input an image, to center it, to normalize it with respect to size and to convert it into polar coordinate form. The image is then integrated over r and θ to provide two orthogonal profiles. The input profiles are compared to the reference images using a threshold comparison technique designed to provide a simple hardware implementation. Rotation of the image is handled by a one-dimensional shifting of the radial profile.

Seven experiments were performed to test the system. The results of these experiments are discussed and possible improvements to the system are suggested.     

A parallel processing model for the study of possible pattern recognition mechanisms in the brain

An electronic model, based on parallel processing concept and able to identify a series of patterns without requiring sophisticated technology, is presented. A receptive part transduces the pattern images into electrical signals and a processing part identifies them. The basic element of the processing part is a “pattern detecting” unit which produces a maximal response for one specific pattern presented on the receptive matrix. A set of 11 “pattern detecting” units allows the model to recognize simple patterns under various conditions and through a neural-like organization, to identify them regardless of their position or size.     

A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video

The need for content-based access to image and video information from media archives has captured the attention of researchers in recent years. Research efforts have led to the development of methods that provide access to image and video data. These methods have their roots in pattern recognition. The methods are used to determine the similarity in the visual information content extracted from low level features. These features are then clustered for generation of database indices. This paper presents a comprehensive survey on the use of these pattern recognition methods which enable image and video retrieval by content.     

图像分割技术及其在路面开裂损坏识别中的应用

研究了一种基于神经网络的图像分割技术，并将其应用于路面识别。该方法通过分析路面的照片，根据路面图像灰度与纹理的特征来判断路面损坏的类型、面积和严重程度，得出路面状况指数，具有速度快、数据质量高、方便等优点，远远优于目前的人工调查方法。     

A comparison of selected pattern recognition functions

In this study computer simulation is used to compare selected pattern recognition functions. The Highleyman deck of 50 hand written characters provides one comparative data base. A second data base is derived from multispectral infrared sensor data taken over California's Imperial Valley. Emphasis is placed on comparing the classical minimum distance recognition functions with two new recognition functions introduced in a recent predecessor article.     

A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing

Gridding, the first step in spotted DNA microarray image processing, usually requires human intervention to achieve acceptable accuracy. We present a new algorithm for automatic gridding based on hierarchical refinement to improve the efficiency, robustness and reproducibility of microarray data analysis. This algorithm employs morphological reconstruction along with global and local rotation detection, non-parametric optimal thresholding and local fine-tuning without any human intervention. Using synthetic data and real microarray images of different sizes and with different degrees of rotation of subarrays, we demonstrate that this algorithm can detect and compensate for alignment and rotation problems to obtain reliable and robust results.     

A review of ridge counting in dermatoglyphics

This paper reviews the problem of counting ridges in a digitized fingerprint and examines an interactive software system that relieves the tedium of visual inspection and standardizes the counting procedure. The software system includes digitizer, preprocessor and counter subsystems. The preprocessor smoothes and thresholds the image. The counter defines an appropriate window around the line between two user-selected points, eliminates artifacts and counts runs of zero pixels. A preliminary experiment with a counter based on the Hough transform is also briefly discussed.     

基于Visual C＋＋的沙波测量成像系统

介绍了利用激光器、CCD相机、图像采集卡和计算机组成的沙波测量成像系统。阐述了系统的工作原理和计算机图像处理的方法。     

An experimental printed circuit board drilling system automated by pattern recognition

The two-dimensional image processing necessary for closed-loop control of automated printed circuit board drilling and a mechanical prototype capable of drilling simulated printed-circuit boards are described. A single closed-loop correction of position permits correction of errors introduced by vidicon camera positional nonlinearity, noise, irregular lighting, inaccuracy of transformational constants, and mechanical slippage. These errors are corrected to an acceptable degree and in a short enough time that automatic drilling may present a challenge to manual drilling labor costs.     

A novel Hough transform method for line detection by enhancing accumulator array

In this paper, an improved Hough transform (HT) method is proposed to robustly detect line segments in images with complicated backgrounds. The work focuses on detecting line segments of distinct lengths, totally independent of prior knowledge of the original image. Based on the characteristics of accumulation distribution obtained by conventional HT, a local operator is implemented to enhance the difference between the accumulation peaks caused by line segments and noise. Through analysis of the effect of the operator, a global threshold is obtained in the histogram of the enhanced accumulator to detect peaks. Experimental results are provided to demonstrate the efficiency and robustness of the proposed method.     

A whitening transformation for two-color blood cell images

An image enhancement technique is described for the preprocessing of stained white blood cell images which have been digitized through two different color filters from either end of the visible spectrum. Typically, corresponding picture elements (or pixels) from blood cell images digitized in this manner exhibit slight changes in grey-level due to the color filtering, but remain strongly correlated in optical density with each other. Also, color and density information are interrelated in the pixels of both of the filtered images. The technique described is a whitening transformation on the bivariate distribution of image pixels, this results in two uncorrelated axes, one relating to density and the other relating to color. The spatial effect on the two original images is to produce two separate, transformed, “color” and “density” images.     

A fast pattern matching algorithm with multi-byte search unit for high-speed network security

A signature-based intrusion detection system identifies intrusions by comparing the data traffic with known signature patterns. In this process, matching of packet strings against signature patterns is the most time-consuming step and dominates the overall system performance. Many signature-based network intrusion detection systems (NIDS), e.g., the Snort, employ one or multiple pattern matching algorithms to detect multiple attack types. So far, many pattern matching algorithms have been proposed. Most of them use single-byte standard unit for search, while a few algorithms such as the Modified Wu-Manber (MWM) algorithm use typically two-byte unit, which guarantees better performance than others even as the number of different signatures increases. Among those algorithms, the MWM algorithm has been known as the fastest pattern matching algorithm when the patterns in a rule set rarely appear in packets. However, the matching time of the MWM algorithm increases as the length of the shortest pattern in a signature group decreases.In this paper, by extending the length of the shortest pattern, we minimize the pattern matching time of the algorithm which uses multi-byte unit. We propose a new pattern matching algorithm called the L⁺¹-MWM algorithm for multi-pattern matching. The proposed algorithm minimizes the performance degradation that is originated from the dependency on the length of the shortest pattern. We show that the L⁺¹-MWM algorithm improves the performance of the MWM algorithm by as much as 20% in average under various lengths of shortest patterns and normal traffic conditions. Moreover, when the length of the shortest pattern in a rule set is less than 5, the L⁺¹-MWM algorithm shows 38.87% enhancement in average. We also conduct experiments on a real campus network and show that 12.48% enhancement is obtained in average. In addition, it is shown that the L⁺¹-MWM algorithm provides a better performance than the MWM algorithm by as much as 25% in average under various numbers of signatures and normal traffic conditions, and 20.12% enhancement in average with real on-line traffic.     

A potential discrete analog processor for pattern recognition

A pattern recognition system is described which employs normalized cross-correlation as a measure of similarity. A potential implementation is presented which is based on existing or feasibile charge-coupled device discrete analog structures. Estimates of processing times are given.     

A net-structure learning system for pattern description

This paper discusses a computer program that recognizes and describes two-dimensional patterns composed of subpatterns. The program also recognizes all patterns in a scene consisting of several patterns.

Patterns are stored in a learned hierarchical, net-structure memory. Weighted links between memory nodes represent subpattern/pattern relationships. Both short term and permanent memories are used.

Pattern recognition is accomplished with a serial heuristic search algorithm, which attempts to search memory and compute input properties efficiently. Without special processing, the program can be asked to look for all occurrences of a specified pattern in a scene.