Grayscale Morphological Filters Research Articles

Bullet fast matching based on single point laser detection

Bullet trace detection’s main task is determining the type of bullet and the identity of the gun that fired it. In order to solve conventional bullet trace matching method problems, a bullet fast matching method based on single point laser detection is proposed. First, adaptive control of the bullet center position and cylinder axis were performed. Then, a laser displacement sensor was implemented to perform a 360° detection in order to trace rifling on the bullet surface in the circumferential direction; grayscale morphological filtering was implemented in order to de-noise detection data, and the Pearson correlation coefficient was implemented in order to perform the trace similarity matching calculation, thereby achieving a fast bullet trace matching. Moreover, the algorithm’s effectiveness was verified via practical testing data.

Computer-aided pulmonary image analysis in small animal models.

To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models. The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors' system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters. 133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient > 0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT'09 data set. The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

Moving Shadow Detection Based on Multi-feature Analysis and Gray-scale Morphological Filtering

For many applications in video surveillance systems, shadow detection is a very important step for enhancing the accuracy of moving object segmentation. This paper proposes a robust multi-feature approach which makes integrated use of intensity, chromacity and texture for shadow elimination. After motion detection, foreground pixels are evaluated by several weak classifiers in a coarse-to-fine manner. In each level, the outputs of the weak classifiers are accumulated and formed as an evidence map. Then shadows are eliminated using a gray-scale morphological filtering. Results on various real surveillance videos show the effectiveness and robustness of the algorithm.

New class of Grayscale Morphological Filter to enhance infrared building target

Nowadays, Grayscale Morphological Filter (GMF) is a well-founded non-linear filtering for image processing. Its geometry-oriented nature provides a strong framework for addressing shape characteristics such as size, connectivity, and others, which are not easily accessed by the traditional linear approach. Therefore, grayscale morphological filters have been widely used to enhance and detect the infrared small target, but it is rarely applied to the domain of building target enhancement in dense urban sites. Additionally, its ability to build target enhancements is weak. If the Signal-to-Noise-Ratio (SNR) of the image is low or the simarilty-pattem of the image is high or the image has been contaminated by heavy structured clutters, the traditional grayscale morphological filter may decrease image quality, leading to the loss of target and an increase in false alarms. Therefore, the primary operations of the traditional grayscale morphology, such as erosion, dilatation, opening, closing, and top-hat operations, have drawbacks of creating artificial patterns and distorting or removing significant details. Although they may perform well in some cases, these methods do not really improve the enhancement ability of the grayscale morphological filter.

Denoising Method of Wood Defect Images Based on Wavelet Packet Combined with Gray-Scale Morphological Filtering

Excessive noise was joined with the acquiring wood defect images process in wood non-destructive testing. The image quality was affected. Wavelet packet transform could decompose high frequency and low frequency of signal at the same time. There were characteristics of open and closed in Gray-scale Morphology. Denoising method of wood defect images based on wavelet packet combined with gray-scale morphological filtering was proposed. Compared with the traditional denoising method, the method received better Peak Signal to Noise Ratio (PSNR) and visual effect. Experimental results verified the feasibility and effectiveness of this method.

Level-set segmentation of pulmonary nodules in megavolt electronic portal images using a CT prior

Pulmonary nodules present unique problems during radiation treatment due to nodule position uncertainty that is caused by respiration. The radiation field has to be enlarged to account for nodule motion during treatment. The purpose of this work is to provide a method of locating a pulmonary nodule in a megavolt portal image that can be used to reduce the internal target volume (ITV) during radiation therapy. A reduction in the ITV would result in a decrease in radiation toxicity to healthy tissue. Eight patients with nonsmall cell lung cancer were used in this study. CT scans that include the pulmonary nodule were captured with a GE Healthcare LightSpeed RT 16 scanner. Megavolt portal images were acquired with a Varian Trilogy unit equipped with an AS1000 electronic portal imaging device. The nodule localization method uses grayscale morphological filtering and level-set segmentation with a prior. The treatment-time portion of the algorithm is implemented on a graphical processing unit. The method was retrospectively tested on eight cases that include a total of 151 megavolt portal image frames. The method reduced the nodule position uncertainty by an average of 40% for seven out of the eight cases. The treatment phase portion of the method has a subsecond execution time that makes it suitable for near-real-time nodule localization. A method was developed to localize a pulmonary nodule in a megavolt portal image. The method uses the characteristics of the nodule in a prior CT scan to enhance the nodule in the portal image and to identify the nodule region by level-set segmentation. In a retrospective study, the method reduced the nodule position uncertainty by an average of 40% for seven out of the eight cases studied.

Design and implementation of a fuzzy hardware structure for morphological color image processing

A hardware implementation of a fuzzy processor suitable for morphological color image processing applications is presented for the first time. From the hardware point of view, only a small number of algorithms for hardware implementation of soft gray-scale morphological filters have been reported in the literature, since research in mathematical morphology focuses mainly on possible extensions of the standard definitions (e.g., color and fuzzy mathematical morphology). The proposed digital hardware structure is based on a sequence of pipeline stages, and parallel processing is used in order to minimize computational times. It is capable of performing the basic morphological operations of standard and soft erosion/dilation for color images of 24-bit resolution. For the computation of morphological operations, a 3 /spl times/ 3-pixel image neighborhood and the corresponding structuring element are used. However, the system can be easily expanded to accommodate windows of larger sizes. The architecture of the processor is generic; the units that perform the fuzzy inference can be utilized for other fuzzy applications. It was designed, compiled, and simulated using the MAX+PLUS II Programmable Logic Development System by Altera Corporation. The fuzzy processor exhibits a level of inference performance of 601 KFLIPS with 54 rules, and can be used for real-time applications where the need for short processing times is of the utmost importance. The selection of a latest technology computer system (Pentium 4/3 GHz with SSE-2) can speed up image processing applications, but the time required still cannot be compared to the corresponding time using this hardware structure.

モルフォロジーフィルタによる線状図形の抽出

モルフォロジーフィルタによる線状図形の抽出

Basis Matrix Representation of Morphological Filters with N-Dimensional Structuring Elements

In this paper, we present a basis matrix representation of grayscale morphological filters in N-dimensions. A procedure is proposed to derive the basis matrix and the block basis matrix (BBM) from an N-dimensional grayscale structuring element (GSE). It is shown that both opening and closing with arbitrary N-dimensional GSE can be accomplished by a local matrix operation using the basis matrix. Furthermore, these basis matrix representations are extended to the efficient implementation of open-closing (OC) and close-opening (CO) using the BBM.

A digit-serial architecture for gray-scale morphological filtering

We present a digit-serial architecture for gray-scale morphological operations that operate on radix-2 redundant numbers. We present new implementations for a redundant number adder and a maximum unit that are used in the morphological dilation unit. These new designs have areas comparable to 2's complement implementations but have significantly smaller latencies.

A fast implementation of 1-D grayscale morphological filters

This paper presents an algorithm for the fast implementation of 1-D grayscale morphological filters with set structuring elements. The algorithm is developed by filter property analysis, instead of by architecture design. It performs an opening or closing with a single pass procedure. The average number of operations required is less than three comparisons per sample for openings and closings, which is independent of the size of the structuring element used. >

Morphological pseudoconvolutions: One-parameter families of derived filters with increased invariant classes

The present paper introduces a digital gray-scale morphological filtering technique that is based on postfiltering a given filter so the invariant class of the new filter is larger than the invariant class of the original filter. More specifically, the invariant class of the new filter contains the original invariant class together with all signals whose variation is below a chosen threshold. The postfiltering includes a single erosion and a single dilation by a one-parameter structuring element, the choice of parameter determining the resulting invariant class. While the methodology is quite general, the two applications considered pertain to moving averages with nonnegative weights and moving medians, both of which are morphological filters. Both suppress noise in a signal and each possesses specific advantages and disadvantages. In brief, means tend to give better noise suppression while blurring edges, whereas medians preserve edges, while at the same time flattening small background variation in the underlying signal. The new one-parameter family of filters derived from moving averages are called pseudomeans. The filters derived from medians are called pseudomedians. Both preserve uncorrupted low background variation, as well as steps. Because they preserve small variation while at the same time behaving like the original filters, both filters are especially effective when the noise occurs in bursts, rather than uniformly across the signal.

Shape Analysis and Reduction of the Morphological Basis for Digital Moving-Average Filters

A digital moving-average filter with nonnegative mask weights that sum to unity is a gray-scale morphological filter. As such, it possesses a representation as a maximum of erosions over its morphological basis. The present paper investigates the special properties of the digital moving average morphological basis, these properties being, for the most part, combinatoric in nature. Shapes of digital structuring elements are related to the filter weights by means of shape classes. This is accomplished by defining a characteristic of a signal relative to the filter weights and then characterizing basis elements by means of the characteristic. The notion of shape is then extended to a more general class of morphological filters. The concept or a morphological quasi average is introduced and the power of such filters to approximate morphological filters is addressed. The effect of basis reduction is studied for a rather wide class of morphological filters, the aim being to quantify the degree to which a reduced-basis filter approximates the original full-basis filter. The reduction analysis is then applied to the moving mean.

The Matheron representation theorem for gray-scale morphological filters

We present a new proof of the Matheron representation theorem for gray-scale morphological filters, without using either the representation theorem for subsets of the plane or the umbra transform.