Top-hat Algorithm Research Articles

Improved one-dimensional dilation-based top-hat algorithm for star segmentation under complicated background conditions.

The white top-hat transformation has been widely used in small bright target extraction. It usually applies an erosion operation to remove the target and then a dilation operation to recover the intensity of the processed image. A bright target will be extracted by subtracting the opening operation (erosion followed by dilation) from the raw image. The drawback of this method is that its denoising ability is poor because the estimated background threshold by an opening operation is smaller than the raw image. This study puts forward the viewpoint that by use of a proposed one-dimensional (1D) symmetrical line-shaped structuring element a bright target can also be removed by the dilation operation. Consequently, the white top-hat transformation can be implemented by subtracting only the dilation operation from the raw image. To the best knowledge of the authors, it is the first time to use this method to achieve the top-hat transformation. The simulation experiment shows that the proposed 1D top-hat algorithm has excellent performance in denoising ability and detection ability. Moreover, real night experiments demonstrate that our proposed algorithm can work reliably under both complicated background conditions and good weather conditions. It is noticeable that the performance of computational efficiency and resource consumption have been considerably improved because a 1D structuring element is employed and the erosion operation is not included.

CMOS Image Sensor Design and Image Processing Algorithm Implementation for Total Hip Arthroplasty Surgery.

As the average age of the population goes higher, the people undergo hip arthroplasty surgery to replace their stiff and painful damaged hip joints. To reduce the risk factor after the surgery we developed a visual aided system in previous works. In order to solve the integration problems of commercial camera with other sensors into the femoral head and to minimize the area and power consumption, in this paper we propose a CMOS image sensor of resolution 200 × 200 specially designed for the application in which each individual pixel measures around 15 μm × 15 μm in size and the image sensor chip size measures about 3.5 mm × 3.5 mm. The proposed sensor is simulated with the input current variations from 2 pA to 100 pA for the individual pixels and the corresponding measurements for each pixel range from 2 mV to 855 mV. Besides, we put forward a new method of pattern detection and recognition in the blood-covered situation, which provides an accurate segmentation of patterns from the blood. All the detected patterns are recognized by generating its right 9-bit binary ID required for the pose estimation calculation. Furthermore, to reduce system power consumption, we implement algorithms on FPGA to process the image data pixel by pixel and transmit it directly to the computer for post-processing. Experimental results show that the pattern detection rate goes as high as 99%, which is 5% better in accuracy compared to the top hat algorithm. The power consumption of the system is 213 mW, which is a 70% decrease compared to our previous work.

Mapping channel edges in seismic data using curvelet transform and morphological filter

Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors.

An Omnidirectional Morphological Method for Aerial Point Target Detection Based on Infrared Dual-Band Model

Aerial infrared point target detection under nonstationary background clutter is a crucial yet challenging issue in the field of remote sensing. This paper presents a novel omnidirectional multiscale morphological method for aerial point target detection based on a dual-band model. Considering that the clutter noise conforms to the Gaussian distribution, the single-band detection model under the Neyman-Pearson (NP) criterion is established first, and then the optimal fused probability of detection under the dual-band model is deduced according to the And fusion rule. Next, the omnidirectional multiscale morphological Top-hat algorithm is proposed to extract all the possible targets distributing in every direction, and the local difference criterion is employed to eliminate the residual background edges further. The dynamic threshold-to-noise ratio (TNR) is adjusted to obtain the optimal probability of detection under the constant false alarm rate (CFAR) criterion. Finally, the dim point target is extracted after dual-band data correlation. The experimental result demonstrates that the proposed method achieves a high probability of detection and performs well with respect to suppressing complex background when compared with common algorithms. In addition, it also has the advantage of low complexity and easy implementation in real-time systems.

Illumination-Robust Foreground Extraction for Text Area Detection in Outdoor Environment

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

Variance Window Based Car License Plate Localization

In this paper, a new method for discovering the candidate car license plate locations is presented. First, the image is decomposed using a Haar wavelet to get the HL band with vertical edges. Then, the HL band image is binarized using an Otsu threshold. Next a black top-hat algorithm is applied to reduce the effects of interfering large continuous features other than the license plate. At this time, a moving window based modified variance score calculation is made for areas with white pixels. This work found that the top 3 detected rectangle windows correctly locate the license plate regions with a success rate of about 98.2%. Moreover, the proposed method is robust enough to locate the plates in cases where the rough vehicle position has not been previously discovered and the cars are not centered in the image.

Image Processing for Mobile Information Retrieval Service

The modern society with the wide spread recognition of the importance of informatics and for the development of information and communication technology is rapidly processing. Especially the rapid development in mobile technology boost up the general expectation that one can get the information he wants anytime anywhere. Accordingly image search for the convenient information retrieval is becoming common. However general image search has difficulties because inexactitude extracting character in the image and getting the detail information in extracted character. Therefore these paper make character recognition through the images that I photographed a sightseeing resort, a signboard of a lot of stores to a smart phone camera, so information offer to be convenient to users is a purpose. A user can get detailed information, by character extraction way called top-hat algorithm and connect to a server.