Adaptive Threshold Operation Research Articles

Quantum Image Edge Extraction Algorithm for Noisy Image

Image preprocessing is important for noise exclusion in order to improve the accuracy of contour detection and image edge extraction. In this paper, a dual quantum spatial filter is proposed in order to reduce statistical noise, image blurring, impulsive noise, and retained details. Non-Maximum-Suppression and instead of manually operating threshold, adaptive thresholding operation is used in the quantum edge detection algorithm to detect more edge information with sharp edges. The circuit of the algorithm is realized through a series of operatives. We have compared PSNR, SSIM, precision rate, accuracy rate, recall rate, error rate, and computational complexity of the algorithm with operator based some existing classical and quantum edge extraction algorithms. The algorithm has the potential to get improved edge continuity, greater sharpness, and extract edge information having less proportion of noise.

Infrared small target detection based on gray intensity descent and local gradient watershed

Robust infrared (IR) small target detection is an essential part of infrared search and tracking systems. Existing IR target detection methods based on human visual system usually use statistical features of rectangular region to measure the difference between target and background. In the case that the dim target size is smaller than the rectangular window cell size, since the window cell contains both the target and the background pixels, the statistical features cannot well represent the target contrast. The use of a rectangular window structure for calculation reduces the statistical difference between the target and the background, that is, reduces the saliency of the target, resulting in a slow rise in the ROC curve of the HVS method. This paper proposes a novel non-window structure filter for small target detection. Firstly, we obtain candidate target points by combining the high-frequency information of the image with the local maximum points, and at the same time, calculate the Sobel gradient map to prepare for subsequent processing. Then, a filter is designed based on gray intensity descent and local gradient watershed (GID-LGW) characteristics of the target, and it is used to calculate the contrast of each candidate target point. Finally, an adaptive threshold operation is applied to extract the target. Experiments show that compared with several baseline methods, the proposed method has a better detection rate, lower false alarm, and high speed, especially for small and weak targets with a size smaller than 3 × 2.

Yeast cell detection in color microscopic images using ROC‐optimized decoloring and segmentation

For the detection and evaluation of yeast cell microscopic images, decoloring and segmentation is crucial. Usually, a standard operation of color conversion to grayscale is used and subsequently, a thresholding algorithm is applied to yield the segmented cells. However, both types of these operations are controlled merely by fixed empirical parameters which are prone to inaccuracy. The authors illustrate a common problem of such a nonoptimal decoloring. They then develop a novel pipeline of algorithms for yeast cell color phantom generation. In this study, the authors extend Grundland' s decoloring approach and couple it with an adaptive thresholding operation. For the generated phantom, the parameters of these operations are optimized using the ROC-based F1-measure. It is showed that this measure is a better choice than the Precision and Recall measure alone. Computer experiments with various combinations of the operation coupling and different phantom backgrounds are accomplished, and the optimum values of all the controllable parameters are found. For the color phantom representing the most realistic conditions, the final fully optimized 4D combination of the improved Grundland' s decoloring and locally adaptive thresholding ensures more efficient cell detection performance than parameterless or nonoptimized combinations.

A pixel-level local contrast measure for infrared small target detection

Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.

Improved Fuzzy C-Means for Infrared Small Target Detection

Infrared (IR) small target detection has been extensively studied due to its importance in IR search and tracking (IRST) systems. Existing methods have some limitations in suppressing cluttered high-contrast backgrounds, which may result in more false detections. In this letter, on the one hand, we propose an improved fuzzy C-mean (IFCM) clustering to accurately segment IR small targets and complex backgrounds. On the other hand, an IFCM-based descriptor fusing multiple features (IFCM-MF) is proposed to suppress complex backgrounds. First, a sliding window is designed to quickly extract the candidate pixels. Then, to better enhance the target and suppress the background, we construct an IFCM-based local window and calculate the IFCM-MF. Finally, IR small targets are detected by adaptive thresholding operation. The experimental results show that our method can better suppress cluttered high-contrast backgrounds and significantly improve the detection performance with high speed.

Estimation of the Number of Endmembers via Thresholding Ridge Ratio Criterion

Endmember is defined as the spectral signature of pure material present in hyperspectral imagery. Estimation of the number of endmembers (NOE) present in a scene is an important preprocessing step and plays a crucial role in hyperspectral image processing, since over- or under-estimation of the NOE will lead to heavily incorrect results. In this article, we develop a thresholding ridge ratio (TRR) criterion based on eigendecomposition for NOE determination. Different from the widely used eigenvalue difference analysis methods, the TRR seeks an adaptive thresholding operation to the ridge ratio of eigenvalue differences, and ridge ratio combined with adaptive thresholding can theoretically guarantee a consistent estimate even when there are several local minima. Based on the TRR criterion, an algorithm is introduced to perform the estimation of NOE. Experimental results on both the simulated and real hyperspectral data sets have demonstrated that the proposed TRR-based algorithm has comparable and even better performances to several benchmark algorithms in the estimation accuracy of the NOE.

Infrared Small Target Detection Based on Facet Kernel and Random Walker

Efficient detection of targets immersed in a complex background with a low signal-to-clutter ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this paper, we address the target detection problem in terms of local image segmentation and propose a novel small target detection algorithm derived from facet kernel and random walker (RW) algorithm which includes four main stages. First, since the RW algorithm is suitable for images with less noises, local order-statistic and mean filtering are applied to remove the pixel-sized noises with high brightness (PNHB) and smooth the infrared images. Second, the infrared image is filtered by the facet kernel to enhance the target pixels and candidate target pixels are extracted by an adaptive threshold operation. Third, inspired by the properties of infrared targets, a novel local contrast descriptor (NLCD) based on the RW algorithm is proposed to achieve clutter suppression and target enhancement. Then, the candidate target pixels are selected as central pixels to construct the local regions and the NLCD map of all local regions is computed. The obtained NLCD map is weighted by the filtered map of facet kernel to further enhance target. Finally, the target is detected by a thresholding operation on the weighted map. Experimental results on three data sets show that the proposed method outperforms conventional baseline methods in terms of target detection accuracy.

Hierarchical Saliency Detection via Probabilistic Object Boundaries

Though there are many computational models proposed for saliency detection, few of them take object boundary information into account. This paper presents a hierarchical saliency detection model incorporating probabilistic object boundaries, which is based on the observation that salient objects are generally surrounded by explicit boundaries and show contrast with their surroundings. We perform adaptive thresholding operation on ultrametric contour map, which leads to hierarchical image segmentations, and compute the saliency map for each layer based on the proposed robust center bias, border bias, color dissimilarity and spatial coherence measures. After a linear weighted combination of multi-layer saliency maps, and Bayesian enhancement procedure, the final saliency map is obtained. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed model outperforms eight state-of-the-art saliency detection models.

An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism

Robust and efficient detection of an infrared (IR) small target is very important in the IR search and track system. Based on the contrast mechanism of the human visual system, an IR small target detection method with high detection rate, low false alarm rate, and short processing time is proposed in this letter. This method consists of two stages. At the first stage, with the top-hat filter and an adaptive threshold operation based on the constant false alarm rate applied to the original image, the suspicious target regions are obtained. In this way, the computing time of the following steps would be reduced a lot; meanwhile, the desired and predictable detection probability with the constant false alarm probability is maintained. At the second stage, we first define a new efficient local contrast measure between the target and the background, and the local self-similarity of an image is introduced to calculate the local saliency map. With the combination of the local self-similarity and local contrast, an efficient saliency map is obtained, which cannot only increase the signal-to-clutter ratio but also suppress residual clutter simultaneously. Then, a simple threshold operation on the saliency map is used to get the true targets. Experimental results indicate that the proposed method is superior in detection rate, false alarm rate, and processing time compared with the contrast algorithms, and it is an efficient method for IR small target detection in a complex background.

Infrared small-target detection using multiscale gray difference weighted image entropy

We propose an effective small-target detection approach based on weighted image entropy. The approach weights the local entropy measure by the multiscale grayscale difference followed by an adaptive threshold operation, which aims to improve the signal-to-noise ratio for cases in which jamming objects in the scene have similar thermal intensity measure with respect to the background as small target. The detection capability of the proposed approach has been validated on six real sequences, and the results demonstrate its significance and improvement.

3D ECT reconstruction by an improved Landweber iteration algorithm

Electrical capacitance tomography (ECT) is a relatively mature non-invasive imaging technique that attempts to map dielectric permittivity of materials. Recently, 3D ECT has gained interest because of its potential to generate volumetric images. The study of a fast and accurate image reconstruction algorithm is a challenge task, especially for 3D reconstruction. In this paper, we propose an improved Landweber iteration algorithm. We incorporate an additional acceleration term into the cost function and apply an adaptive threshold operation to the image obtained in each iteration for reducing artefacts. The algorithm proposed is tested by the noise-free and noise-contaminated capacitance data. Sensitivity matrixes and capacitance data of a 3D ECT sensor are obtained by using the finite element (FE) method. Extensive simulations in 3D reconstruction are carried out. The results verify the effectiveness of these improvements. Both the reconstruction time and the artefacts in the reconstructed image are reduced obviously. The experimental results of 3D reconstruction of objects in the shape of letters U and L confirm the effectiveness of the proposed algorithm further.

Apply Adaptive Threshold Operation and Conditional Connected-component to Image Text Recognition

How to effectively extract text from an image is a critical issue in the text recognition domain. Due to the variety of background components, for example, different kind of colors, texture, or brightness in an image will deteriorate the problem of text recognition. In this research, we applied adaptive threshold operation and conditional connected-component to deal with non-uniform lightness and complicated background images. Different from the general procedure of using the whole image to separate the background from the objects, our research adopted the divide and merge strategy to tackle this problem. Instead of segregating the grayscale image into many regions, our approach partitioned an image into three equal-sized horizontal segments to identify the local threshold value of each segment efficiently. With this approach, we successfully identified and recognized texts from an image. The result shows that the rates of object identification and recognition achieve 81.17% and 91.30%, respectively.