Simple Adaptive Threshold Research Articles

Infrared moving small-target detection using strengthened spatial-temporal tri-layer local contrast method

Infrared Search and Tracking (IRST) is challenged by detecting dim and small targets in complex backgrounds. In the context of moving small target detection, a perturbed, highly illuminated background is prone to engendering a high rate of false alarms. Furthermore, the variance in movement speed and scale of the targets can easily undermine the robustness of detection methods when extracting inter-frame information. In order to overcome these inadequacies, an effective method that leverages spatial and temporal profile information is proposed. In the spatial domain, targets are enhanced by computing the ratio difference as local contrast, and layered gradient kernel preprocessing along with gray difference calculations are applied to mitigate the impact of highly illuminated background. In the time domain, a tri-layer window for temporal profile of target pixels is utilized as an enhancement. By combining detections from both domains, target extraction is achieved through simple adaptive thresholding segmentation. The experimental results demonstrate that the proposed method is capable of effectively extracting slowly moving infrared dim small targets in complex backgrounds. Compared to existing spatiotemporal joint detection methods, the robustness is enhanced, false alarm rates are reduced, and higher computational efficiency is achieved.

Fast and Robust Infrared Small Target Detection Using Weighted Local Difference Variance Measure.

Infrared (IR) small-target-detection performance restricts the development of infrared search and track (IRST) systems. Existing detection methods easily lead to missed detection and false alarms under complex backgrounds and interference, and only focus on the target position while ignoring the target shape features, which cannot further identify the category of IR targets. To address these issues and guarantee a certain runtime, a weighted local difference variance measure (WLDVM) algorithm is proposed. First, Gaussian filtering is used to preprocess the image by using the idea of a matched filter to purposefully enhance the target and suppress noise. Then, the target area is divided into a new tri-layer filtering window according to the distribution characteristics of the target area, and a window intensity level (WIL) is proposed to represent the complexity level of each layer of windows. Secondly, a local difference variance measure (LDVM) is proposed, which can eliminate the high-brightness background through the difference-form, and further use the local variance to make the target area appear brighter. The background estimation is then adopted to calculate the weighting function to determine the shape of the real small target. Finally, a simple adaptive threshold is used after obtaining the WLDVM saliency map (SM) to capture the true target. Experiments on nine groups of IR small-target datasets with complex backgrounds illustrate that the proposed method can effectively solve the above problems, and its detection performance is better than seven classic and widely used methods.

Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge

The rapid expansion of video conferencing and remote works due to the COVID-19 pandemic has resulted in a massive volume of video data to be analyzed in order to understand the audience engagement. However, analyzing this data efficiently, particularly in real-time, poses a scalability challenge as online events can involve hundreds of people and last for hours. Existing solutions, especially open-sourced contributions, usually require dedicated and expensive hardware, and are designed as centralized cloud systems. Additionally, they may also require users to stream their video to remote servers, which raises privacy concerns. This paper introduces scalable and efficient computer vision algorithms for analyzing face orientation and eye blink in real-time on edge devices, including Android, iOS, and Raspberry Pi. An example solution is presented for proctoring online meetings, workplaces, and exams. It analyzes audiences on their own devices, thus addressing scalability and privacy issues, and runs at up to 30 fps on a Raspberry Pi. The proposed face orientation detection algorithm is extremely simple, efficient, and able to estimate the head pose in two degrees of freedom, horizontal and vertical. The proposed Eye Aspect Ratio (EAR) with simple adaptive threshold demonstrated a significant improvement in terms of false positives and overall accuracy compared to the existing constant threshold method. Additionally, the algorithms are implemented and open sourced as a toolkit with modular, cross-platform MediaPipe Calculators and Graphs so that users can easily create custom solutions for a variety of purposes and devices.

Infrared Small Target Detection Based on 1-D Difference of Guided Filtering

This letter proposes an efficient infrared small target detection method based on the 1-D difference of guided filtering (DoGF). First, the 1-D DoGF is constructed by measuring the difference of image structure fidelity between primitive guided filtering (GF) and local variance weighted GF from the perspective of 1-D signal analysis, which can effectively filter out 1-D noise components and protrude pulse signals. Second, the 1-D row and column DoGF are applied to process the infrared image along horizontal and vertical directions, respectively, and then the row–column crossed DoGF (rcDoGF) and column–row crossed DoGF (crDoGF) are calculated and integrated, which can greatly highlight the pulse-like small target signal and eliminate the background clutter. Finally, the small targets can be extracted with a simple adaptive threshold. Experimental results show that the proposed algorithm has high detection accuracy for small infrared targets under heavy noise interference, as well as for targets with different sizes and shapes.

Small Maritime Target Detection Using Gradient Vector Field Characterization of Infrared Image

Infrared small maritime target detection under strong ocean waves, a challenging task, plays a key role in maritime distress target search and rescue applications. Many methods based on directionality or gradient properties have proven to perform well for infrared images with heterogeneous scenarios. However, they tend to perform poorly when facing strong ocean wave background, mainly due to the following: 1) infrared images have low signal-to-clutter ratio with low intensity for small targets; 2) some waves have high local contrast that may be similar to or higher than targets. To solve these issues, a new method based on gradient vector field characterization (GVFC) of infrared images is proposed. First, we construct the gradient vector field and coarsely extract suspected targets. Then, gradient vector distribution measure (GVDM) is presented, which comprehensively integrates a synergistic homogeneity test based on Kolmogorov–Smirnov test with absolute difference standard deviation for gradient direction angle and regression analysis for gradient modulus. The proposed GVDM takes advantage of pixel-level gradient distribution property to further filtrate refined suspected targets. Moreover, gradient modulus horizontal local dissimilarity is proposed to measure the diversity of gradient modulus in horizontal direction between targets and waves, so as to enhance target saliency and suppress residual clutter simultaneously, which achieves preferable performance. Finally, a simple adaptive threshold is applied to confirm targets. Extensive experiments implemented on infrared maritime images with strong ocean waves demonstrate that the proposed method is superior to the state-of-the-art methods with respect to robustness and detection accuracy.

IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

Small target detection is a crucial and challenging task in infrared search and track system. Background estimation-based methods is an effective and important approach for infrared small target detection. Affected by the target pixels, existing background estimation methods may reconstruct an inaccurate background. Based on image inpainting technique, we propose a novel two-stage approach to predict more accurate backgrounds. At the first stage, the inner and outer window-based image inpainting (IOWII) is used to obtain a rough background estimation. Then a mask of candidate target region is automatically obtained by calculating and evaluating the difference between raw image and rough background. In the second stage, the final accurate background is predicted by mask-based image inpainting (MII). It recovers the removed candidate target area using the information of surrounding background pixels, avoiding target pixels to participate in the calculation of background reconstruction. Finally, the target saliency map is obtained by subtracting the final estimated background from the original image, and a simple adaptive threshold is used to segment the target. Experimental results on real infrared images and sequences demonstrate that the proposed method outperforms other state-of-the-arts. It is simple and effective, with strong robustness and good real-time performance.

A Novel Spatiotemporal Saliency Method for Low-Altitude Slow Small Infrared Target Detection

The effective monitoring of low-altitude slow small (LSS) targets represented by unmanned aerial vehicle (UAV) is a great challenge in the field of security in recent years. Most of the existing infrared (IR) small target algorithms focus on high-altitude target detection. However, the low-altitude background is complex and changeable, and high-intensity suspected targets exist widely. Existing methods usually cause high false alarm or failure detection for LSS targets. In this letter, we propose a novel spatiotemporal saliency method for LSS IR targets in image sequences. First, spatial variance saliency mapping and temporal gray saliency mapping are calculated in spatial domain and temporal domain, respectively. Then, the fusion saliency map is obtained by fusing the spatial saliency map and temporal saliency map. Finally, the target is extracted by a simple adaptive threshold segmentation. The proposed method is verified in five low-altitude IR image sequences. Experimental results demonstrate that the proposed method can achieve better detection performance than the existing state-of-the-art methods for LSS targets.

Small Infrared Target Detection Based on Weighted Local Difference Measure

Against an intricate infrared cloudy-sky background, jamming objects such as the edges of clouds in the scene have a similar thermal intensity measure with respect to the background as small targets. This may cause high false alarm rates and low probabilities of detection according to conventional small target detection methods. In this paper, we propose a weighted local difference measure (WLDM)-based scheme for the detection of small targets against various complex cloudy-sky backgrounds. Initially, a WLDM map is achieved to simultaneously enhance targets and suppress background clutters and noise. In this way, the true targets can be easily separated from jamming objects. After that, a simple adaptive threshold is used to segment the targets. More than 460 infrared small target images against diverse intricate cloudy-sky backgrounds were utilized to validate the detection capability of the WLDM-based method. Experimental results demonstrate that the proposed algorithm not only works more robustly for different cloudy-sky backgrounds, target movements, and signal-to-clutter ratio (SCR) values but also has a better performance with regard to the detection accuracy, in comparison to traditional baseline methods. In particular, the proposed method is able to significantly improve SCR values of the images.

Multiscale patch-based contrast measure for small infrared target detection

Infrared (IR) small target detection plays an important role in IR guidance systems. In this paper, a biologically inspired method called multiscale patch-based contrast measure (MPCM) is proposed for small IR target detection. MPCM can increase the contrast between target and background, which makes it easy to segment small target by simple adaptive thresholding method. Experimental results on three sequences demonstrate that the proposed small target detection method can not only suppress background clutter effectively even if with strong noise interference, but also detect targets accurately with low false alarm rate and high speed.

Small infrared target detection based on image patch ordering

Small infrared (IR) target detection is an important and challenging issue in IR target tracking system. In this paper, a patch ordering-based method is proposed for small IR target detection in an image with complicated background. Inspired by the contrast mechanism of human vision system, a patch ordering-based contrast measure (POCM) is designed to deal with the input image, which cannot only suppress the background noise and clutter but also enhance the targets significantly. In this way, POCM can increase the contrast ratio between target and background. This leads to higher Signal Clutter Ratio (SCR). Then the true target can be detected by applying simple adaptive thresholding method. The experimental results on two sequences show that the proposed method can efficiently detect small IR target from heavy clutter.

Classification of Myocardial Infarction Using Multi Resolution Wavelet Analysis of ECG

In this paper, classification of anterior and inferior myocardial infarction from normal cases is done using the changes happening in ECG waves. Depth of Q peak and elevation in ST segment is taken in consideration for classification purpose. A multiresolution approach along with an adaptive thresholding is used to extract these ECG features. Classification of inferior myocardial infarction (IMI) and anterior myocardial infarction (AMI) is done using a simple adaptive threshold (SAT) method. The sensitivity, specificity and accuracy is 93.22%, 94.28% and 93.61% respectively in case of IMI and in AMI cases its 83.33%, 88.57%, 86.15% respectively. Classification is also done using artificial neural network, but its performance is comparatively low. This may be due to inadequacy of data available. The PTB diagnostic ECG database is used for evaluation of the methods.

New real-time heartbeat detection method using the angle of a single-lead electrocardiogram

This study presents a new real-time heartbeat detection algorithm using the geometric angle between two consecutive samples of single-lead electrocardiogram (ECG) signals. The angle was adopted as a new index representing the slope of ECG signal. The method consists of three steps: elimination of high-frequency noise, calculation of the angle of ECG signal, and detection of R-waves using a simple adaptive thresholding technique. The MIT-BIH arrhythmia database, QT database, European ST-T database, T-wave alternans database and synthesized ECG signals were used to evaluate the performance of the proposed algorithm and compare with the results of other methods suggested in literature. The proposed method shows a high detection rate—99.95% of the sensitivity, 99.95% of the positive predictivity, and 0.10% of the fail detection rate on the four databases. The result shows that the proposed method can yield better or comparable performance than other literature despite the relatively simple process. The proposed algorithm needs only a single-lead ECG, and involves a simple and quick calculation. Moreover, it does not require post-processing to enhance the detection. Thus, it can be effectively applied to various real-time healthcare and medical devices.

Mean Shift-Based Defect Detection in Multicrystalline Solar Wafer Surfaces

This paper presents an automated visual inspection scheme for multicrystalline solar wafers using the mean-shift technique. The surface quality of a solar wafer critically determines the conversion efficiency of the solar cell. A multicrystalline solar wafer contains random grain structures and results in a heterogeneous texture in the sensed image, which makes the defect detection task extremely difficult. Mean-shift technique that moves each data point to the mode of the data based on a kernel density estimator is applied for detecting subtle defects in a complicated background. Since the grain edges enclosed in a small spatial window in the solar wafer show more consistent edge directions and a defect region presents a high variation of edge directions, the entropy of gradient directions in a small neighborhood window is initially calculated to convert the gray-level image into an entropy image. The mean-shift smoothing procedure is then performed on the entropy image to remove noise and defect-free grain edges. The preserved edge points in the filtered image can then be easily identified as defective ones by a simple adaptive threshold. Experimental results have shown the proposed method performs effectively for detecting fingerprint and contamination defects in solar wafer surfaces.

Adaptive thresholding for visual attention and tracking systems

A simple adaptive thresholding circuit for visual attention and tracking applications is presented. When used in visual attention systems or in tracking systems, the circuit allows background inhibition and false alarm reduction. A test chip with an eight-element adaptive thresholding circuit has been implemented in a standard 0.5-μm CMOS process and is operated via a 3.3-V supply in the saturation region. The circuit architecture is discussed, operation in saturation and subthreshold regions are explained, and measurements from a test chip are reported.