Molecular spectroscopic detection plays a crucial role in gas chromatography (GC). Some detectors constitute element-selective spectroscopy, where an element-containing species generates the detected ...signal, e.g. flame photometric detection (S, P, Cu); chemiluminescence detection (S, N). These respond with selective response, usually with excellent analyte detectability and reduced matrix interferences. Classical molecular spectroscopic detectors – Fourier transform infrared, nuclear magnetic resonance, ultraviolet – respond by giving a spectrum characteristic of the (intact) molecule. Molecular structure response plays multi-faceted roles: it produces a unique spectrum of a molecule, provided it is resolved by the column and presented to the detector as a single compound; or the chromatogram can be generated by responding to the total signal, or selectively to a given component of the signal. This review summarises the response, sensitivities, applicability, and recent literature reports of molecular spectroscopic detection. Hyphenation with dual detection and brief comments on multidimensional GC is included.
•The role of spectroscopic detection in gas chromatography is discussed.•A range of common and less-common spectroscopic detectors in GC is reviewed.•These are divided into element-selective, and those offering bulk molecular detection.•Aspects of selectivity of detection and analytical figures of merit are considered.•Improved molecular identification results from complementary spectroscopic detection.
Robust and effective detection of small target and false alarm (FA) suppression are the key techniques in infrared search and track systems. In this paper, the derivative entropy-based contrast ...measure (DECM) is proposed for small-target detection under various complex background clutters. Initially, different directional derivatives of an infrared image are calculated based on the facet model. Then, by analyzing the derivative properties of small target, the primitive entropy formula is improved by incorporating derivative information. With the improved entropy, the contrast measure is constructed to enhance small target and suppress background clutters in each derivative subband. Finally, the contrast measure maps derived from derivative subbands are fused together. The small target could be segmented easily from the fusion result. Experimental results demonstrate that DECM could effectively enhance dim small targets and suppress complex background clutters. Besides, DECM is also robust to infrared small-target images with noises of different levels. The detection results achieve higher detection ratio and lower FA compared with those of other methods under various infrared scenes.
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co-registered images taken at different times, the illumination ...variations and misregistration errors overwhelm the real object changes. Exploring the relationships among different spatial–temporal pixels may improve the performances of CD methods. In our work, we propose a novel Siamese-based spatial–temporal attention neural network. In contrast to previous methods that separately encode the bitemporal images without referring to any useful spatial–temporal dependency, we design a CD self-attention mechanism to model the spatial–temporal relationships. We integrate a new CD self-attention module in the procedure of feature extraction. Our self-attention module calculates the attention weights between any two pixels at different times and positions and uses them to generate more discriminative features. Considering that the object may have different scales, we partition the image into multi-scale subregions and introduce the self-attention in each subregion. In this way, we could capture spatial–temporal dependencies at various scales, thereby generating better representations to accommodate objects of various sizes. We also introduce a CD dataset LEVIR-CD, which is two orders of magnitude larger than other public datasets of this field. LEVIR-CD consists of a large set of bitemporal Google Earth images, with 637 image pairs (1024 × 1024) and over 31 k independently labeled change instances. Our proposed attention module improves the F1-score of our baseline model from 83.9 to 87.3 with acceptable computational overhead. Experimental results on a public remote sensing image CD dataset show our method outperforms several other state-of-the-art methods.
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of ...salient object detection for nature scene image to the optical RSI. In this paper, we propose an end-to-end deep network called LV-Net based on the shape of network architecture, which detects salient objects from optical RSIs in a purely data-driven fashion. The proposed LV-Net consists of two key modules, i.e., a two-stream pyramid module (L-shaped module) and an encoder-decoder module with nested connections (V-shaped module). Specifically, the L-shaped module extracts a set of complementary information hierarchically by using a two-stream pyramid structure, which is beneficial to perceiving the diverse scales and local details of salient objects. The V-shaped module gradually integrates encoder detail features with decoder semantic features through nested connections, which aims at suppressing the cluttered backgrounds and highlighting the salient objects. In addition, we construct the first publicly available optical RSI data set for salient object detection, including 800 images with varying spatial resolutions, diverse saliency types, and pixel-wise ground truth. Experiments on this benchmark data set demonstrate that the proposed method outperforms the state-of-the-art salient object detection methods both qualitatively and quantitatively.
Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly ...heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and nonlocal priors simultaneously. First, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background nonlocal self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entrywise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the elementwise reweighted higher order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.
Metal object detection (MOD) and detection of position (DoP) of pick-up coils are increasingly critical to the commercialisation of the wireless power transfer system. In this study, a detection ...scheme based on phase-detection, which can both realise the function of MOD and DoP, is newly introduced. The method is based on the principle that the presence of metal objects and pick-up coils both affect the impedance phase of the detection coil loop. Comparing with the traditional detection methods, which is based on measuring the absolutely value of impedance in detection loop, the malfunction is avoided by adopting phase difference as the criterion of the existence of metal objects and pick-up coils. An inexpensive and high-precision sensing circuit is newly designed to acquire the phase difference accurately. Simulation models and experiments are conducted to verify the feasibility of the proposed detection scheme. The ${\rm \Delta }\delta $Δδ of the detection coil circuit with a coin placed reaches −5.17, while the ${\rm \Delta }\delta $Δδ of other detection coils without metal objects keeps zero. In the meantime, the position of the pick-up coil is identified because the ${\rm \Delta }\delta $Δδ of these detection coils closer to the centre of the pick-up coil varies more significantly while other coils vary slightly.
Change detection has been a hotspot in the remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms ...have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has a better change detection performance. However, the changes of multi-temporal images are usually complex, and the existing methods are not effective enough. In recent years, the deep network has shown its brilliant performance in many fields, including feature extraction and projection. Therefore, in this paper, based on the deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called deep SFA (DSFA). In the DSFA model, two symmetric deep networks are utilized for projecting the input data of bi-temporal imagery. Then, the SFA module is deployed to suppress the unchanged components and highlight the changed components of the transformed features. The change vector analysis pre-detection is employed to find unchanged pixels with high confidence as training samples. Finally, the change intensity is calculated with chi-square distance and the changes are determined by threshold algorithms. The experiments are performed on two real-world data sets and a public hyperspectral data set. The visual comparison and the quantitative evaluation have shown that DSFA could outperform the other state-of-the-art algorithms, including other SFA-based and deep learning methods.
Infrared small target detection plays an important role in precision guidance, infrared warning, and other applications. The infrared patch-tensor (IPT) model has good detection performance, but some ...challenges still exist, such as the inaccurate representation of the background rank and poor robustness against noise and sparse interference. In order to solve these problems, a new IPT model is proposed in this article. First, to approximate the tensor rank more reasonably, t-SVD is generalized to multimodal t-SVD, and the tensor fibered rank is introduced. Moreover, the tensor fibered nuclear norm based on the Log operator (LogTFNN) is used to nonconvex approximate tensor fibered rank. Second, to suppress sparse interference such as strong edges and corner points, the prior information is extracted by the local structure tensor. Third, the hypertotal variation (HTV) is used as a joint regularization term to remove noise. Then, the alternating direction method of multipliers (ADMM) is used to solve the model. The proposed algorithm was tested on the 20 single-frame infrared images and six sequences of real scenes. Lots of experiments demonstrate that this algorithm has the robustness to noise and different scenes. Different evaluation metrics also show that the proposed algorithm has a significant superiority in detection performance compared with various state-of-the-art methods.
Salient Object Detection via Integrity Learning Zhuge, Mingchen; Fan, Deng-Ping; Liu, Nian ...
IEEE transactions on pattern analysis and machine intelligence,
03/2023, Volume:
45, Issue:
3
Journal Article
Peer reviewed
Open access
Although current salient object detection (SOD) works have achieved significant progress, they are limited when it comes to the integrity of the predicted salient regions. We define the concept of ...integrity at both a micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object. Meanwhile, at the macro level, the model needs to discover all salient objects in a given image. To facilitate integrity learning for SOD, we design a novel I ntegrity Co gnition N etwork ( ICON ), which explores three important components for learning strong integrity features. 1) Unlike existing models, which focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e., kernel shape and context) and increase feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce an integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of our ICON, comprehensive experiments are conducted on seven challenging benchmarks. Our ICON outperforms the baseline methods in terms of a wide range of metrics. Notably, our ICON achieves <inline-formula><tex-math notation="LaTeX">\sim</tex-math> <mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="zhang-ieq1-3179526.gif"/> </inline-formula>10% relative improvement over the previous best model in terms of average false negative ratio (FNR), on six datasets. Codes and results are available at: https://github.com/mczhuge/ICON .
Infrared (IR) small target detection with high detection rate, low false alarm rate, and high detection speed has a significant value, but it is usually very difficult since the small targets are ...usually very dim and may be easily drowned in different types of interferences. Current algorithms cannot effectively enhance real targets and suppress all the types of interferences simultaneously. In this letter, a multiscale detection algorithm utilizing the relative local contrast measure (RLCM) is proposed. It has a simple structure: first, the multiscale RLCM is calculated for each pixel of the raw IR image to enhance real targets and suppress all the types of interferences simultaneously; then, an adaptive threshold is applied to extract real targets. Experimental results show that the proposed algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms. Besides, the proposed algorithm has the potential of parallel processing, which is very useful for improving the detection speed.
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