Face recognition using illuminant locality preserving projections

A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

ED-Ged:Nighttime Image Semantic Segmentation Based on Enhanced Detail and Bidirectional Guidance

Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.

Efficacy and safety of Usights UC100 illuminated microcatheter in microcatheter-assisted trabeculotomy

AIM:To evaluate the efficacy and safety of Usights UC100 illuminated microcatheter in microcatheter-assisted trabeculotomy(MAT).METHODS:Totally 10 eyes of 10 patients with primary open angle glaucoma(POAG)who underwent MAT facilitated by Usights UC100(5 eyes)or iTrack(5 eyes)were reviewed.The success of this surgery was defined as intraocular pressure(IOP)<22 mm Hg with>30%reduction,without oral glaucoma medications,or additional glaucoma surgery.RESULTS:The mean pre-operative IOP was 25.38±10.22 mm Hg in the Usights UC100 group and 19.98±3.87 mm Hg in the iTrack group.MAT was achieved in all eyes in both groups.The success rates for the Usights UC100 group and iTrack groups were in all and 4 eyes,respectively.Both microcatheters produced a statistically significant reduction in IOP,and eyes using Usights UC100 achieved a lower IOP than the iTrack group at 3mo followup(12.58±1.52 and 14.84±1.89 mm Hg,respectively),but no statistical significance was there.No severe side effects were observed in either group.CONCLUSION:MAT using Usights UC100 or iTrack both achieve significant pressure reduction in cases of POAG,and Usights UC100 is as safe as iTrack.

Numerical Simulation of Super-Resolution Structured Illumination Microscopy (SIM) Using Heintzmann-Cremer Algorithm with Non-Continuous Spatial Frequency Support

We report a comprehensive numerical study of super resolution (SR) structured illumination microscopy (SIM) utilizing the classic Heintzmann-Cremer SIM process and algorithm. In particular, we investigated the impact of the diffraction limit of the underlying imaging system on the optimal SIM grating frequency that can be used to obtain the highest SR enhancement with non-continuous spatial frequency support. Besides confirming the previous theoretical and experimental work that SR-SIM can achieve an enhancement close to 3 times the diffraction limit with grating pattern illuminations, we also observe and report a series of more subtle effects of SR-SIM with non-continuous spatial frequency support. Our simulations show that when the SIM grating frequency exceeds twice that of the diffraction limit, the higher SIM grating frequency can help achieve a higher SR enhancement for the underlying imaging systems whose diffraction limit is low, though this enhancement is obtained at the cost of losing resolution at some lower resolution targets. Our simulations also show that, for underlying imaging systems with high diffraction limits, however, SR-SIM grating frequencies above twice the diffraction limits tend to bring no significant extra enhancement. Furthermore, we observed that there exists a limit grating frequency above which the SR enhancement effect is lost, and the reconstructed images essentially have the same resolution as the one obtained directly from the underlying imaging system without using the SIM process.

Speckle structured illumination endoscopy with enhanced resolution at wide field of view and depth of field

Structured illumination microscopy(SIM)is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity.The spatial resolution of SIM is typically limited to two times of the diffraction limit and the depth of field is small.In this work,we propose and experimentally demonstrate a low cost,easy to implement,novel technique called speckle structured illumination endoscopy(SSIE)to enhance the resolution of a wide field endoscope with large depth of field.Here,speckle patterns are used to excite objects on the sample which is then followed by a blind-SIM algorithm for super resolution image reconstruction.Our approach is insensitive to the 3D morphology of the specimen,or the deformation of illuminations used.It greatly simplifies the experimental setup as there are no calibration protocols and no stringent control of illumination patterns nor focusing optics.We demonstrate that the SSIE can enhance the resolution 2–4.5 times that of a standard white light endoscopic(WLE)system.The SSIE presents a unique route to super resolution in endoscopic imaging at wide field of view and depth of field,which might be beneficial to the practice of clinical endoscopy.

Deep learning enhanced NIR-Ⅱ volumetric imaging of whole mice vasculature

Fluorescence imaging through the second near-infrared window(NIR-Ⅱ,1000–1700 nm) allows in-depth imaging.However, current imaging systems use wide-field illumination and can only provide low-contrast 2D information, without depth resolution. Here, we systematically apply a light-sheet illumination, a time-gated detection, and a deep-learning algorithm to yield high-contrast high-resolution volumetric images. To achieve a large Fo V(field of view) and minimize the scattering effect, we generate a light sheet as thin as 100.5 μm with a Rayleigh length of 8 mm to yield an axial resolution of 220 μm. To further suppress the background, we time-gate to only detect long lifetime luminescence achieving a high contrast of up to 0.45 Icontrast. To enhance the resolution, we develop an algorithm based on profile protrusions detection and a deep neural network and distinguish vasculature from a low-contrast area of 0.07 Icontrast to resolve the 100μm small vessels. The system can rapidly scan a volume of view of 75 × 55 × 20 mm3and collect 750 images within 6mins. By adding a scattering-based modality to acquire the 3D surface profile of the mice skin, we reveal the whole volumetric vasculature network with clear depth resolution within more than 1 mm from the skin. High-contrast large-scale 3D animal imaging helps us expand a new dimension in NIR-Ⅱ imaging.

Complex-amplitude Fourier single-pixel imaging via coherent structured illumination

We propose a method of complex-amplitude Fourier single-pixel imaging(CFSI)with coherent structured illumination to acquire both the amplitude and phase of an object.In the proposed method,an object is illustrated by a series of coherent structured light fields,which are generated by a phase-only spatial light modulator,the complex Fourier spectrum of the object can be acquired sequentially by a single-pixel photodetector.Then the desired complex-amplitude image can be retrieved directly by applying an inverse Fourier transform.We experimentally implemented this CFSI with several different types of objects.The experimental results show that the proposed method provides a promising complex-amplitude imaging approach with high quality and a stable configuration.Thus,it might find broad applications in optical metrology and biomedical science.

Ecological and habitat ranges of orchids in the northernmost regions of their distribution areas:A case study from Ural Mountains,Russia

The Orchidaceae,which is one of the most interesting families of angiosperms,contains a large number of rare species.Despite their acknowledged importance,little attention has been paid to the study of orchids distributed in northern territories.In this study,we determined the syntaxonomical diversity and ecological parameters of orchid habitats in two of Europe's largest protected areas,the Pechoro-Ilychsky Reserve and the Yugyd Va National Park(northeastern European Russia),and then compared our findings to those in other parts of orchid distribution ranges.For this purpose,we studied 345 descriptions of plant communities(releves) containing species from Orchidaceae and defined habitat parameters using Ellenberg indicator values with the community weight mean approach,nonmetric multidimensional scaling(NMS),and relative niche width.We found that orchids were distributed in eight habitat types and 97 plant associations.The largest number of orchid species is found in forest communities.Half of the orchid species under study occur in the mires and rock habitats with open vegetation.Several orchids consistently occur in areas disturbed by human activity.In addition,our study indicates that the main drivers of orchid distribution across the vegetation types are light and soil nitrogen.Our analysis of the ecological parameters of orchid habitats indicates that some orchid species can be classified as habitat specialists that are confined to a relatively narrow ecological niche in the Urals(e.g.,Goodyera repens,Cypripedium guttatum and Dactylorhiza maculata).Several other species(e.g.Neottia cordata and Dactylorhiza fuchsia) grow under diverse ecological parameters.

Compact and robust dual-color linearly polarized illumination source for three-photon fluorescence imaging

The miniaturized femtosecond laser in near infrared-Ⅱregion is the core equipment of threephoton microscopy.In this paper,we design a compact and robust illumination source that emits dual-color linearly polarized light for three-photon microscopy.Based on an all-polarizationmaintaining passive mode-locked fiber laser,we shift the center wavelength of the pulses to the 1.7m band utilizing cascade Raman effect,thereby generate dual-wavelength pulses.To enhance clarity,the two wavelengths are separated through the graded-index multimode fiber.Then we obtain the dual-pulse sequences with 1639.4 nm and 1683.7 nm wavelengths,920 fs pulse duration,and 23.75 MHz pulse repetition rate.The average power of the signal is 53.64mW,corresponding to a single pulse energy of 2.25 nJ.This illumination source can be further amplified and compressed for three-photon fluorescence imaging,especially dual-color three-photon fluorescence imaging,making it an ideal option for biomedical applications.

Comparison of point detection and area detection for point-scanning structured illumination microscopy

Structured illumination microscopy(SIM)is suitable for biological samples because of its relatively low-peak illumination intensity requirement and high imaging speed.The system resolution is affected by two typical detection modes:Point detection and area detection.However,a systematic analysis of the imaging performance of the different detection modes of the system has rarely been conducted.In this study,we compared laser point scanning point detection(PS-PD)and point scanning area detection(PS-AD)imaging in nonconfocal microscopy through theoretical analysis and simulated imaging.The results revealed that the imaging resolutions of PSPD and PS-AD depend on excitation and emission point spread functions(PSFs),respectively.Especially,we combined the second harmonic generation(SHG)of point detection(P-SHG)and area detection(A-SHG)with SIM to realize a nonlinear SIM-imaging technique that improves the imaging resolution.Moreover,we analytically and experimentally compared the nonlinear SIM performance of P-SHG with that of A-SHG.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement:We developed a generalized computational approach to design uniform,high-intensity excitation light for low-cost,quantitative fluorescence imaging of in vitro,ex vivo,and in vivo samples with a single device.Introduction:Fluorescence imaging is a ubiquitous tool for biomedical applications.Researchers extensively modify existing systems for tissue imaging,increasing the time and effort needed for translational research and thick tissue imaging.These modifications are applicationspecific,requiring new designs to scale across sample types.Methods:We implemented a computational model to simulate light propagation from multiple sources.Using a global optimization algorithm and a custom cost function,we determined the spatial positioning of optical fibers to generate 2 illumination profiles.These results were implemented to image core needle biopsies,preclinical mammary tumors,or tumor-derived organoids.Samples were stained with molecular probes and imaged with uniform and nonuniform illumination.Results:Simulation results were faithfully translated to benchtop systems.We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings.The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model.Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used.Conclusion:We demonstrate the significance of using a computationally designed illumination for in vitro,ex vivo,and in vivo fluorescence imaging.Applicationspecific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features.This approach is generalizable across light sources,biological applications,and detectors.

Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

An image can be degraded due to many environmental factors like foggy or hazy weather,low light conditions,extra light conditions etc.Image captured under the poor light conditions is generally known as non-uniform illumination image.Non-uniform illumination hides some important information present in an image during the image capture Also,it degrades the visual quality of image which generates the need for enhancement of such images.Various techniques have been present in literature for the enhancement of such type of images.In this paper,a novel architecture has been proposed for enhancement of poor illumination images which uses radial basis approximations based BEMD(Bi-dimensional Empirical Mode Decomposition).The enhancement algorithm is applied on intensity and saturation components of image.Firstly,intensity component has been decomposed into various bi-dimensional intrinsic mode function and residue by using sifting algorithm.Secondly,some linear transformations techniques have been applied on various bidimensional intrinsic modes obtained and residue and further on joining the transformed modes with residue,enhanced intensity component is obtained.Saturation part of an image is then enhanced in accordance to the enhanced intensity component.Final enhanced image can be obtained by joining the hue,enhanced intensity and enhanced saturation parts of the given image.The proposed algorithm will not only give the visual pleasant image but maintains the naturalness of image also.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.

Research and Application of Excitation Method Optimization in Areas with Low Signal-to-Noise Ratio of Seismic Data

In areas with a low signal-to-noise ratio of seismic data,the continuity of the seismic reflection waves in the exploration target layer is very poor,which will reduce the imaging accuracy and make it impossible to solve certain geological tasks.This article suggests an approach to address the issue of seismic acquisition by optimizing excitation parameters.It involves conducting a detailed investigation of the surface structure,enhancing the observation system,increasing the coverage appropriately,and transitioning from combined-well excitation to single-well excitation.Additionally,the use of technical tools like qualitative evaluation of the observation system and forward modeling are employed to determine the final optimized seismic acquisition plan.The effectiveness of this approach is evident from the seismic profile obtained in an exploration area in Inner Mongolia.

CCD视频信号处理电路设计

针对e2v公司的CCD67 Back Illuminated NIMO型CCD,设计其视频信号处理电路,包括:前置放大电路、相关双采样(CDS)电路以及低通滤波电路等。实验结果表明,设计的CCD视频信号处理电路能够达到去除噪声、提取有用信号的目的。

Controlled illumination and seismic acquisition geometry for target-oriented imaging

The conventional method of seismic data acquisition geometry design is based on the assumption of horizontal subsurface reflectors, which often is not suitable for complex structure. We start from a controlled illumination analysis and put forward a method of seismic survey geometry design for target-oriented imaging. The method needs a velocity model obtained by a preliminary seismic interpretation. The one-way Fourier finite-difference wave propagator is used to extrapolate plane wave sources on the target layer to the surface. By analyzing the wave energy distribution at the surface extrapolated from the target layer, the shot or receiver locations needed for target layer imaging can be determined. Numerical tests using the SEG-EAGE salt model suggest that this method is useful for confirming the special seismic acquisition geometry layout for target-oriented imaging.

Intelligent emergency service system based on wireless sensor and actuator networks

An intelligent emergency service（ IES） system is designed for indoor environments based on a wireless sensor and actuator network（ WSAN） composed of a gateway, sensor nodes, and a multi-robot system（ MRS）. If the MRS receives accident alarm information, the group of robots will navigate to the accident sites and provide corresponding emergency services.According to the characteristics of the MRS, a distributed consensus formation protocol is designed, which can assure that the multiple robots arrive at the accident site in a specified formation. The prototype emergency service system was designed and implemented, and some relevant simulations and experiments were carried out. The results showthat the MRS can successfully provide emergency lighting and failure node replacement services when accidents happen. The effectiveness of the algorithm and the feasibility of the system are verified.