Probabilistic ISC for matching images of objects having individual difference

By combining wavelet transform and increment sign correlation with iterative correction, we present a robust image matching method, which effectively reduced mismatch. Firstly, by decomposing the sensed image and the reference image with wavelet, smooth images are generated in coarse scales. By the use of increment and decrement information of adjacent pixels, smooth images are encoded into binary images, and the matching position can be found by use of the cross correlation function. Wavelet decomposition can effectively remove noise and greatly improve the real-time performance of matching algorithm while increment sign correlation method is not sensitive to the gray level of images. The combination of wavelet transform and increment sign correlation can deal with noise, shadow and occlusion as well as improve the matching speed compared with traditional matching methods. Considering the condition of heavy noise and occlusion that the matching method mentioned above can not find the correct position in condition of heavy noise and occlusion, we present an iterative correction image matching method, i.e. use the above matching result as prior knowledge and achieve the correct matching position by correcting matching results iteratively. Theoretical analysis shows the feasibility of the presented method, and our experiments show its validity.

Improvement of a Non-Contact Nurse Call System by Image Processing with Increment Sign Correlation

Multimedia information processing is a new technology developing rapidly recent years, among which the Image Matching Technology has wide range of applications in various fields, such as face recognition and building recognition, image database retrieval, etc. This paper focus on the accuracy of image matching using SIFT (Scale Invariant Feature Transform) algorithm to extract feature points from images. And then the feature points are used to find out the matching points between two images by calculating the correlation degree based on the Analysis of Grey Correlation Degree. Compared with the original algorithm, in the case that the matching time is almost the same, the algorithm in this paper has higher matching accuracy and robustness, proved by experiments, can adapt to the requirements of image recognition in various conditions and can eliminate the influence of stretching, rotation and illumination change.

Automatic detection of human faces in the environment is essential and needed to develop a mobile service robot so that it can detect and communicate with surrounding humans. Since the use of shape geometrical features alone is not good enough for reliable detection, color cues are used to augment the reliability of face detector. While color adds new information, it is problematic due to the dependence of image colors on the ambient illumination. Reduction of the illumination effect on image colors is therefore an important preprocessing step if color information is to be utilized for face detection. The methods presented in the previous literature to account for the illumination changes requires that the possible and expected illumination conditions to be specified a priori and the system should be trained in some way to account for the expected image changes. We propose a system of illumination preprocessing that does not require neither prior knowledge nor experimentation with the illuminants. We use a simple attachment with the camera to measure the illumination color and the image colors are compensated for illumination changes in real time using the reflection from the reference attachment nose. The system is implemented and experiments showed the success of the system to detect human faces despite moderate changes of the illumination conditions

Subspace methods that utilize principal component analysis (PCA) are widely used for pattern classification or detection problems. Kernel PCA (KPCA) that is an extension of PCA is also applied to subspace methods. However, its computational cost is very high since the computational cost mainly depends on the number of samples in kernel methods. Recently, subset KPCA (SKPCA) has been proposed in order to reduce its computational complexity. In this paper, we apply SKPCA to subspace methods, and compare SKPCA with KPCA using some sample selection methods. Experimental results demonstrate advantages of subspace methods using SKPCA.

AC thin film electroluminescent devices of MIS and MISIM have been fabricated with a novel dielectric layer of Eu2O3 as an insulator. The threshold voltage for light emission is found to depend strongly on the frequency of excitation source in these devices. These devices are fabricated with an active layer of ZnS:Mn and a novel dielectric layer of Eu2O3 as an insulator. The observed frequency dependence of brightness-voltage characteristics has been explained on the basis of the loss characteristic of the insulator layer. Changes in the threshold voltage and brightness with variation in emitting or insulating film thickness have been investigated in metal-insulator-semiconductor (MIS) structures. It has been found that the decrease in brightness occurring with decreasing ZnS layer thickness can be compensated by an increase in brightness obtained by reducing the insulator thickness. The optimal condition for low threshold voltage and higher stability has been shown to occur when the active layer to insulator thickness ratio lies between one and two.

Robust nose tip localization based on two-stage subclass discriminant analysis

Surface defects have a serious detrimental effect on the quality of steel. To address the problems of low efficiency and poor accuracy in the manual inspection process, intelligent detection technology based on machine learning has been gradually applied to the detection of steel surface defects. An improved YOLOv8 steel surface defect detection model called YOLOv8-MGVS is designed to address these challenges. The MLCA mechanism in the C2f module is applied to increase the feature extraction ability in the backbone network. The lightweight GSConv and VovGscsp cross-stage fusion modules are added to the neck network to reduce the loss of semantic information and achieve effective information fusion. The self-attention mechanism is exploited into the detection network to improve the detection ability of small targets. Defect detection experiments were carried out on the NEU-DET dataset. Compared with YOLOv8n from experimental results, the average accuracy, recall rate, and frames per second of the improved model were improved by 5.2%, 10.5%, and 6.4%, respectively, while the number of parameters and computational costs were reduced by 5.8% and 14.8%, respectively. Furthermore, the defect detection generalization experiments on the GC-10 dataset and SDD DET dataset confirmed that the YOLOv8-MGVS model has higher detection accuracy, better lightweight, and speed.

A framework for recognising human faces from unfamiliar views is described and a simple implementation of this framework evaluated. The interaction between training view and testing view is shown to compare with observations in human face recognition experiments. The ability of the system to learn from several training views, as available in video footage, is shown to improve the overall performance of the system as is the use of multiple testing images.

This paper presents observational data for the star 2MASSJ10183905+0014078 (UCAC4 452-048383), in which so far no brightness variability has been detected. However, photometric observations made in 2015 allowed recording are activity with an amplitude of 0.8 m for this star. The star parameters, namely magnitude and color index (V0 = 7.59, (B - V)0 = 1.19) and, therefore, the position on the Hertzsprung-Russell diagram, luminosity (L☉ = 0.1), spectral class (K7), mass (M☉ = 0.6), the spectral characteristics in the optical (Ca, Fe, Na D, H_ lines, Mg H, and TiO bands) and X-ray ranges allow us to classify the star as UVCet type object. The ash profile can be attributed to type II, with a relatively slow increase and decrease in brightness.

Freezing and freeze-drying processes are commonly used to extend the shelf life of drug products and to ensure their safety and efficacy upon use. When designing a freezing process, it is beneficial to characterize multiple physicochemical properties of the formulation, such as nucleation rate, crystal growth rate, temperature and concentration of the maximally freeze-concentrated solution, and melting point. Differential scanning calorimetry has predominantly been used in this context but does have practical limitations and is unable to quantify the kinetics of crystal growth and nucleation. In this work, we introduce a microfluidic technique capable of quantifying the properties of interest and use it to investigate aqueous sucrose solutions of varying concentration. Three freeze-thaw cycles were performed on droplets with 75-μm diameters at cooling and warming rates of 1 °C/min. During each cycle, the visual appearance of the droplets was optically monitored as they experienced nucleation, crystal growth, formation of the maximally freeze-concentrated solution, and melting. Nucleation and crystal growth manifested as increases in droplet brightness during the cooling phase. Heating was associated with a further increase as the temperature associated with the maximally freeze-concentrated solution was approached. Heating beyond the melting point corresponded to a decrease in brightness. Comparison with the literature confirmed the accuracy of the new technique while offering new visual data on the maximally freeze-concentrated solution. Thus, the microfluidic technique presented here may serve as a complement to differential scanning calorimetry in the context of freezing and freeze-drying. In the future, it could be applied to a plethora of mixtures that undergo such processing, whether in pharmaceutics, food production, or beyond.

Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.

Color constancy is the ability to assign a constant color to an object independent of changes in illumination. Color constancy is achieved by taking context information into account. Previous approaches that have used matching paradigms to quantify color constancy found degrees of constancy between 20% and 80%. Here, we studied color constancy in a color-naming task under different conditions of surround illumination and patch size. Observers categorized more than 400 patches for each illumination condition. This allows one to overcome inherent limitations in color naming and to study the changes in color categories under illumination changes. When small central test patches with a full context illumination were categorized, observers followed the illumination shift almost completely, showing a high degree of constancy (99%). Reducing the available context information or increasing the patch size decreased the degree of constancy to about 50%. Moderate degrees of constancy (66%) occurred even when the test patches were never viewed simultaneously but only in temporal alternation with the illumination. Boundaries between color categories were largely stable within and across observers under neutral illumination. Under changing illumination, there were small but systematic variations in the color category boundaries. Color category boundaries tended to rotate away from the illumination color. This variation was largest under full context conditions where highest degrees of color constancy were obtained.

This paper proposes a face recognition algorithm in which the discrete Gabor transform is used to extract the image face features vector that is then feed into a multilayer perceptron to be carried out the recognition task. The features vector, estimated using the Gabor transform, presents a small intra-person variation while the inter-persons variation is considerably large. This fact provides robustness against changes in illumination, wardrobe, facial expressions, scale, and position inside the captured image, as well as inclination, noise contamination and filtering. Proposed scheme also provides some tolerance to changes on the age of the person under analysis. Evaluation results using the proposed scheme with identification and verification configurations are given to show the desirable features of proposed algorithm

Background modeling methods in video analysis: A review and comparative evaluation