Sudden Illumination Changes Research Articles

Camera-based photoplethysmography in critical care patients.

Camera-based photoplethysmography (cbPPG) is an optical measurement technique that reveals pulsatile blood flow in cutaneous microcirculation from a distance. cbPPG has been shown to reflect pivotal haemodynamic events like cardiac ejection in healthy subjects. In addition, it provides valuable insight into intrinsic microcirculatory regulation as it yields dynamic, two-dimensional perfusion maps. In this study, we evaluate the feasibility of a clinical cbPPG application in critical care patients. A mobile camera set-up to record faces of patients at the bed site was constructed. Videos were made during the immediate recovery after cardiac surgery under standard critical care conditions and were processed offline. Major motion artefacts were detected using an optical flow technique and suitable facial regions were manually annotated. cbPPG signals were highpass filtered and Fourier spectra out of consecutive 10s signal segments calculated for heart rate detection. Signal-to-noise ratios (SNR) of the Fourier spectra were derived as a quality measure. Reference data of vital parameters were synchronously acquired from the bed site monitoring system. Seventy patient videos of an average time of 28.6±2.8 min were analysed. Heart rate (HR) was detected within a±5 bpm range compared to reference in 83% of total recording time. Low SNR and HR detection failure were mostly, but not exclusively, attributed to non-physiological events like patient motion, interventions or sudden changes of illumination. SNR was reduced by low arterial blood pressure, whereas no impact of other perioperative or disease-related parameters was identified. Cardiac ejection is detectable by cbPPG under pathophysiologic conditions of cardiovascular disease and perioperative medicine. cbPPG measurements can be seamlessly integrated into the clinical work flow of critical care patients.

A novel Rotational Symmetry Dynamic Texture (RSDT) based sub space construction and SCD (Similar-Congruent-Dissimilar) based scoring model for background subtraction in real time videos

Background Subtraction (BS) plays an important role in video surveillance system because it provides a focus of attention for moving object detection. But there are still challenging scenarios such as dynamic backgrounds and illumination variation. To achieve greater foreground detection quality under these circumstances, a novel approach called "Rotational Symmetry Dynamic Texture" for Background Subtraction is proposed. The concept of key frame is used to avoid processing overhead. A spatio temporal patch is used to describe motion and appearance of a video sequence. From that patch the proposed Rotational Symmetry Dynamic Texture (RSDT) method will encode the relationship between the referenced pixel and its neighbours, based on the directions that are calculated using the concept of rotational symmetry and line symmetry. A novel "SCD (Similar-Congruent-Dissimilar) based scoring" model is used to estimate appearance consistency and temporal coherence of the video. The two layer approach is used to solve sudden illumination changes and to improve processing speed by adopting mean values of blocks. It is suitable for both indoor and outdoor environments because the subspace construction is purely based on directional codes with rotational symmetry and line symmetry. Metric F-Score is used to measure the performance of the proposed method. Finally this paper provides an effective and robust background subtraction scheme. From performance evaluation, it is observed that Rotational Symmetry Dynamic Texture with SCD based scoring model guarantees accurate background subtraction than the existing literature.

Robust foreground detection in sudden illumination change

Background subtraction is an important part of various computer vision applications that can detect the foreground objects by comparing the current pixels with a background model. The general approaches gradually update the background model according to the current status, but might fail in sudden illumination changes. An illumination-robust background modelling method is proposed to address this problem. The method provides quick illumination compensation using two background models with different adaption rates. Experimental results show that the proposed algorithm outperforms several state-of-art approaches and provides low computational cost.

급격한 조명 변화에 강건한 동영상 대조비 개선 방법

동영상 대조비 개선 과정에서 단일 영상을 위해 연구된 대조비 개선 방법들을 사용할 수 있지만, 동영상의 연속성이 고려되지 않으면 원본 동영상에 없는 깜박임을 야기할 수 있다. 또한 동영상의 연속성을 고려하는 경우, 깜박임은 억제할 수 있지만 연속성 때문에 조명의 급격한 변화할 때 불필요한 페이드인/아웃(fade-in/out) 현상이 발생하는 단점이 발생할 수 있다. 본 논문에서는 깜박임과 페이드인/아웃 현상 없이 동영상의 대조비를 개선하는 방법을 제안한다. 제안하는 방법은 Fast Gray-Level Grouping(FGLG)를 사용하여 각 프레임의 대조비를 개선하고, 깜박임을 억제하기 위해 Exponential smoothing 필터를 사용한다. 불필요한 페이드인/아웃 현상을 억제하기 위해서는 S형 함수로 Exponential smoothing 필터의 평활화 비율을 프레임 별로 적응적으로 계산하여 적용한다. 실험에서 제안하는 방법과 기존의 방법들은 6가지 측정 기준을 적용하여 성능을 비교 및 분석한다. 실험 결과, 제안하는 방법은 영상 형태 보존을 측정하는 MSSIM과 깜박임을 측정하는 Flickering score에서 정량적으로 가장 높은 결과를 보여주었으며, 시각적인 품질 비교를 통해 조명 변화에 따른 적응적인 개선을 정성적 결과로 입증하였다.

Motion objects segmentation based on structural similarity background modelling

It is important to efficiently segment motion objects from video in computer vision applications. A novel foreground segmentation approach has been developed based on structural similarity background modelling, which responds quickly to sudden illumination changes and dynamic background. Both structural similarity map and environmental variation parameters are taken as a dynamic feedback controller to update the background. A multi‐modal features fusion strategy has been proposed to segment foregrounds in a dynamic cluttered scene without any hypothesis for the scenario content in advance. Experiments for videos with some challenging content have been performed. Comparative study with state‐of‐the‐art methods has indicated the superior performance of the proposed method.

Using a Simple Color Constancy Method for Indoor and Outdoor Applications

Color constancy is a problem of hard solution, and most of existing theories are applied only to synthesized images, while others present a limited performance when applied to real images. In this paper, we apply and analyze a color constancy algorithm that is used with real images subjected to sudden changes in illumination, both in outdoor and indoor environments. In this algorithm, by knowing the colors of some points on the scene submitted to a standard illumination, scene image color correction is made so that it appears always to be under the standard illumination influence. The method employed in this paper is applied to some tasks, such as tracking colored targets, controlling a robot using visual information and pre-processing outdoor images to help on place characterization. Moreover, the algorithm implemented in this work is camera independent because it does not depend on the camera-sensitive responses. The only requirement is that the camera responses can be approximated by the CIE XYZ \(2^o\) standard observer functions. The experimental results are discussed and analyzed in order to evaluate the performance of the color constancy method.

Local color transformation analysis for sudden illumination change detection

Sudden illumination changes are a fundamental problem in background modeling applications. Most strategies to solve it are based on determining the particular form of the color transformation which the pixels undergo when an illumination change occurs. Here we present an approach which does not assume any specific form of the color transformation. It is based on a quantitative assessment of the smoothness of the local color transformation from one frame to the background model. In addition to this, an assessment of the obtained illumination states of the pixels is carried out with the help of fuzzy logic. Experimental results are presented, which demonstrate the performance of our approach in a range of situations.

Object detection oriented video reconstruction using compressed sensing

Moving object detection plays a key role in video surveillance. A number of object detection methods have been proposed in the spatial domain. In this paper, we propose a compressed sensing (CS)-based algorithm for the detection of moving object in video sequences. First, we propose an object detection model to simultaneously reconstruct the foreground, background, and video sequence using the sampled measurement. Then, we use the reconstructed video sequence to estimate a confidence map to improve the foreground reconstruction result. Experimental results show that the proposed moving object detection algorithm outperforms the state-of-the-art approaches and is robust to the movement turbulence and sudden illumination changes.

Effective background modelling and subtraction approach for moving object detection

This study presents a hierarchical background modelling and subtraction approach for real‐time detection of moving objects. At the first level, a novel pixel‐wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow–lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block‐wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state‐of‐the‐art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.

A background model re-initialization method based on sudden luminance change detection

Sudden changes in illumination often occur in real world scenarios and may cause considerable difficulties in modeling backgrounds for the state-of-the-art background subtraction methods. In this paper, we propose a simple and effective background re-initialization method that detects sudden luminance change effectively. The purpose of the proposed method is not on the presentation of a specific solution for object detection, but is instead the improvement of the background subtraction approach so that it is capable of sudden luminance change adaptation. Two embodiments related to background subtraction, and which are based on the proposed method, are also presented. These embodiments can detect the moving objects accurately as the luminance of the background model is adjusted quickly after the proposed method is employed for generating the background model. Experimental results demonstrate that the proposed method effectively improves the background subtraction methods as measured by qualitative as well as quantitative assessments.

A Background Subtraction Algorithm Robust to Intensity Flicker Based on IP Camera

To solve the problem of indoor surveillance, this paper is concerned about the framework that allows the detection of moving objects based on IP camera. The current background subtraction algorithms cannot correctly classify foreground and background if there are sudden illumination changes. To eliminate the influence of illumination changes, intensity flicker correction technique is employed before background subtraction in this paper. Meanwhile to reduce the false alarms further, the union of two separate detection results is employed. In the experimental results, both global illumination changes and local ones are included. The results show that the improved subtractor proposed in this paper performs better than ViBe both in the aspect of correct detection rate and false alarms.

Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios

This paper proposes a novel bio-inspired neural system based on Self-organizing Maps (SOMs) and Cellular Neural Networks (CNNs), called SOM-CNN, to detect dynamic objects in normal and complex scenarios. A contribution of our work is a Retinotopic SOM (RESOM) architecture feasible for video and motion analysis. It is inspired by the visual perception mechanism of the human visual cortex, and satisfactorily addresses the disadvantages encountered by other methods in the area. We also propose a new CNN scheme for image thresholding, called Neighbor Threshold CNN (NTCNN), and a self-adapting parameter scheme for the RESOM and the NTCNN models. The proposed system can deal with sudden and gradual illumination changes, dynamic backgrounds, camouflage, camera jitter, and stopped dynamic objects. Experimental results on complex scenarios, using the Precision (Pe), Recall (Rc), F measure, (F1) and Similarity (Si) metrics, yield acceptable average performances with Pe=0.875, Rc=0.8316, F1=0.843 and Si=0.741. Results also show that our proposed system performs better than other methods that have been suggested in the literature. The system can process information at 35fps, rendering it suitable for real-time applications.

An Adaptive Unsupervised Neural Network Based on Perceptual Mechanism for Dynamic Object Detection in Videos with Real Scenarios

The analysis of moving objects in video sequences has been a paramount issue in applications related to intelligent surveillance systems, robotics, and medicine. Although several works aimed to analyze objects in video sequences have been reported, many of them need manual parameter adjustments and they are not tolerant to illumination changes and dynamic backgrounds. Therefore, a novel scheme termed Dynamic Retinotopic SOM based on an adaptive artificial neural network, to detect moving objects is proposed in this work. The neural network is a model based on the mechanisms of the visual cortex that we called Retinotopic SOM (RESOM) and it is also proposed in this paper. Furthermore, RESOM is a real-time neural network that can adapt its learning parameters based on the scene behavior and it mimics perception abilities. A quantitative comparison with other segmentation methods reported in the literature using real video scenes showed that the proposed DR-SOM segmentation method automatically adjusts its parameters and outperforms the reported methods in condition of dynamic backgrounds, and gradual and sudden illumination changes.

A self-adaptive Gaussian mixture model

Most background modeling techniques use a single leaning rate of adaptation that is inadequate for real scenes because the background model is unable to effectively deal with both slow and sudden illumination changes. This paper presents an algorithm based on a self-adaptive Gaussian mixture to model the background of a scene imaged by a static video camera. Such background modeling is used in conjunction with foreground detection to find objects of interest that do not belong to the background. The model uses a dynamic learning rate with adaptation to global illumination to cope with sudden variations of scene illumination. The algorithm performance is benchmarked using the video sequences created for the Background Models Challenge (BMC) [1]. Experimental results are compared with the performance of other algorithms benchmarked with the BMC dataset, and demonstrate comparable detection rates.

Robust background modelling using region-based codebooks

Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, we present a novel approach to background subtraction that addresses both of these challenges. Based on the work of Kim et al., we develop an improved codebook background modelling and subtraction technique. We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer. We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion and further propose a two-step update of the background codebook to handle both sudden and gradual illumination changes. Experimental results confirm the efficacy of our technique.

Background modeling in the maritime domain

Maritime environment represents a challenging scenario for automatic video surveillance due to the complexity of the observed scene: waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moreover, an appropriate background model has to deal with gradual and sudden illumination changes, camera jitter, shadows, and reflections that can provoke false detections. Using a predefined distribution (e.g., Gaussian) for generating the background model can result ineffective, due to the need of modeling non-regular patterns. In this paper, a method for creating a "discretization" of an unknown distribution that can model highly dynamic background such as water is described. A quantitative evaluation carried out on two publicly available datasets of videos and images, containing data recorded in different maritime scenarios, with varying light and weather conditions, demonstrates the effectiveness of the approach.

Adaptive background model for non-static background subtraction by estimation of the color change ratio

Background modeling, a preliminary processing step for foreground detection, is a challenging task because of the complexity and variety of background regions and unexpected scenarios such as sudden illumination changes, waving trees, rippling water, etc. In this work, we develop a pixel-based background modeling method that uses a probabilistic approach by means of changing color sequences. This method uses two background models in tandem. The first model uses a static background, which is obtained via a probabilistic approach and is a standard from which the foreground is extracted. The second method uses an adaptive background, which is modeled by the degree of color change. This background functions as an additional standard from which the foreground is extracted and is appropriate for eliminating non-static background elements. These models enable the developed method to automatically adapt to various environments. The algorithm was tested on various video sequences and its performance was evaluated by comparison with other state-of-the-art background subtraction methods.

Background Modeling Method Based on 3D Shape Reconstruction Technology

In this research, we present a novel dynamic background modeling method based on reconstructed 3D shapes, which can solve background modeling problems of multi-camera in real-time. While 3D shape reconstruction is a popular technology widely used for detecting, tracking or identifying various objects, little effort has been made in applying this useful method to background subtraction. In this work we propose an approach to using 3D shape reconstruction technology to develop a novel decision making mechanism for background image updating. This 3D shape reconstruction based background subtraction method is adaptive to changes in illumination, capable of handling sudden illumination changes as well as complex dynamic scenes efficiently. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2387

AN ADAPTIVE BACKGROUND UPDATION AND GRADIENT BASED SHADOW REMOVAL METHOD

Moving object segmentation has its own niche as an important topic in computer vision. It has avidly being pursued by researchers. Background subtraction method is generally used for segmenting moving objects. This method may also classify shadows as part of detected moving objects. Therefore, shadow detection and removal is an important step employed after moving object segmentation. However, these methods are adversely affected by changing environmental conditions. They are vulnerable to sudden illumination changes, and shadowing effects. Therefore, in this work we propose a faster, efficient and adaptive background subtraction method, which periodically updates the background frame and gives better results, and a shadow elimination method which removes shadows from the segmented objects with good discriminative power. Keywords- Moving object segmentation,

로컬 히스토그램 명세화에 기반한 화질 개선

In this paper we propose an image enhancement technique based on histogram specification method over local overlapping regions referred as Local Histogram Specification. First, both reference and original images are splitted into local regions that each overlaps half of its adjacent regions and general histogram specification method is used between corresponding local regions of reference and original image. However it produces noticeable boundary effects. Linear weighted image blending method is used to reduce this effect in order to make seamless image and we also proposed new technique dealing with over-enhanced contrast areas. We satisfied with our experimental results that showed better enhancement accuracy and less noise amplifications compared to other well-known image enhancement methods. We conclude that the proposed method is well suited for motion detection systems as a responsible part to overcome sudden illumination changes.