Sudden Light Changes Research Articles

Shot Boundary Detection Using Color Clustering and Attention Mechanism

Shot boundary detection (SBD) is widely used in scene segmentation, semantic analysis, and video retrieval. However, existing SBD algorithms have certain applications in video processing, but they have the following three problems. First, these algorithms cannot effectively handle shot boundaries caused by sudden lighting changes. Second, when there are dimly lighting frames in the video, these algorithms cannot perform boundary detection well. Third, when there is object or camera motion in the video, these algorithms also fail to work. To resolve these issues, we propose an SBD algorithm with color clustering changes in small regions (CCSR) to detect the shot transitions, which are abrupt changes and gradual transitions (dissolve and fade). The main idea behind the CCSR algorithm is to compute the distance of color features and to preserve the spatio-temporal information as much as possible. This model has relatively less dependence on the threshold parameters and sliding windows. Unlike other SBD algorithms, the clustering results of CCSR weaken factors such as object motion and illumination changes between adjacent frames in the video, which is helpful for reducing false detections. Furthermore, we utilize an attention mechanism in the gradual transitions to improve detection efficiency and accuracy. Finally, we evaluated the SBD algorithm, which was tested on a standard TRECVID dataset. The experimental results suggest that our algorithm yields significant improvements in precision and recall compared to the current techniques, with an average improvement of 10.35% and 8.85%, respectively. Moreover, compared with state-of-the-art algorithms, the results prove that the proposed method improves the F-score by more than 2.64% and the computation time efficiency by over 10%.

LiDAR Based Self-Driving Car

LiDAR, typically used as an acronym for “’light detection and ranging’”, is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real time. Its advantages include impressively accurate depth perception, which allows LiDAR to know the distance to an object to within a few centimetres, up to 60 metres away. It’s also highly suitable for 3D mapping, which means returning vehicles can then navigate the environment predictably —a significant benefit for most self-driving technologies. One of the key strengths of LiDAR is the number of areas that show potential for improvement. These include solid-state sensors, which could reduce its cost tenfold, sensor range increases of up to 200m, and 4-dimensional LiDAR, which senses the velocity of an object as well as its position in 3-D space. However, despite these exciting advances, LiDAR is still hindered by a key factor; its significant cost. LiDAR is not the only self-driving detection technology, with cameras as the major rival, championed by Tesla as the best way forward. Elon Musk has described LiDAR as “a fool’s errand” and “unnecessary”. The argument runs that humans drive based only on ambient visible light, so robots should equally be able to. A camera is significantly smaller and cheaper than LiDAR (although more of them are needed), and has the advantage of seeing in better resolution and in colour, meaning it can read traffic lights and signs. However, cameras have a wide host of characteristics that make them tricky to use in common driving conditions. Whereas LiDAR uses near infra-red light, cameras use visible light, and are thus more susceptible to issues when faced with rain, fog, or even some textures. In addition, LiDARs do not depend on ambient light, generating their own light pulses, whereas cameras are more sensitive to sudden light changes, direct sunlight and even raindrops

RETRACTED: Target detection algorithm and data model optimization based on improved Gaussian mixture model

A set of multi-stage image processing algorithms developed to detect the change of image sequence analysis. This means Moving Target Identification (MTI). The difference compared with the traditional method, the new method of spatial diversity. General purpose of object detection, image processing and image understanding of the specific field of research. Hue Saturation and Value (HSV,) colour space algorithm uses spot and shape detection to ensure detection under various conditions of lighting, shade, and distance. The algorithm is tested on the lighting and form detection account where the different variation of the displays. The only computational process of the challenge group is the presence of more than one target of the same colour in finding the correct target under changing lighting conditions. It has been found that the elasticity target people based on localization of these image processing methods for better detection is compared with the target detection. Digital image data contains most of this image data recognition model is optimized by integrating task planning normalization and inertial representation of the remote sensing image classification model based on spatial communication. The immediate message issue is that the Gaussian compound model cannot detect the entire moving object, and is prone to sudden light changes, etc. The advanced algorithm to be performed is proposed based on the Gaussian compound model and the detection method of the three legal difference moving object. Subsequently, a new adaptive selection technique for Gaussian distributions is introduced to reduce processing time and improve detection accuracy.

RETRACTED: Adaptive motion estimation and sequential outline separation based moving object detection in video surveillance system

Moving object detection is an important task in the basic research field of automated video surveillance systems in computer vision and video processing. The application of moving object tracking is critical for military, surveillance systems and operational robot applications, and getting more critical day by day. Closer view gap, ghosting, and sudden lighting changes have been the primary issues in moving object detection in existing methods. To think about the above issues, this work proposes two methodologies like consolidating background subtraction and improved sequential outline separation strategies for the recognition of various moving objects from indoor and outdoor genuine video dataset. This kind of framework can be realized in the general public places such as shopping malls, air terminals and railway stations or the safety of any private premises is the primary concern. This work precisely distinguishes the moving objects with shifting object size and number in various complex situations. The simulation work is done with MATLAB software, to measure the detection error and processing time of the proposed strategy. The proposed sequential outline separation method starts with background subtraction and foreground detection for motion and object discovery and it is a procedure of separating the territory of enthusiasm from developed background. Simulation results and error rate investigation demonstrate that our proposed strategies identify the moving targets productively. As Compared to other conventional systems, our proposed adaptive motion estimation and sequential outline separation method performs better by achieving an accuracy of 97.45%, a sensitivity of 94.2%, and a specificity of 97.72%.

Physiological and oxidative stress response of goldfish Carassius auratus induced by a light dimming system.

Light is an essential factor for organisms and affects the endocrine and stress regulation of fish in nature. However, sudden changes in light and dark conditions in artificial environments can negatively impact fish. In the present study, to evaluate the physiological and oxidative stress responses of goldfish (Carassius auratus) exposed to two different light conditions, sudden light changes and slowly dimming light changes for 24 h, we analyzed the mRNA expression and activity of stress indicators [corticotropin-releasing hormone (CRH) and pro-opiomelanocortin (POMC)], levels of plasma cortisol and glucose, mRNA expression of glucocorticoid receptor (GR), and activity of plasma oxidative stress indicators (superoxide dismutase and catalase). Consequently, the mRNA expressions and activities of CRH and POMC, plasma levels of cortisol and glucose, and mRNA expression of GR were found to be significantly increased during the light changes, particularly in the control group. Additionally, plasma levels of cortisol and glucose in the control group were significantly higher than those in the dimming group during the light changes. However, no significant differences in mRNA expression levels and activities of antioxidant enzymes both in the control and dimming groups were observed. These results indicate that dimming light induces less stress than sudden changes in light.

Adaptive Mean Shift Based Face Tracking by Coupled Support Map

The mean-shift algorithm is a local search technique that uses the similarity of the color distributed information between the target model and the local candidate image in the target. It has been proven to be superior in simplicity and stability of the technique and has been widely used for face tracking applications. However, one of the major problems in face tracking using color distribution is its vulnerability to backgrounds with similar color distribution, occlusion, and illumination changes. In this paper, we suggest a Coupled Support Map (CSM) to resolve the problem, and show the effectiveness of the robust adaptive mean-shift (AMS) face tracking method. Through series of experiments, we conclude the robustness of suggested algorithms against face size and sudden lighting changes.

A novel robust approach for handling illumination changes in video segmentation

In this work we propose a novel algorithm for foreground segmentation in video sequences that achieves better accuracy, while maintaining low complexity and allows real time execution. The proposed algorithm combines selected features from a number of well-established and state of the art algorithms, such as the Gaussian mixture models, the Self Organizing Maps and the illumination sensitive method. The presented methodology is capable of efficiently handling sudden light changes, both from natural and multiple artificial light sources, which may have caused erroneous segmentation for other algorithms. Comparative results are presented utilizing user-defined ground truth segmentation for two different types of indoor video sequences, one of which was obtained by a hemispheric omnidirectional dioptric (fish-eye) camera, with and without illumination changes and the second by a plain camera. The behavior of the algorithm with respect to its controlling parameters is investigated and its computational burden is studied.

The rise of the photosynthetic rate when light intensity increases is delayed in ndh gene-defective tobacco at high but not at low CO2 concentrations

The 11 plastid ndh genes have hovered frequently on the edge of dispensability, being absent in the plastid DNA of many algae and certain higher plants. We have compared the photosynthetic activity of tobacco (Nicotiana tabacum, cv. Petit Havana) with five transgenic lines (ΔndhF, pr-ΔndhF, T181D, T181A, and ndhF FC) and found that photosynthetic performance is impaired in transgenic ndhF-defective tobacco plants at rapidly fluctuating light intensities and higher than ambient CO2 concentrations. In contrast to wild type and ndhF FC, which reach the maximum photosynthetic rate in less than 1 min when light intensity suddenly increases, ndh defective plants (ΔndhF and T181A) show up to a 5 min delay in reaching the maximum photosynthetic rate at CO2 concentrations higher than the ambient 360 ppm. Net photosynthesis was determined at different CO2 concentrations when sequences of 130, 870, 61, 870, and 130 μmol m-2 s-1 PAR sudden light changes were applied to leaves and photosynthetic efficiency and entropy production (Sg) were determined as indicators of photosynthesis performance. The two ndh-defective plants, ΔndhF and T181A, had lower photosynthetic efficiency and higher Sg than wt, ndhF FC and T181D tobacco plants, containing full functional ndh genes, at CO2 concentrations above 400 ppm. We propose that the Ndh complex improves cyclic electron transport by adjusting the redox level of transporters during the low light intensity stage. In ndhF-defective strains, the supply of electrons through the Ndh complex fails, transporters remain over-oxidized (specially at high CO2 concentrations) and the rate of cyclic electron transport is low, impairing the ATP level required to rapidly reach high CO2 fixation rates in the following high light phase. Hence, ndh genes could be dispensable at low but not at high atmospheric concentrations of CO2.

Foreground detection using loopy belief propagation

This paper develops a simple and effective method for detection of sows and piglets in grayscale video recordings of farrowing pens. This approach consists of three stages: background updating, calculation of pseudo-wavelet coefficients and foreground object segmentation. In the first stage, the texture integration is used to update the background modelling (i.e. the reference image). In the second stage, we apply an “a trous” wavelet transform on the current reference image and then perform subtraction between the current original image and the approximation of the current reference image. In the third stage, the pairwise relationships between a pixel and its neighbours on a factor graph are modelled based on the pseudo-wavelet coefficients, and the image probabilities are approximated by using loopy belief propagation. Experiments have shown promising results in extracting foreground objects from complex farrowing pen scenes, such as sudden light changes and dynamic background as well as motionless foreground objects.

Thylakoid terminal oxidases are essential for the cyanobacterium Synechocystis sp. PCC 6803 to survive rapidly changing light intensities.

Cyanobacteria perform photosynthesis and respiration in the thylakoid membrane, suggesting that the two processes are interlinked. However, the role of the respiratory electron transfer chain under natural environmental conditions has not been established. Through targeted gene disruption, mutants of Synechocystis sp. PCC 6803 were generated that lacked combinations of the three terminal oxidases: the thylakoid membrane-localized cytochrome c oxidase (COX) and quinol oxidase (Cyd) and the cytoplasmic membrane-localized alternative respiratory terminal oxidase. All strains demonstrated similar growth under continuous moderate or high light or 12-h moderate-light/dark square-wave cycles. However, under 12-h high-light/dark square-wave cycles, the COX/Cyd mutant displayed impaired growth and was completely photobleached after approximately 2 d. In contrast, use of sinusoidal light/dark cycles to simulate natural diurnal conditions resulted in little photobleaching, although growth was slower. Under high-light/dark square-wave cycles, the COX/Cyd mutant suffered a significant loss of photosynthetic efficiency during dark periods, a greater level of oxidative stress, and reduced glycogen degradation compared with the wild type. The mutant was susceptible to photoinhibition under pulsing but not constant light. These findings confirm a role for thylakoid-localized terminal oxidases in efficient dark respiration, reduction of oxidative stress, and accommodation of sudden light changes, demonstrating the strong selective pressure to maintain linked photosynthetic and respiratory electron chains within the thylakoid membrane. To our knowledge, this study is the first to report a phenotypic difference in growth between terminal oxidase mutants and wild-type cells and highlights the need to examine mutant phenotypes under a range of conditions.

Real-time robust background subtraction under rapidly changing illumination conditions

Fast robust background subtraction under sudden lighting changes is a challenging problem in many applications. In this paper, we propose a real-time approach, which combines the Eigenbackground and Statistical Illumination method to address this issue. The first algorithm is used to reconstruct the background frame, while the latter improves the foreground segmentation. In addition, we introduce an online spatial likelihood model by detecting reliable background pixels. Extensive quantitative experiments illustrate our approach consistently achieves significantly higher precision at high recall rates, compared to several state-of-the-art illumination invariant background subtraction methods.

Improvement of mean shift tracking performance using a convex kernel function and extracting motion information

Color feature is now taken into real consideration as one of the important cues in the area of objects tracking, in image sequences. This feature has attracted considerable attention, in recent years. One of the well-known tools in color feature extraction is to use mean shift (MS) tracking algorithm. The probability of finding the object location in line with this tracking algorithm is somehow desirable, in image sequences, by maximizing the Bhattacharyya coefficient between both objects and corresponding candidate models. Even though the MS tracking algorithm is just known as a popular tool in the field of object tracking, it does not have sufficient merit to be realized in complex environments, i.e., background with object’s similar color, sudden light changes, occlusion types and so on. In such a case, the amount of the present coefficient could truly be decreased, during the tracking process, because of the mentioned environmental problems. A convex kernel function in association with the motion information of video sequences is used in this investigation to improve the MS tracking algorithm for the purpose of overcoming the existing problems. The proposed approach is employed to present the MS kernel function, directly. Thus, by using the investigation in its present form, the capability of the MS kernel is increased. Moreover, by using both color feature and motion information, simultaneously, in comparison with single color feature, noises and also uninterested regions can actually be eliminated. Experimental results on data set illustrate that the proposed approach has an optimum performance in real-time object tracking under the severe conditions.

Increasing the Accuracy of Detection and Recognition in Visual Surveillance

Visual surveillance has two major steps of detecting and recognizing moving objects. In the detection stage, moving objects must be detected as quickly and accurately as possible and the influence of environmental light changes and waving trees should be reduced. In this research a block-based method is introduced in HSV color space in the detection stage. This method did not scan all the pixels of the frame and acted well in situations like sudden light changes. A powerful pattern recognition system should have powerful feature extraction and classification. Note that, feature extraction in gray level or RGB color space has problems such as environmental light changes, adding noise or changes in contrast and sharpness of images, which lead to weak classification. So the HSV color space was used. Here, Block-based Improved Center Symmetric Local Binary Pattern is introduced for feature extraction. In each component of the HSV color space, information of highlight areas in the image such as edge, shape and some texture was extracted. The histogram was calculated in two-level blocks and Support Vector Machine was used for classifying into vehicles, motorcycles and pedestrians. The obtained results in increasing the detection accuracy and decreasing the spent time were satisfactory. DOI: http://dx.doi.org/10.11591/ijece.v2i3.337

Light history modulates antioxidant and photosynthetic responses of biofilms to both natural (light) and chemical (herbicides) stressors

In multiple stress situations, the co-occurrence of environmental and chemical factors can influence organisms' ability to cope with toxicity. In this context, the influence of light adaptation on the response of freshwater biofilms to sudden light changes or to herbicides exposure was investigated by determining various parameters: diatom community composition, photosynthetic parameters, chlorophyll a content, antioxidant enzyme activities. Biofilms were grown in microcosms under sub-optimal, saturating, and high light intensities and showed already described characteristics of shade/light adaptation (community structure, photosynthetic adaptation, etc.). Light history modulated antioxidant and photosynthetic responses of biofilms to the stress caused by short-term exposure to sudden light changes or to herbicides. First biofilms adapted to sub-optimal light intensity (shade-adapted) were found to be more sensitive to an increase in light intensity than high-light adapted ones to a reduction in light intensity. Second, while light history influenced biofilms' response to glyphosate, it had little influence on biofilms' response to copper and none on its response to oxyfluorfen. Indeed glyphosate exposure led to a stronger decrease in photosynthetic efficiency of shade-adapted biofilms (EC(50) = 11.7 mg L(-1)) than of high-light adapted communities (EC(50) = 35.6 mg L(-1)). Copper exposure led to an activation of ascorbate peroxidase (APX) in biofilms adapted to sub-optimal and saturating light intensity while the protein content decreased in all biofilms exposed to copper. Oxyfluorfen toxicity was independent of light history provoking an increase in APX activity. In conclusion this study showed that both previous exposure to contaminants and physical habitat characteristics might influence community tolerance to disturbances strongly.

Dual-mode detection for foreground segmentation in low-contrast video images

In video surveillance, the detection of foreground objects in an image sequence from a still camera is critical for object tracking, activity recognition, and behavior understanding. The widely used background updating models such as single Gaussian and mixture of Gaussians are based mainly on the mean gray level of a given observation period, which could be inevitably affected by outliers and noise in the images. The mean from the average of background pixel values and foreground pixel values of consecutive image frames in an observed period will not represent the true background in the scene. Thus, the mean background models cannot detect low-contrast objects and promptly respond to sudden light changes. In this paper, a dual-mode scheme for foreground segmentation is proposed. The mode is based on the most frequently occurring gray level of observed consecutive image frames, and is used to represent the background in the scene. In order to accommodate the dynamic changes of a background, the proposed method uses a dual-mode model for background representation. The dual-mode model can represent two main states of the background and detect a more complete silhouette of the foreground object in the dynamic background. The proposed method can promptly calculate the exact gray level mode of individual pixels in image sequences by simply dropping the last image frame and adding the current image in an observed period. The comparative evaluation of foreground segmentation methods is performed on the Microsoft's Wallflower dataset. The results show that the proposed method can quickly respond to illumination changes and well extract foreground objects in a low-contrast background.

Growth Reactions of Sub-Alpine Norway Spruce (Picea abies (L.) Karst.) Following One-Sided Light Exposure (Case Study at Davos “Lusiwald”)

In 1982, several rectangular openings were cut in a 100 year old sub-alpine Norway spruce forest stand to initiate regeneration at the Lusiwald site at Davos, Switzerland. The openings on the steep, north-facing slope created rapid changes to the environment of the border trees. Growth reactions of these border trees were compared and analysed with reference trees from the adjacent closed canopy stand in 1997. The radial growth pattern of the two data sets differed within the 14-year period since the openings were cut; the border trees showed growth releases. The growth reaction at the stem base was larger than at breast height. Changes in wind exposure may have influenced border trees to adapt their root systems. Sub-alpine Norway spruce stands aged around 100 years, which are usually considered slow-growing on a north aspect, still seem capable of reacting to greater resource availability such as sudden light changes.

Motion tracking with fast adaptive background subtraction

To extract and track moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm’s good performances when used in an indoor monitoring system.