Parking slot detection system based on structural similarity index

Robust structural similarity index measure for images with non-Gaussian distortions

Finding free slots in the parking area has become a major problem that the modern society is facing nowadays, which also leads to traffic overcrowding at the parking area. As the number of vehicles are increasing at a quick pace find parking slots has become difficult. The proposed paper helps to solve the parking problem. In traditional parking system a person has to find vacant slot or a gate keeper needs to inform about the available parking slot at parking area. To address the issue, a system where the user will be able to know the number of vacant slots in a particular parking area along with total number of slots at the entrance of the parking area. As a critical component of this project, by built a car parking slot detection system which collects data from a camera at parking area. Today's most popular object detection model YOLO, is trained to achieve desired results with good precision scores at real-time speed. The model will be getting all the parking slots that are present in the parking area, to which marking boxes are drawn around for training. Real time processing is performed on the obtained data by the model to find whether the slot is vacant or occupied. Thus, it gives the information of available vacant slots to park the car at parking area. Key Words: YOLO, Object detection, Computer vision, Machine learning.

Due to light scattering by air particles, images taken under bad lighting conditions (such as haze, fog, mist, or smog) have a reduced level of visibility. One blurry image can be made visible again using single image dehazing techniques. Due to the ill-posedness of the Single image dehazing problem, it is difficult to solve. Even though, the Dark channel prior (DCP) has been the most prevalent method for image dehazing algorithms, but it suffers from huge computational time and picture quality. The accurate transmission map estimation is one of the best preferred ways to achieve the least computational time and high picture quality in DCP. Hence, this work is focused on constructing transmission map of a hazy image based on feed forward artificial neural network. The recommended method uses a feed forward ANN to conduct the transmission map straight from the minimum channel and a normalizing procedure to increase the re-covered image information. With a training set of eighty images, the network is trained by means of mean square error (MSE). The proposed method utilizing peak signal to noise ratio (PSNR) and structural similarity (SSIM) index measures are used to assess the restoration quality. The investigational conclusions have demonstrated that, the suggested method outperforms the dehazing of an input image without degrading visible quality (PSNR = 69.16, SSIM index = 0.8913). In addition, the proposed method is suited for real-time applications given the average computing time it achieves is 1.03 s.

Inkjet printers can be used to fabricate anthropomorphic phantoms by the use of iodine-doped ink. However, challenges persist in implementing this technique. The calibration from grayscale to ink density is complex and time-consuming. The purpose of this work is to develop a printing methodology that requires a simpler calibration and is less dependent on printer characteristics to produce the desired range of x-ray attenuation values. Conventional grayscale printing was substituted by single-tone printing; that is, the superposition of pure black layers of iodinated ink. Printing was performed with a consumer-grade inkjet printer using ink made of potassium-iodide (KI) dissolved in water at 1g/ml. A calibration for the attenuation of ink was measured using a commercial x-ray system at 70kVp. A neonate radiograph obtained at 70kVp served as an anatomical model. The attenuation map of the neonate radiograph was processed into a series of single-tone images. Single-tone images were printed, stacked, and imaged at 70kVp. The phantom was evaluated by comparing attenuation values between the printed phantom and the original radiograph; attenuation maps were compared using the structural similarity index measure (SSIM), while attenuation histograms were compared using the Kullback-Leibler (KL) divergence. A region of interest (ROI)-based analysis was also performed, where the attenuation distribution within given ROIs was compared between phantom and patient. The phantom sharpness was evaluated in terms of modulation transfer function (MTF) estimates and signal spread profiles of high spatial resolution features in the image. The printed phantom required 36 pages. The printing queue was automated and it took about 2h to print the phantom. The radiograph of the printed phantom demonstrated a close resemblance to the original neonate radiograph. The SSIM of the phantom with respect to that of the patient was 0.53. Both patient and phantom attenuation histograms followed similar distributions, and the KL divergence between such histograms was 0.20. The ROI-based analysis showed that the largest deviations from patient attenuation values were observed at the higher and lower ends of the attenuation range. The limiting resolution of the proposed methodology was about 1mm. A methodology to generate a neonate phantom for 2D imaging applications, using single-tone printing, was developed. This method only requires a single-value calibration and required less than 2h to print a complete phantom.

Aim: Image is the most powerful tool to analyze the information. Sometimes the captured image gets affected with blur and noise in the environment, which degrades the quality of the image. Image restoration is a technique in image processing where the degraded image can be restored or recovered to its nearest original image. Materials and Methods: In this research Lucy-Richardson algorithm is used for restoring blurred and noisy images using MATLAB software. And the proposed work is compared with Wiener filter, and the sample size for each group is 30. Results: The performance was compared based on three parameters, Power Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Correlation (NC). High values of PSNR, SSIM and NC indicate the better performance of restoration algorithms. Lucy-Richardson provides a mean PSNR of 10.4086db, mean SSIM of 0.4173%, and NC of 0.7433% and Wiener filter provides a mean PSNR of 6.3979db, SSIM of 0.3016%, NC of 0.3276%. Conclusion: Based on the experimental results and statistical analysis using independent sample T test, image restoration using Lucy-Richardson algorithm significantly performs better than Wiener filter on restoring the degraded image with PSNR (P<0.001) and SSIM (P<0.001).

With the growth in population, traffic congestion and parking have become a serious problem. There is the explosive growth of the per capita amount of vehicles. In this paper, a Location-Based Shared Smart Parking System is proposed to solve the parking problem. This system is designed for both private and public parking areas. This system helps the user to find the nearest possible parking area and gives the information related to the availability of parking slots in that respective parking area. The main focus of this system is to reduce the time for finding the parking slot and also avoids unnecessary travelling through occupied parking slots in a parking area. The proposed system is designed using IoT technology, WSN(IR sensors), QR code and RFID technology. IR sensors are used to detect the presence of the car in the parking slot and QR code is used to authenticate the user in the public parking area, RFID technology is used to authenticate the user in private parking area. The system is expected to reduce traffic problems, fuel consumption which results in reduces carbon footprints in an atmosphere.

The development of private vehicle ownership is very rapid, but it is not balanced by adequate supporting infrastructure such as roads and parking lots. Especially for parking lots, parking horizontally will be difficult to develop because of the limited land availability. The addition of parking lots can be done vertically by building a multi-storey parking building. So far, the existence of multi-storey parking buildings has not provided an information system on the availability of parking spaces. The form of multi-storey parking lots often makes the driver have to go around few times to find an empty space. This situations wasting of energy and time. This study aims to design an information system as application models in human-computer interaction, that will inform someone there is empty or already filled parking space, so that it can save time and make it easier for drivers to find parking spaces to park their cars. This information system prototype design consists of three main parts, called sensors, controllers, and displays. Car parking control system that works with an automatic portal (parking gate) along with a display screen for information on the location of empty parking spaces and those that have been filled. The portal will open if information states the parking lot is still available, and the portal will not open if information states the parking lot is full.

Nowadays most of the information is stored in images and there is a need to convert this information into an editable format. The objective of this paper includes the development of a user-friendly tool to extract text from the video or to recognize well-written handwriting in a scanned document to an editable form. The proposed methodology is robust and can provide high-grade performance with layout distortion. The explication utilizes modalities like Optical Character Recognition (OCR), Artificial Neural Networks (ANN), Deep Learning and Maximally Stable Extremal Regions (MSER) techniques for text detection. The video extraction can be attained by using video to frame conversion and then to use the Structural Similarity Index Measure (SSIM). The prototype is developed using MATLAB and provided with a GUI, which is deployable on a workstation. The generated GUI is employed to define Region of Interest (ROI), specify the required text layout, highlighting the specific portions of the image, select the appropriate language and also export the text to a word document or notepad applications where it can be edited.

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

UTOMATED video surveillance addresses real-time observation of people and vehicles within a busy environment, leading to a description of their actions and interactions. The technical issues include moving object detection and tracking, object classification, human motion analysis, and activity understanding, touching on many of the core topics of computer vision, pattern analysis, and aritificial intelligence. Video surveillance has spawned large research projects in the United States, Europe, and Japan, and has been the topic of several international conferences and workshops in recent years. There are immediate needs for automated surveillance systems in commercial, law enforcement, and military applications. Mounting video cameras is cheap, but finding available human resources to observe the output is expensive. Although surveillance cameras are already prevalent in banks, stores, and parking lots, video data currently is used only “after the fact” as a forensic tool, thus losing its primary benefit as an active, real-time medium. What is needed is continuous 24-hour monitoring of surveillance video to alert security officers to a burglary in progress or to a suspicious individual loitering in the parking lot, while there is still time to prevent the crime. In addition to the obvious security applications, video surveillance technology has been proposed to measure traffic flow, detect accidents on highways, monitor pedestrian congestion in public spaces, compile consumer demographics in shopping malls and amusement parks, log routine maintainence tasks at nuclear facilities, and count endangered species. The numerous military applications include patrolling national borders, measuring the flow of refugees in troubled areas, monitoring peace treaties, and providing secure perimeters around bases and embassies. The 11 papers in this special section illustrate topics and techniques at the forefront of video surveillance research. These papers can be loosely organized into three categories. Detection and tracking involves real-time extraction of moving objects from video and continuous tracking over time to form persistent object trajectories. C. Stauffer and W.E.L. Grimson introduce unsupervised statistical learning techniques to cluster object trajectories produced by adaptive background subtraction into descriptions of normal scene activity. Viewpoint-specific trajectory descriptions from multiple cameras are combined into a common scene coordinate system using a calibration technique described by L. Lee, R. Romano, and G. Stein, who automatically determine the relative exterior orientation of overlapping camera views by observing a sparse set of moving objects on flat terrain. Two papers address the accumulation of noisy motion evidence over time. R. Pless, T. Brodský, and Y. Aloimonos detect and track small objects in aerial video sequences by first compensating for the self-motion of the aircraft, then accumulating residual normal flow to acquire evidence of independent object motion. L. Wixson notes that motion in the image does not always signify purposeful travel by an independently moving object (examples of such “motion clutter” are wind-blown tree branches and sun reflections off rippling water) and devises a flow-based salience measure to highlight objects that tend to move in a consistent direction over time. Human motion analysis is concerned with detecting periodic motion signifying a human gait and acquiring descriptions of human body pose over time. R. Cutler and L.S. Davis plot an object’s self-similarity across all pairs of frames to form distinctive patterns that classify bipedal, quadripedal, and rigid object motion. Y. Ricquebourg and P. Bouthemy track apparent contours in XT slices of an XYT sequence volume to robustly delineate and track articulated human body structure. I. Haritaoglu, D. Harwood, and L.S. Davis present W4, a surveillance system specialized to the task of looking at people. The W4 system can locate people and segment their body parts, build simple appearance models for tracking, disambiguate between and separately track multiple individuals in a group, and detect carried objects such as boxes and backpacks. Activity analysis deals with parsing temporal sequences of object observations to produce high-level descriptions of agent actions and multiagent interactions. In our opinion, this will be the most important area of future research in video surveillance. N.M. Oliver, B. Rosario, and A.P. Pentland introduce Coupled Hidden Markov Models (CHMMs) to detect and classify interactions consisting of two interleaved agent action streams and present a training method based on synthetic agents to address the problem of parameter estimation from limited real-world training examples. M. Brand and V. Kettnaker present an entropyminimization approach to estimating HMM topology and

In this paper an efficient approach “Hybrid Assessment and Enhancement technique for Foreign Object Detection (HAE-FOD)” is proposed to detect debris in the runway. Foreign objects such as engine fasteners or aircraft parts are left in the runway when an aircraft takes off. These objects may cause damage to life and property when they are not detected. In practice, they are identified manually which is tiresome and not always accurate. Computer vision using image processing can be of aid to detect the foreign objects. So, an automated system for detection of foreign objects in runway is proposed in this paper. It facilitates accurate foreign object detection under varying lighting and environment conditions. Images from runway are assessed for their quality and enhanced for detection of foreign objects. Images are assessed to determine the quality using the combined Image Quality Assessment Techniques of Histogram, SSIM (Structural Similarity Index Measure) and PSNR Ratio. Then, the image quality is improved using Enhancement Techniques with the combination of Gamma Correction, CLAHE (Contrast Limited Adaptive Histogram Equalization) and Wiener Filter. To identify Foreign Objects from the enhanced images, Image Segmentation Techniques such as Edge Detection, Background Subtraction and Temporal Differencing are used. The proposed hybrid system with the combination of enhancement techniques based on type of degradation provides a better way for object detection in runway.

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

In this work we propose a novel SSIM (Structural Similarity Index Measure)-guided brain tissue classification approach, implementing Kernel Fisher Discriminant Analysis (KFDA). In Computer Vision, KFDA has been shown to be competitive with other state-of-the-art techniques. In the KFDA-based framework, we exploit the complex structure of grey matter, white matter and cerebro-spinal fluid intensity clusters to find an optimal classification. We illustrate our novel technique using a dataset of early normal brain development in the age range from 10 days to 4.5 years. The SSIM metric, an objective measure of an image quality as perceived by the Human Visual System, is used to evaluate the quality of brain segmentation. SSIM comparison of the quality of classification obtained by the KFDA-based and the Expectation-Maximization algorithms shows the superior performance of the proposed technique.

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems.

Finding an empty parking space in a crowded area is very stressful due to congestion. The absence of empty space leads to fatigue before the main activity, increased fuel consumption, which leads to increased pollution, and increased traffic due to the search for empty space. Therefore, it was necessary to have a system that would help drivers know the condition of each parking space. Empty or occupied with a vehicle. Where empty and occupied spaces are calculated and determined. The system displays on the screen at a convenient location in the parking lot a map to guide the driver to vacant positions and a warning if all positions are occupied. All this is done through images taken from the surveillance cameras in the parking lot. We find that the advantages of using vision-based systems over other existing systems are threefold. First There is no need to update the infrastructure of the parking lot, provided that the place is equipped with CCTV cameras that monitor the parking spaces covering the entire place. Secondly, camera-based systems give the exact location i.e. a detailed map of the vacant car parks which are good and necessary vacant lots. Third, camera-based methods are highly applicable in street parking spaces and residential areas.