Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

Morphological region-based initial contour algorithm for level set methods in image segmentation

Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to generate the automatic technique in order to reduce the human interaction and moreover, suitable IC to have accurate result. In this paper a new technique which we called Morphological Region-Based Initial Contour (MRBIC), is proposed to overcome this issue. The idea is to generate the most suitable IC since the manual initialization of the level set function surface is a well-known drawback for accurate segmentation which has dependency on selection of IC and wrong selection will affect the result. We have utilized the statistical and morphological information inside and outside the contour to establish a region-based map function. This function is able to find the suitable IC on images to perform by level set methods. Experiments on synthetic and real images demonstrate the robustness of segmentation process using MRBIC method even on noisy images and with weak boundary. Furthermore, computational cost of segmentation process will be reduced using MRBIC

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement using more detailed information of grayness of an image. However, these methods do not perform well on images taken in uncontrolled environment which pose different challenges such as illumination variation, perspective distortion and viewpoint variation. In this paper, we have worked to devise a more robust image enhancement method using fuzzy logic. We propose a novel multi scale entropy based measurement performed using fuzzy logic image processing and utilize it to define and enhance the contrast. For this purpose, we present a mathematical formula to calculate contrast using an adaptive amplification constant. Our approach uses both the local and global entropy information. We have experimented our algorithm on images from Crowd Counting UCF dataset, which contains very dense crowds and complex texture that stands in line with the challenges targeted in this paper. The results show an improved quality than original dataset images and prove that our method enhances the images with a more dynamic ranged contrast as well as better visual results

A functional enhancement on scarred fingerprint using sigmoid filtering

Fingerprint has been widely used in biometric applications. Numerous established researches on image enhancement techniques have been done to improve the quality of fingerprint images. However, the production of low-quality images due to the presence of scars remains a challenge in biometrics. The scars damage the fingerprint minutiae information due to broken ridges and they reduce the accuracy of identification. This research developed an image enhancement approach to improve the quality of scarred fingerprint images to generate accurate minutiae extraction. To achieve the aim, the scarred image was improved by removing noise using a new filter, Median Sigmoid (MS), and the corrected ridges were reconstructed using ridges structure enhancement algorithm. This was done to enhance the broken ridges structure. MS filter is a combination of median filter and modified sigmoid function that improves the image contrast and simultaneously removes noise in the fingerprint image. Following that, the filtered image was used in the ridges structure enhancement process. To identify true minutiae, the broken ridges structure in the filtered image needed to be accurately verified. In the ridges structure reconstruction process, an algorithm was enhanced to identify the best value of Sigma parameter (σ) used in the Gaussian Low-pass filter to generate a better orientation image. The image is important to reconstruct the corrupted fingerprint ridges structure. The evaluation for the proposed approach used the National Institute of Standards and Technology Special Database 14, and the results showed a 37% improvement of the quality index in comparison to approaches found in related research. The findings of the evaluation showed that the proposed enhancement approach produced a better minutiae extraction result and this is very significant in the field of fingerprint image enhancement

Automatic computer-aided caries detection from dental x-ray images using intelligent level set

Dental diseases have high risk of affection across the globe and mostly in adult population. The analysis of dental X-ray images has some difficulties in comparison to other medical images, which makes segmentation a more challenging process. One of the most important and yet largely unsolved issues in the level set method framework is the efficiency of signed force, speed function and initial contour (IC) generation. In this paper, a new segmentation method based on level set (LS) is proposed in two phases; IC generation using morphological information of image and intelligent level set segmentation utilizing motion filtering and back propagation neural network. The segmentation results are efficient and accurate as compared to other studies. The new approach to isolate each segmented teeth image is proposed by employing integral projection technique and feature map designed for each tooth to extract the local information and therefore to detect caries area. The achieved overall performance of the proposed segmentation method was evaluated at 120 periapical dental radiograph (X-ray), with images at 90% and the detection accuracy of 98%

Features selection for offline handwritten signature verification: State of the art

This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification

Enhancing fragility of zero-based text watermarking utilizing effective characters list

Text is an important medium used for sharing information worldwide. For a text document, digital watermarking is an efficient way for copyright protection, authentication, tamper proofing, to name but a few. In this paper, a zero-based watermarking approach is proposed for document authentication and tamper detection. To enhance the fragility of watermark, the proposed text watermarking approach can be comfortably utilized – based on the Effective Characters List (ECL) for watermark generation. The ECL method is generated for English text zero-watermarking by maintaining the contents of the original document and constructing the watermark by formulating the smooth transition between the selected characters in the documents. The evaluation of the proposed watermarking approach is based on three famous watermarking attacks including deletion, insertion, and reordering with an accuracy of 80.76%, 80.36%, and 88.1%, respectively. For a fair evaluation, a comparison is put forth with a recent zero-based watermarking method - clearly showing that the proposed method outperforms existing with greater accuracy. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall

Effects of probiotics on anxiety, stress, mood and fitness of badminton players

Data Availability Statement: The data is available upon request from the authors.Copyright © 2021 by the authors. Background: Reports of probiotic consumption on athletes’ performance are debatable due to their equivocal results. There is a need for more evidence on the effects of probiotic intake on psychological state and fitness level. Thus, this study determined the effects of daily probiotic consumption on competitive anxiety, perceived stress and mood among university badminton players, besides their fitness like aerobic capacity, strength, speed, leg power and agility. Methods: Thirty university badminton players aged from 19 to 22 years old were randomly divided equally into two groups, where the probiotic group (PG; n = 15) received a drink that contained Lactobacillus casei Shirota (3 × 1010 CFU) and placebo group (CG; n = 15) a placebo drink for six weeks. Anxiety, stress and mood levels were determined using the CSAI-2R, PSS and BRUMS questionnaires, respectively. Fitness levels were measured using by subjecting the players to 20-m shuttle runs (aerobic capacity), handgrips (muscular strength), vertical jumps (leg power), 40-m dash (speed) and T-test (agility). The Student’s t-test (p < 0.05) was used to determine the differences between PG and CG players. Results: After six weeks, the anxiety and stress levels of PG players significantly decreased by 16% (p < 0.001) and 20% (p < 0.001), respectively, but there were no significant changes detected in CG players. Supplementation of probiotics also improved aerobic capacity in PG players by 5.9% (p < 0.001) but did not influence the speed, strength, leg power and agility. Conclusions: Probiotics supplementation showed improved aerobic capacity and relieve anxiety and stress. However, further studies need to be carried out to determine the mechanisms through which probiotic intake produces these effects.This research was funded by this research Internal Collaboration Research Grant by Universitas Negeri Semarang, Universiti Teknologi MARA (100-IRMI/INT 16/6/2 (012/2019), and Research Universiti Individual Grant, Universiti Sains, Malaysia (1001.PPSK.8012370)