International Journal of Image, Graphics and Signal Processing最新文献_第5页

Building a Medium Scale Dataset for Nondestructive Disease Classification in Mango Fruits Using Machine Learning and Deep Learning Models 利用机器学习和深度学习模型构建芒果果实无损疾病分类的中等规模数据集

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-08-08 DOI: 10.5815/ijigsp.2023.04.07

V. Ashok, Bharathi R K, P. Shivakumara

{"title":"Building a Medium Scale Dataset for Nondestructive Disease Classification in Mango Fruits Using Machine Learning and Deep Learning Models","authors":"V. Ashok, Bharathi R K, P. Shivakumara","doi":"10.5815/ijigsp.2023.04.07","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.04.07","url":null,"abstract":": The growing quality and safety concern about fresh agricultural produce among consumers have led to the development of non-destructive quality assessment and testing techniques of fruits and vegetables. Humans judge the quality of fruits based on sensory attributes like taste, aroma etc. The shape, size, color, presence of defects which are external to fruits also influence the degree of consumer acceptability of produce. The traditional time consuming, manual fruit quality inspection is replaced by automated, fast, consistent, non-destructive techniques using computer vision in combination with learning algorithms. But the lack of benchmark datasets for agricultural produce has made an objective comparison of the proposed methods difficult. Hence, the proposed work aims to build a medium scale dataset for mango fruits of “Alphonso” cultivar with three classes: chilling injury, defective and non-defective. The reliability of the proposed dataset consisting of 2279 color images of mango fruits with 736 samples in chilling injury class, 632 samples in defective class and 911 samples in non-defective class, was established using a novel approach of developing a predictive model based on discriminant function analysis (DFA) which assigns group membership to each sample of the dataset. Extensive benchmarking analysis is established on the validated dataset using statistical and deep learning algorithms like support vector machine (SVM) and convolutional neural network (CNN), respectively. SVM achieved significant disease classification accuracy of 95% and 91.52% accuracy was achieved by custom CNN. The results of the proposed work indicate that the proposed color image dataset of mango fruits can be used as a benchmark dataset by other researchers for objective comparison in quality evaluation of mango fruits.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115780185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Object tracking via a Novel Parametric Decisions based RGB-Thermal Fusion 基于参数决策的rgb -热融合目标跟踪

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-08-08 DOI: 10.5815/ijigsp.2023.04.01

Satbir Singh, A. Khosla, Rajiv Kapoor

引用次数: 0

COVID-19 and Malaria Parasite Detection and Classification by Bins Approach with Statistical Moments Using Machine Learning 基于机器学习统计矩的箱子方法检测和分类COVID-19和疟疾寄生虫

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.01

Hrishikesh Telang, Kavita Sonawane

{"title":"COVID-19 and Malaria Parasite Detection and Classification by Bins Approach with Statistical Moments Using Machine Learning","authors":"Hrishikesh Telang, Kavita Sonawane","doi":"10.5815/ijigsp.2023.03.01","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.03.01","url":null,"abstract":": This work introduces the novelty as an application of histogram-based bins approach with statistical moments for detecting and classifying malaria using blood smear images into parasitized and uninfected cell images and the rising disease of COVID-19/Normal lung images. Proposed algorithms greatly vary as compared to the previous work. This work aims to improve accuracy in detection and classification and reduce feature vector dimensionality. It focuses on detailed image contents extracted into 8 bins by considering the significance of the R, G, and B color component relationship in the formation of each pixel. The texture features are represented by the first four moments for each of the three colors separately. This leads to the generation of 12 features vectors, each of size 8 components for each image in the database. Feature dimensionality reduction is achieved by applying different feature selection techniques to obtain desired optimum feature space. The comprehensive feature analysis presented here identifies many useful findings in order to validate the contribution of each image content uniquely in detection and classification. The proposed approach experimented with two image datasets: the malaria dataset obtained from the National Library of Medicine (NLM) and the lung image dataset acquired from the Radiography Database from Kaggle. The performance of work presented here is evaluated and compared with previous work with the same set of parameters, namely precision, recall, F1 score, and the AUC. We have achieved and improved the performances compared to previous work and also achieved better results even for the COVID-19 dataset.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130849090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating the Effects of Voice Quality and Speech Intelligibility of Audio Compression in Automatic Emotion Recognition 自动情绪识别中音频压缩对语音质量和语音清晰度影响的估计

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.06

A. Reddy, Dileep kumar Ravikanti, Rakesh Betala, K. V. Sharma, K. S. Reddy

{"title":"Estimating the Effects of Voice Quality and Speech Intelligibility of Audio Compression in Automatic Emotion Recognition","authors":"A. Reddy, Dileep kumar Ravikanti, Rakesh Betala, K. V. Sharma, K. S. Reddy","doi":"10.5815/ijigsp.2023.03.06","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.03.06","url":null,"abstract":": This paper projects, the impact & accuracy of speech compression on AER systems. The effects of various codecs like MP3, Speex, and Adaptive multi-rate(NB & WB) are compared with the uncompressed speech signal. Loudness enlistment, or a steeper-than-normal increase in perceived loudness with presentation level, is associated with sensorineural hearing loss. Amplitude compression is frequently used to compensate for this abnormality, such as in a hearing aid. As an alternative, one may enlarge these by methods of expansion as speech intelligibility has been represented as the perception of rapid energy changes, may make communication more understandable. However, even if these signal-processing methods improve speech understanding, their design and implementation may be constrained by insufficient sound quality. Therefore, syllabic compression and temporal envelope expansion were assessed for in speech intelligibility and sound quality. An adaptive technique based on brief, commonplace words either in noise or with another speaker competing was used to assess the speech intelligibility. Speech intelligibility was tested in steady-state noise with a single competing speaker using everyday sentences. The sound quality of four artistic excerpts and quiet speech was evaluated using a rating scale. With a state-of-art, spectral error, compression error ratio, and human labeling effects, The experiments are carried out using the Telugu dataset and well-known EMO-DB. The results showed that all speech compression techniques resulted in reduce of emotion recognition accuracy. It is observed that human labeling has better recognition accuracy. For high compression, it is advised to use the overall mean of the unweighted average recall for the AMR-WB and SPEEX codecs with 6.6 bit rates to provide the optimum quality for data storage.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133649451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Video Steganography for Pixel Location Optimization Using Fr-WEWO Algorithm based Deep CNN Model 基于深度CNN模型的r- wewo算法的高效视频隐写像素位置优化

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.02

S. Salunkhe, Surendra Bhosale, Shubham V. Narkhede

引用次数: 0

Design and Implementation of Speckle Noise Reduction Algorithm Using 2D Ultrasound Image 基于二维超声图像的散斑降噪算法设计与实现

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.03

Md. Habibur Rahman, Md. Selim Hossain, Farhana Islam

{"title":"Design and Implementation of Speckle Noise Reduction Algorithm Using 2D Ultrasound Image","authors":"Md. Habibur Rahman, Md. Selim Hossain, Farhana Islam","doi":"10.5815/ijigsp.2023.03.03","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.03.03","url":null,"abstract":": Ultrasound is mostly used for diagnosis to deal with the specific abnormality in human body. To observe the internal organs including liver, kidneys, pancreas, thyroid gland, ovaries etc. ultrasound can be used. In diagnostic applications, 2 to 18 MHz frequencies are used. The sound wave explorations occurred through soft tissue and fluids. It bounces back as echoes from denser surfaces and creates an image. While producing ultrasound images from echo signal speckle noise is induced in a multiplicative way. Thus, speckle becomes the key challenge for ultrasound imaging. Several speckle reducing linear, non-linear and anisotropic diffusion-based methods are implemented to preserve the sharp edges of ultrasound images. Those methods contain lake of smoothing and edge preservation. However, this research proposed a combined method of adaptive filter (wiener) and anisotropic diffusion (modified Perona Malik) for speckle reduction of 2D ultrasound images by retain the important anatomical features. A comparison of all the existing methods studied based on the simulated experiment. To test the methods liver, kidney, heart and pancreas noise free images are used. Then, speckle noise is manually added with distinguished variance in between 0.02 and 0.20. Quality metrics are used to test the performance and show the improvements of the proposed method. About 71.79% structure similarity (SSIM), 66.72% root mean square error (RMSE), 56.93% signal to noise ratio (SNR), and 62.30% computational time are improved on average compared with the other methods.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131300636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Lte Rss for a Robust Path Loss Analysis with Noise Removal 基于去噪的Lte Rss鲁棒路径损耗分析

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.05

Seyi E. Olukanni, J. Isabona, I. Odesanya

引用次数: 0

An Efficient CNN Model for Automatic Diagnosis of Cardiomegaly from Chest Radiographic Images 从胸片图像自动诊断心脏肥大的有效CNN模型

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.07

Akanksha Soni, Avinash Rai

引用次数: 0

A Comparative Study on Plant Disease Detection and Classification Using Deep Learning Approaches 基于深度学习方法的植物病害检测与分类比较研究

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-06-08 DOI: 10.5815/ijigsp.2023.03.04

B. Balaji, Satyanarayana Murthy, Ramu Kuchipudi

{"title":"A Comparative Study on Plant Disease Detection and Classification Using Deep Learning Approaches","authors":"B. Balaji, Satyanarayana Murthy, Ramu Kuchipudi","doi":"10.5815/ijigsp.2023.03.04","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.03.04","url":null,"abstract":": Agriculture is a big sector in nations like India, and it provides a living for many people. To improve crop productivity, it’s very necessary to identify and classify plant diseases and prevent them from spreading further so that they do not affect the whole plant. Artificial intelligence (AI) and computer vision can help detect plant diseases that humans cannot always catch and overcome the shortcomings of continuous human monitoring. In this article, we aim to detect and classify diseases in tomato and apple leaves using deep learning approaches and compare the results between different models. Because tomatoes and apples are important components of the human diet, crop waste can result in losses for both farmers and ordinary people. These plant diseases have an immediate and negative impact on both the amount and quality of yield. Crop diseases must be identified and prevented as soon as possible to improve crop yield. Therefore, we need to monitor and analyze the growth stages of the plants so that the farmers can produce disease-free and with minimal losses to the crop. Furthermore, we used the sequential convolutional neural network (CNN) model followed by transfer learning models like VGG19, Resnet152V2, Inception V3, and MobileNet and compared the models based on accuracy. The performance of the models was evaluated using various factors such as dropout, batch size, and the number of epochs. For both, the datasets, the tomato, and apple MobileNet architecture performed better than the other existing models.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129915250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of Wavelets for Representation and Compression of Skin Cancer Images 小波在皮肤癌图像表示与压缩中的研究

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.03

Pavithra D R, Sudarshan Patil Kulkarni

引用次数: 0