فهرس المقالات Image Classification

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  1 - بررسی و پیش‌بینی تغییرات پوشش جنگل‌ها با استفاده از طبقه‌بندی شیءگرا فازی تصاویر ماهواره‌ای و زنجیره مارکوف، مطالعه موردی‌: شهرستان رومشکان
  رحمان زندی هژار شهابی ابراهیم اکبری
  10.29252/.4.6.103
  جنگل‌ها میراثی گران‌بها و یکی از عوامل مهم در اکوسیستم هر منطقه می باشند که علاوه‌ بر استفاده و بهره برداری از آن‌ها، در حفاظت و صیانت‌‌شان نیز باید اقدامات اساسی صورت گیرد. جنگل‌های زاگرس، به‌ویژه در استان لرستان، طی سالیان گذشته، در اثر بی‌توجهی روبه‌ زوال نهاده و چهر أکثر

  جنگل‌ها میراثی گران‌بها و یکی از عوامل مهم در اکوسیستم هر منطقه می باشند که علاوه‌ بر استفاده و بهره برداری از آن‌ها، در حفاظت و صیانت‌‌شان نیز باید اقدامات اساسی صورت گیرد. جنگل‌های زاگرس، به‌ویژه در استان لرستان، طی سالیان گذشته، در اثر بی‌توجهی روبه‌ زوال نهاده و چهره متفاوتی به خود گرفته‌اند. هدف این تحقیق بررسی، آشکارسازی و مدل سازی آینده تغییرات پوشش جنگل‌های شهرستان رومشکان می‌باشد. بدین منظور ابتدا تغییر کاربری‌های صورت گرفته بین سال‌های 1987 و 2017 با استفاده از تصاویر لندست و تکنیک طبقه‌بندی فازی شیءپایه استخراج شد، و به کلاس های (کشاورزی، جنگل، مرتع، عوارض آبی، مسکونی) طبقه بندی شدند. نتایج حاصل در طول سی سال کاهش شدید (17/81 کیلومترمربع) مناطق جنگلی و افزایش اراضی کشاورزی و مرتع در مناطق جنگلی را نشان می دهد. در بازه زمانی 1987-2002 جنگل ها دچار تغییرات خاصی نشده و عمده تغییرات شامل گسترش طبقه کشاورزی در مراتع بوده است. در بازه دوم از سال 2002 به بعد پوشش جنگل ها دچار کاهش شدید شده و مساحت آن‌ها از 58/122 به 42/43 کیلومتر مربع در سال 2017 رسیده است که 16/79 کیلومترمربع کاهش نشان داد. در ادامه برای پیش‌بینی روند تغییرات از زنجیره مارکوف استفاده شد که با توجه به نتایج پیش‌بینی زنجیره مارکوف در سال 2030، در نواحی جنگلی تغییراتی معادل 70/10 درصد اتفاق خواهد افتاد و عمده تغییرات مربوط به تغییر کاربری از کلاس جنگل به کلاس‌های کشاورزی و مرتع به ترتیب با 901/6 و 172/9 کیلومتر مربع خواهد بود. تفاصيل المقالة
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  2 - A new Sparse Coding Approach for Human Face and Action Recognition
  Mohsen Nikpoor Mohammad Reza Karami-Mollaei Reza Ghaderi
  10.7508/jist.2017.17.003
  Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the sparse coding method the images may be classified into أکثر

  Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the sparse coding method the images may be classified into the same class and devalue classification performance. In this paper, we propose an Affine Graph Regularized Sparse Coding approach for resolving this problem. We apply the sparse coding and graph regularized sparse coding approaches by adding the affinity constraint to the objective function to improve the recognition rate. Several experiments has been done on well-known face datasets such as ORL and YALE. The first experiment has been done on ORL dataset for face recognition and the second one has been done on YALE dataset for face expression detection. Both experiments have been compared with the basic approaches for evaluating the proposed method. The simulation results show that the proposed method can significantly outperform previous methods in face classification. In addition, the proposed method is applied to KTH action dataset and the results show that the proposed sparse coding approach could be applied for action recognition applications too. تفاصيل المقالة
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  3 - Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks
  Elham Gholami Seyed Reza Kamel Tabbakh Maryam khairabadi
  10.52547/jist.9.35.191
  Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting nu أکثر

  Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods. تفاصيل المقالة
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  4 - Optimized kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification
  Mohammad Hasheminejad
  10.52547/jist.16105.10.38.111
  Hyperspectral image (HSI) classification is an essential means of the analysis of remotely sensed images. Remote sensing of natural resources, astronomy, medicine, agriculture, food health, and many other applications are examples of possible applications of this techni أکثر

  Hyperspectral image (HSI) classification is an essential means of the analysis of remotely sensed images. Remote sensing of natural resources, astronomy, medicine, agriculture, food health, and many other applications are examples of possible applications of this technique. Since hyperspectral images contain redundant measurements, it is crucial to identify a subset of efficient features for modeling the classes. Kernel-based methods are widely used in this field. In this paper, we introduce a new kernel-based method that defines Hyperplane more optimally than previous methods. The presence of noise data in many kernel-based HSI classification methods causes changes in boundary samples and, as a result, incorrect class hyperplane training. We propose the optimized kernel non-parametric weighted feature extraction for hyperspectral image classification. KNWFE is a kernel-based feature extraction method, which has promising results in classifying remotely-sensed image data. However, it does not take the closeness or distance of the data to the target classes. Solving the problem, we propose optimized KNWFE, which results in better classification performance. Our extensive experiments show that the proposed method improves the accuracy of HSI classification and is superior to the state-of-the-art HIS classifiers. تفاصيل المقالة