Research ArticleOpen accessPhrase in the titleSparse Representation Tracking with Auxiliary Adaptive Appearance ModelsJunqiu Wang, Chao Yang, Zhichao Gan, Yasushi YagiWe propose an effective tracking algorithm based on sparse representation and auxiliary adaptive appearance modeling. Based on a sparse representation, l1 minimization can follow targets in challenging situations. Unfortunately, tracking approaches based on l1 minimization are likely to be inefficient because they measure using dense coefficient distributions. The number of target candidates can be very large when the state space … …American Journal of Engineering and Applied Sciences, Vol. 10, Issue 1 · 14 Jan 2017 · 7,676 accessesView PDFCite
Research ArticleOpen accessIn the keywordsFake News Detection Using Weighted Fine-Tuned BERT and Sparse Recurrent Neural NetworkAsha Kathigi, Mukta Pujar, A M S Akshatha et al.Fake news refers to misinformation or false reports shared in the form of images, articles, or videos, disguised as real news to manipulate people’s opinions. Recently, fake news and rumors have spread extensively and rapidly around the world. This has led to the production and propagation of inaccurate news articles. Therefore, it is necessary to restrict the spread of fake … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,087 accessesView PDFCite
Research ArticleOpen accessIn the keywordsMultiresolution Laplacian Sparse Coding Technique for Image ClassificationIntidhar Jemel, Ridha Ejbali, Mourad ZaiedSparse coding is a set of techniques used for learning a collection of over-complete bases to represent data efficiently. This technique has been used in different domain such as feature quantization and image classification. Despite its capacity of modeling, it could not represent similarity of the image coding which cause a poor performance in locality. The cause of this limitation … …Journal of Computer Science, Vol. 14, Issue 8 · 23 May 2018 · 7,920 accessesView PDFCite
Review ArticleOpen accessIn the keywordsUnleashing the Power of Image Denoising: A Comprehensive Review of Classical to Deep Learning MethodsArchana Saini, Ayush DograImage denoising is a vital step in many image processing and computer vision professions that aims to improve picture quality by decreasing noise while maintaining important image information. In this article, a detailed overview is presented of traditional and deep learning based picture denoising approaches. Classical methods, such as linear filtering, transform domain techniques, and patch-based approaches like Non-Local Means … …Journal of Computer Science, Vol. 21, Issue 8 · 8 Oct 2025 · 3,107 accessesView PDFCite
Research ArticleOpen accessIn the abstractCLASSIFICATION OF DIGITAL IMAGES USING FUSION ELEVATED ORDER CLASSIFIER IN WAVELET NEURAL NETWORKR. Arulmurugan, P. SengottuvelanThe revival of wavelet neural networks obtained an extensive use in digital image processing. The shape representation, classification and detection play a very important role in the image analysis. Boosted Greedy Sparse Linear Discriminate Analysis (BGSLDA) trains the cascade level of detection in an efficient manner. With the application of reweighting concept and deployment of class-reparability criterion, lesser search was … …Journal of Computer Science, Vol. 10, Issue 9 · 23 Jun 2014 · 6,790 accessesView PDFCite