The false positive (FP) is an over-segment result where the noncancerous pixel is segmented as a cancer pixel. The FP rate is considered a challenge in localising masses in mammogram images. Hence, in this article, a rejection model is proposed by using a supervised learning method in mass classification such as support vector machine (SVM). The goal of the rejection model which is based on SVM is the reduction of FP rate in segmenting mammogram through the Chan-Vese method, which is initialised by the marker controller watershed (MCWS) algorithm. The MCWS algorithm is utilised for segmentation of a mammogram image. The segmentation is subsequently refined through the Chan-Vese method, followed by the development of the proposed SVM rejection model with different window size as well as its application in eliminating incorrect segmented nodules. The dataset comprised of 57 nodules and 113 non-nodules and the study successfully proved the effectiveness of the SVM rejection model to decrease the FP rate.
In this paper, an image binarization method for separating text from the background of degraded textual images is proposed. This proposed methods are based on combination of Window Tracking Method (WTM) and Dynamic Image Histogram (DIH). The WTM and DIH methods work on an image that has been pre-processed. The WTM method searches for the largest pixel value in a 3 × 3 window up to a maximum of five tracking steps, while the method searches for a definite frequency between the two highest values in the image histogram. We test proposed method on DIBCO dataset and self-collection faded manuscripts. The experimental results show that our proposed method outperforms state of the art methods.
Pengecaman nombor plat (PNP) kenderaan telah dikaji secara intensif di kebanyakan negara. Berdasarkan perbezaan jenis nombor kenderaan yang digunakan, keperluan suatu sistem PNP adalah berlainan bagi setiap negara. Di dalam makalah ini, suatu sistem PNP automatik dicadangkan bagi kenderaan Malaysia dengan nombor plat piawai berdasarkan pada pemprosesan imej, penggugusan, pengekstrakan ciri dan rangkaian neural. Perpustakaan pemprosesan imej telah dibangunkan dalam satu pembangunan yang dirujuk sebagai Pelantar Pembangunan Sistem Penglihatan (PPSP). Tapisan penajam, tapisan minimum, tapisan median dan tapisan homomorfik telah digunakan di dalam proses pembaikan imej. Selepas penggunaan pembaikan imej, imej ditemberengkan menggunakan analisis blok, profil-profil imbasan garisan mendatar, penggugusan dan pendekatan alkhwarizmi kelancaran jarak larian untuk mengenal pasti lokasi nombor plat kenderaan. Secara keseluruhannya setiap imej dijelmakan menjadi objek-objek blok dan maklumat-maklumat penting seperti jumlah blok, lokasi, tinggi dan lebar, dianalisis bagi tujuan latihan gugusan dan pemilihan gugusan terbaik dengan blok terbanyak. Alkhwarizmi cadangan dipanggil pendekatan Alkhwarizmi Gugusan dan Kelancaran Jarak Larian (GKJL) digunakan untuk mencari lokasi nombor plat pada kedudukan yang betul. GKJL terdiri daripada dua cadangan alkhwarizmi berasingan, iaitu alkhwarizmi cadangan pengesan sisi menggunakan imej hasil topeng kernel 3×3 dan ofset 128 skala kelabu, dan hasil imej tersebut diambangkan untuk mengira Kelancaran Jarak Larian (KJL). Kedua teknik ini memperbaiki teknik gugusan dalam fasa penemberengan. Untuk menilai keberkesanannya, tiga eksperimen berasingan telah dijalankan. Jadual analisis kesilapan dibina berdasarkan kepada tiga eksperimen tersebut. Prototaip sistem mempunyai ketepatan melebihi 96% dan cadangan-cadangan untuk penambahbaikan sistem turut dibincangkan.
Detection and identification of text in natural scene images pose major challenges: image quality varies as scenes are taken under different conditions (lighting, angle and resolution) and the contained text entities can be in any form (size, style and orientation). In this paper, a robust approach is proposed to localize, extract and recognize scene texts of different sizes, fonts and orientations from images of varying quality. The proposed method consists of the following steps: preprocessing and enhancement of input image using the National Television System Committee (NTSC) color mapping and the contrast enhancement via mean histogram stretching; candidate text regions detection using hybrid adaptive segmentation and fuzzy c-means clustering techniques; a two-stage text extraction from the candidate text regions to filter out false text regions include local character filtering according to a rule-based approach using shape and statistical features and text region filtering via stroke width transform (SWT); and finally, text recognition using Tesseract OCR engine. The proposed method was evaluated using two benchmark datasets: ICDAR2013 and KAIST image datasets. The proposed method effectively dealt with complex scene images containing texts of various font sizes, colors, and orientation; and outperformed state-of-the-art methods, achieving >80% in both precision and recall measures.