Sobel Edge Map Research Articles

Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Robust estimation of noise strength in Magnetic Resonance Images (MRIs) is a task of great importance due to its extensive applications in image post-processing techniques. Many noise estimation algorithms have been proposed in the past to retrieve noise characteristics of the magnitude image. These algorithms rely on either the spatial or transform domain information of the image. In this research article, we propose a noise estimation algorithm that utilizes a hybrid Discrete Wavelet Transform (DWT) and edge information suppression based algorithm to estimate the strength of noise in magnitude MR images. The wavelet coefficients corresponding to spatial domain edges are eliminated using a down-sampled complement of the Sobel edge map. The distribution of average noise energy in spatial and transform domain which follows Parseval’s theorem is utilized for calculating the initial noise estimate. Further, the robustness of the proposed algorithm is enhanced using Feedforward Neural Network. The proposed algorithm is observed to be computationally fast and accurate. Results on synthetic and clinical T1- and T2-w brain MRIs following Gaussian or Rician distribution show better performance than the existing benchmark algorithms over a wide range of input noise.

Tamil text detection in videos using Gradient Vector Flow and Fuzzy C-Means

In videos, detecting text with multifaceted scenarios is perplexing. Texts in those videos have content full data facts that will be applied for various applications. Here, a system is proposed to enrich the text detection process from video. Here, a new method is implemented that detects Tamil text based on Gradient Vector Flow (GVF) and fuzzy c-means. First the video is split into number of frames. To circumvent temporal redundancy in each frame, a key frame is chosen and the frame where the text be located is identified to be the key frame. The dominant edge pixel is identified in that frame by the sobel edge map. Edge components are detected conforming towards the dominant pixel in sobel detector for constructing Text Candidates (TC).Clustering of a pixel is performed to detect text by using fuzzy c means clustering algorithm. Finally text is detected.

Gradient Vector Flow and Grouping-Based Method for Arbitrarily Oriented Scene Text Detection in Video Images

Text detection in videos is challenging due to low resolution and complex background of videos. Besides, an arbitrary orientation of scene text lines in video makes the problem more complex and challenging. This paper presents a new method that extracts text lines of any orientations based on gradient vector flow (GVF) and neighbor component grouping. The GVF of edge pixels in the Sobel edge map of the input frame is explored to identify the dominant edge pixels which represent text components. The method extracts edge components corresponding to dominant pixels in the Sobel edge map, which we call text candidates (TC) of the text lines. We propose two grouping schemes. The first finds nearest neighbors based on geometrical properties of TC to group broken segments and neighboring characters which results in word patches. The end and junction points of skeleton of the word patches are considered to eliminate false positives, which output the candidate text components (CTC). The second is based on the direction and the size of the CTC to extract neighboring CTC and to restore missing CTC, which enables arbitrarily oriented text line detection in video frame. Experimental results on different datasets, including arbitrarily oriented text data, nonhorizontal and horizontal text data, Hua's data and ICDAR-03 data (camera images), show that the proposed method outperforms existing methods in terms of recall, precision and f-measure.

Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing

In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua's data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.

A robust video text detection approach using SVM

A new method for detecting text in video images is proposed in this article. Variations in background complexity, font size and color, make detecting text regions in video images a difficult task. A pyramidal scheme is utilized to solve these problems. First, two downsized images are generated by bilinear interpolation from the original image. Then, the gradient difference of each pixel is calculated for three differently sized images, including the original one. Next, three K-means clustering procedures are applied to separate all the pixels of the three gradient difference images into two clusters: text and non-text, separately. The K-means clustering results are then combined to form the text regions. Thereafter, projection profile analysis is applied to the Sobel edge map of each text region to determine the boundaries of candidate text regions. Finally, we identify text candidates through two verification phases. In the first verification phase, we verify the geometrical properties and texture of each text candidate. In the second verification phase, statistical characteristics of the text candidate are computed using a discrete wavelet transform, and then the principal component analysis is further used to reduce the number of dimensions of these features. Next, the optimal decision function of the support vector machine, obtained by sequential minimal optimization, is applied to determine whether the text candidates contain texts or not.