Histogram of Marked Background (HMB) Feature Extraction Method for Arabic Handwriting Recognition

Features extraction is one of the most important steps in handwriting recognition systems. In this paper, we propose a novel features extraction method, which is adapted to the complex nature of Arabic handwriting. The proposed feature called histogram of marked background (HMB) is not considering only ink pixels in a text image, but also uses the background of the image. Each background pixel in the text image was marked according to the repartition of ink pixels in its neighborhood. Feature vectors are extracted by computing histograms from the marked images. Hidden Markov models (HMMs) with Hidden Markov model toolkit (HTK) were used in the recognition process. The experiments were performed on two datasets: IBN SINA database of historical Arabic documents and Isolated Farsi Handwritten Character Database (IFHCDB). The proposed feature in this study produced efficient and promising results for Arabic handwriting recognition, for both isolated characters and for historical documents.

Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.

Transformasi Citra Biner Menggunakan Metode Thresholding Dan Otsu Thresholding

Thersholding merupakan salah satu metode sederhana dalam transformasi citra dari citra grayscale untuk membentuk citra biner, sebuah citra digital yang hanya memiliki dua kemungkinan warna pixelnya hitam dan putih, ” jika nilainya berada antara dua nilai threshold dan threshold outside dimana adalah kebalikan dari threshold inside. Biasanya pixel object diberi nilai 1 sementara pixel background diber inilai 0. Proses awal yang banyak dilakukan dalam image processing adalah mengubah citra berwarna menjadi citra gray­scale, hal ini digunakan untuk menyederhanakan model citra. Citra berwarna terdiri dari 3 layer matrik yaitu R­layer, G­layer dan B­layer. Sehingga untuk melakukan proses­proses selanjutnya tetap diperhatikan tiga layer di atas. Sehingga konsep itu diubah dengan mengubah 3 layer di atas menjadi 1 layer matrik gray­scale dan hasilnya adalah citra gray­scale Sebuah metode sederhana akan memilih nilai rata-rata atau nilai tengah, dengan pemikiran jika pixel object lebih terang dari pada background, pixel tersebut juga lebih terang dari rata-rata background tersebut. Sebuah pendekatan mutakhir adalah dengan membentuk histogram dari intensitas pixel dan menggunakan titik lembah sebagai nilai ambang. Dalam penelitian ini akan dibahas mengolahan citra digital yang akan ditransformasi dari penelitian ini dengan menggunakan Metode Thresholding didapatkan accuracy 39,22 % dan dilakukan gray thresholding didapatkan nilai accuracy 54,51%.

Image Matting Based Multi-Focus Image Fusion With Optimal Cluster Size

Image fusion is used to intensify the quality of images by combining two images of same scene obtained from different techniques. The present work deals with the effective extraction of pixel information from the source images that hold the key to multi focus image fusion. A solely vicinity-based image matting algorithm that relies on the close pixel clusters in the input images and their trimap, is presented in this article. The pixel cluster size, N plays a significant role in deciding the identity of the unknown pixel. The distance between each unknown pixel from foreground and background pixel clusters has been computed based on minimum quasi Euclidean distance. The minimum distance ratio gives the alpha value of each unknown pixel in the image. Finally, the focus regions are blend together to obtain the resultant fused image. On perceiving the results visually and objectively, it is concluded that proposed method works better in extracting the focused pixels and improving fusion quality, compared with other existing fusion methods.

The P25 protein of Potato virus X suppresses accumulation of 24nt siRNAs in the N. benthamiana transgenic line 16c

By transiently expressing viral suppressors of RNA silencing (VSRs) in combination with GFP silencing-inducing constructs in leaves of Nicotiana benthamiana line 16c, Hamilton et al. (2002) could establish a positive correlation between the production of 24nt GFP small interfering (si)RNAs in infiltrated leaves and the systemic onset of GFP silencing in remote tissues. In the context of GFP silencing inducers based on replicating Potato virus X (PVX), the P25 protein of PVX was found to specifically inhibit the local accumulation of 24nt GFP siRNAs. In the original paper, there were background pixel pattern duplications in the figure reporting the P25 experiments. We have now repeated these experiments with the original clones and the results presented here confirm those reported in the original paper.

A Motion Edge Extraction Method under the Condition of Camera Swing

Motion edge extraction is the object of most motion segmentation methods. We have proposed a motion edge extraction method in our previous work. However, if the camera swings, a number of background edges are classified into foreground edges. In order to solve this problem, we propose a background pixel relating method to modify the motion model and to remove those misclassified edges. At the end of this paper, we use the CAVIAR project test sequence to test our method. The result is satisfactory.

Study of Motion Detection Technology Based on Davinci Platform

A motion object detection method is presented based on Davinci platform. This paper adopts color histogram algorithm to detect moving target, which is operated on TMS320DM6446. In continuous frames, probability distributions of both foreground pixel and background pixel are counted separately to build color histogram. Background probability can be computed based on gauss model. After background separation, we can use median filtering to suppress image noise, detect connected domain, converge foreground pixels to make up of moving object regions and mark them. The results show that the method presented has good accuracy and quick speed for realtime application.

Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence.