Face detection in profile views using fast discrete curvelet transform (FDCT) and support vector machine (SVM)

Human face detection is an indispensable component in face processing applications, including automatic face recognition, security surveillance, facial expression recognition, and the like. This paper presents a profile face detection algorithm based on curvelet features, as curvelet transform offers good directional representation and can capture edge information in human face from different angles. First, a simple skin color segmentation scheme based on HSV (Hue - Saturation - Value) and YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in color images with good detection rate and low misdetection rate

Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

<p>Abstract</p> <p>Background</p> <p>Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.</p> <p>Results</p> <p>Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.</p> <p>Conclusions</p> <p>The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.</p

Image Area Reduction for Efficient Medical Image Retrieval

Content-based image retrieval (CBIR) has been one of the most active areas in medical image analysis in the last two decades because of the steadily increase in the number of digital images used. Efficient diagnosis and treatment planning can be supported by developing retrieval systems to provide high-quality healthcare. Extensive research has attempted to improve the image retrieval efficiency. The critical factors when searching in large databases are time and storage requirements. In general, although many methods have been suggested to increase accuracy, fast retrieval has been rather sporadically investigated. In this thesis, two different approaches are proposed to reduce both time and space requirements for medical image retrieval. The IRMA data set is used to validate the proposed methods. Both methods utilized Local Binary Pattern (LBP) histogram features which are extracted from 14,410 X-ray images of IRMA dataset. The first method is image folding that operates based on salient regions in an image. Saliency is determined by a context-aware saliency algorithm which includes folding the image. After the folding process, the reduced image area is used to extract multi-block and multi-scale LBP features and to classify these features by multi-class Support vector machine (SVM). The other method consists of classification and distance-based feature similarity. Images are firstly classified into general classes by utilizing LBP features. Subsequently, the retrieval is performed within the class to locate the most similar images. Between the retrieval and classification processes, LBP features are eliminated by employing the error histogram of a shallow (n/p/n) autoencoder to quantify the retrieval relevance of image blocks. If the region is relevant, the autoencoder gives large error for its decoding. Hence, via examining the autoencoder error of image blocks, irrelevant regions can be detected and eliminated. In order to calculate similarity within general classes, the distance between the LBP features of relevant regions is calculated. The results show that the retrieval time can be reduced, and the storage requirements can be lowered without significant decrease in accuracy

Implementasi Transformasi Curvelet Dan Ruang Warna HVS Untuk Temu Kembali Citra Batik Berbasis Isi Pada Situs Batik

Batik merupakan bagian dari kekayaan budaya bangsa Indonesia. Dengan banyaknya motif dari tiap daerah di Indonesia dan sebagai pelestarian warisan budaya Indonesia, diperlukan inventarisasi data dari tiap motif batik. Namun, mencari dan mendapatkan kembali citra batik yang diinginkan pada sekumpulan data motif batik yang besar tidaklah mudah. Temu kembali citra berbasis isi merupakan suatu metode untuk pengenalan citra batik melalui ekstraksi fitur isi citra untuk melihat, mencari, dan menemukan kembali citra dari koleksi besar. Untuk memecahkan permasalahan di atas, pada Tugas Akhir ini diimplementasikan sistem temu kembali citra yang efektif dan efisien menggunakan Transformasi Curvelet dan ruang warna HSV. Transformasi Curvelet adalah representasi multi-skala baru yang cocok untuk obyek dengan kurva. Ruang Warna HSV konsisten dengan persepsi manusia karena HSV merepresentasikan warna dalam cara yang mirip dengan bagaimana manusia berpikir. Dari tiap citra batik akan di ambil fitur tekstur dari energi dan s tandar deviasi dari koefisien curvelet dengan Transformasi Curvelet pada tiap wedge tiap skala. Untuk fitur warna diambil histogram ruang warna HSV yang telah terkuantisasi menjadi 72 bins warna. Dari fitur tersebut lalu dicari similaritas tiap citra batik dengan menggunakan jarak Canberra. Berdasarkan hasil uji coba yang dilakukan terhadap dataset Batik, metode ini menghasilkan rata-rata precision sebesar 96.85% menggunakan Transformasi Curvelet 4 skala dan Histogram kuantisasi ruang warna HSV. Hal ini mengindikasikan bahwa Transformasi Curvelet dan ruang warna HSV merupakan metode yang menjanjikan dalam proses temu kembali citra batik. ============================================================================================================================= Batik is part of the richness of Indonesian culture. With so many motifs of each region in Indonesia and the preservation of cultural heritage of Indonesia, inventory data from each motif is required. However, search for and retrieve the desired image of batik motif on a large data set is not easy. Content-based image retrieval is a method for batik image recognition through the contents of the image feature extraction to view, search, and rediscover the image of a great collection. To solve the above problems, this final project propose image retrieval system that implemented effectively and efficiently using Curvelet Transformation and HSV color space. Curvelet transformation is a new multi-scale representation suitable for objects with curves. HSV color space is consistent with human perception because HSV represents color in a way similar to how humans think. Of each image will be taken batik texture feature of the energy and standard deviation of curvelet coefficients with curvelet transform each wedge on each scale. Color feature take the color histogram feature HSV color space that has been quantized into 72 bins color. From the feature above, similarity of each batik image calculated using Canberra distance. Based on the results of experiments performed on a dataset of batik, this method resulted in an average precision of 96.85% using 4-scale Curvelet transform and quantization histogram of HSV color space. This indicates that the Curvelet Transform and HSV color space is a promising method in the process of batik image retrieval

Identification de personnes par fusion de différentes modalités biométriques

This thesis contributes to the resolution of the problems which are related to the analysis of the biometric data outcome from the iris, the fingerprint and the fusion of these two modalities, for person identification. Thus, after the evaluation of those proposed biometric systems, we have shown that the multimodal biometric system based on iris and fingerprint outperforms both monomodal biometric systems based whatsoever on the iris or on the fingerprint.Cette thèse contribue essentiellement à la résolution des problèmes liés à l'analyse des données biométriques issues de l'iris, de l'empreinte digitale et de la fusion de ces deux modalités pour l'identification de personne. Ainsi, après l'évaluation des trois systèmes biométriques proposés, nous avons prouvé que le système biométrique multimodal basé sur l'iris et l'empreinte digitale est plus performant que les deux systèmes biométriques monomodaux basés que se soit sur l'iris ou sur l'empreinte digitale