Recognizing Visual Object Using Machine Learning Techniques

Nowadays, Visual Object Recognition (VOR) has received growing interest from researchers and it has become a very active area of research due to its vital applications including handwriting recognition, diseases classification, face identification ..etc. However, extracting the relevant features that faithfully describe the image represents the challenge of most existing VOR systems. This thesis is mainly dedicated to the development of two VOR systems, which are presented in two different contributions. As a first contribution, we propose a novel generic feature-independent pyramid multilevel (GFIPML) model for extracting features from images. GFIPML addresses the shortcomings of two existing schemes namely multi-level (ML) and pyramid multi-level (PML), while also taking advantage of their pros. As its name indicates, the proposed model can be used by any kind of the large variety of existing features extraction methods. We applied GFIPML for the task of Arabic literal amount recognition. Indeed, this task is challenging due to the specific characteristics of Arabic handwriting. While most literary works have considered structural features that are sensitive to word deformations, we opt for using Local Phase Quantization (LPQ) and Binarized Statistical Image Feature (BSIF) as Arabic handwriting can be considered as texture. To further enhance the recognition yields, we considered a multimodal system based on the combination of LPQ with multiple BSIF descriptors, each one with a different filter size. As a second contribution, a novel simple yet effcient, and speedy TR-ICANet model for extracting features from unconstrained ear images is proposed. To get rid of unconstrained conditions (e.g., scale and pose variations), we suggested first normalizing all images using CNN. The normalized images are fed then to the TR-ICANet model, which uses ICA to learn filters. A binary hashing and block-wise histogramming are used then to compute the local features. At the final stage of TR-ICANet, we proposed to use an effective normalization method namely Tied Rank normalization in order to eliminate the disparity within blockwise feature vectors. Furthermore, to improve the identification performance of the proposed system, we proposed a softmax average fusing of CNN-based feature extraction approaches with our proposed TR-ICANet at the decision level using SVM classifier

Classification of dental x-ray images

Forensic dentistry is concerned with identifying people based on their dental records. Forensic specialists have a large number of cases to investigate and hence, it has become important to automate forensic identification systems. The radiographs acquired after a person is deceased are called the Post-mortem (PM) radiographs, and the radiographs acquired while the person is alive are called the Ante-mortem (AM) radiographs. Dental biometrics automatically analyzes dental radiographs to identify the deceased individuals. While, ante mortem (AM) identification is usually possible through comparison of many biometric identifiers, postmortem (PM) identification is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashes and natural disasters such as Tsunami) most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Under such circumstances, the best candidates for postmortem biometric identification are the dental features because of their survivability and diversity.;In my work, I present two different techniques to classify periapical images as maxilla (upper jaw) or mandible (lower jaw) images and we show a third technique to classify dental bitewing images as horizontally flipped/rotated or horizontally un-flipped/un-rotated. In our first technique I present an algorithm to classify whether a given dental periapical image is of a maxilla (upper jaw) or a mandible (lower jaw) using texture analysis of the jaw bone. While the bone analysis method is manual, in our second technique, I propose an automated approach for the identification of dental periapical images using the crown curve detection Algorithm. The third proposed algorithm works in an automated manner for a large number of database comprised of dental bitewing images. Each dental bitewing image in the data base can be classified as a horizontally flipped or un-flipped image in a time efficient manner

Recent Advances in Steganography

Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced

An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

Realtime image noise reduction FPGA implementation with edge detection

The purpose of this dissertation was to develop and implement, in a Field Programmable Gate Array (FPGA), a noise reduction algorithm for real-time sensor acquired images. A Moving Average filter was chosen due to its fulfillment of a low demanding computational expenditure nature, speed, good precision and low to medium hardware resources utilization. The technique is simple to implement, however, if all pixels are indiscriminately filtered, the result will be a blurry image which is undesirable. Since human eye is more sensitive to contrasts, a technique was introduced to preserve sharp contour transitions which, in the author’s opinion, is the dissertation contribution. Synthetic and real images were tested. Synthetic, composed both with sharp and soft tone transitions, were generated with a developed algorithm, while real images were captured with an 8-kbit (8192 shades) high resolution sensor scaled up to 10 × 103 shades. A least-squares polynomial data smoothing filter, Savitzky-Golay, was used as comparison. It can be adjusted using 3 degrees of freedom ─ the window frame length which varies the filtering relation size between pixels’ neighborhood, the derivative order, which varies the curviness and the polynomial coefficients which change the adaptability of the curve. Moving Average filter only permits one degree of freedom, the window frame length. Tests revealed promising results with 2 and 4ℎ polynomial orders. Higher qualitative results were achieved with Savitzky-Golay’s better signal characteristics preservation, especially at high frequencies. FPGA algorithms were implemented in 64-bit integer registers serving two purposes: increase precision, hence, reducing the error comparatively as if it were done in floating-point registers; accommodate the registers’ growing cumulative multiplications. Results were then compared with MATLAB’s double precision 64-bit floating-point computations to verify the error difference between both. Used comparison parameters were Mean Squared Error, Signalto-Noise Ratio and Similarity coefficient.O objetivo desta dissertação foi desenvolver e implementar, em FPGA, um algoritmo de redução de ruído para imagens adquiridas em tempo real. Optou-se por um filtro de Média Deslizante por não exigir uma elevada complexidade computacional, ser rápido, ter boa precisão e requerer moderada utilização de recursos. A técnica é simples, mas se abordada como filtragem monotónica, o resultado é uma indesejável imagem desfocada. Dado o olho humano ser mais sensível ao contraste, introduziu-se uma técnica para preservar os contornos que, na opinião do autor, é a sua principal contribuição. Utilizaram-se imagens sintéticas e reais nos testes. As sintéticas, compostas por fortes e suaves contrastes foram geradas por um algoritmo desenvolvido. As reais foram capturadas com um sensor de alta resolução de 8-kbit (8192 tons) e escalonadas a 10 × 103 tons. Um filtro com suavização polinomial de mínimos quadrados, SavitzkyGolay, foi usado como comparação. Possui 3 graus de liberdade: o tamanho da janela, que varia o tamanho da relação de filtragem entre os pixels vizinhos; a ordem da derivada, que varia a curvatura do filtro e os coeficientes polinomiais, que variam a adaptabilidade da curva aos pontos a suavizar. O filtro de Média Deslizante é apenas ajustável no tamanho da janela. Os testes revelaram-se promissores nas 2ª e 4ª ordens polinomiais. Obtiveram-se resultados qualitativos com o filtro Savitzky-Golay que detém melhores características na preservação do sinal, especialmente em altas frequências. Os algoritmos em FPGA foram implementados em registos de vírgula fixa de 64-bits, servindo dois propósitos: aumentar a precisão, reduzindo o erro comparativamente ao terem sido em vírgula flutuante; acomodar o efeito cumulativo das multiplicações. Os resultados foram comparados com os cálculos de 64-bits obtidos pelo MATLAB para verificar a diferença de erro entre ambos. Os parâmetros de medida foram MSE, SNR e coeficiente de Semelhança

Multi-resolution dental image registration based on genetic algorithm

The Automated Dental Identification System (ADIS) is a Post Mortem Dental Identification System. This thesis presents dental image registration, required for the preprocessing steps of the image comparison component of ADIS. We proposed a multi resolution dental image registration based on genetic algorithms. The main objective of this research is to develop techniques for registration of extracted subject regions of interest with corresponding reference regions of interest.;We investigated and implemented registration using two multi resolution techniques namely image sub sampling and wavelet decomposition. Multi resolution techniques help in the reduction of search data since initial registration is carried at lower levels and results are updated as the levels of resolutions increase. We adopted edges as image features that needed to be aligned. Affine transformations were selected to transform the subject dental region of interest to achieve better alignment with the reference region of interest. These transformations are known to capture complex image distortions. The similarity between subject and reference image has been computed using Oriented Hausdorff Similarity measure that is robust to severe noise and image degradations. A genetic algorithm was adopted to search for the best transformation parameters that give maximum similarity score.;Testing results show that the developed registration algorithm yielded reasonable results in accuracy for dental test cases that contained slight misalignments. The relative percentage errors between the known and estimated transformation parameters were less than 20% with a termination criterion of a ten minute time limit. Further research is needed for dental cases that contain high degree of misalignment, noise and distortions