Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

Intracranial Hemorrhage Annotation for CT Brain Images

In this paper, we created a decision-making model to detect intracranial hemorrhage and adopted Expectation Maximization(EM) segmentation to segment the Computed Tomography (CT) images. In this work, basically intracranial hemorrhage is classified into two main types which are intra-axial hemorrhage and extra-axial hemorrhage. In order to ease classification, contrast enhancement is adopted to finetune the contrast of the hemorrhage. After that, k-means is applied to group the potential and suspicious hemorrhagic regions into one cluster. The decision-making process is to identify whether the suspicious regions are hemorrhagic regions or non-regions of interest. After the hemorrhagic detection, the images are segmented into brain matter and cerebrospinal fluid (CSF) by using expectation-maximization (EM) segmentation. The acquired experimental results are evaluated in terms of recall and precision. The encouraging results have been attained whereby the proposed system has yielded 0.9333 and 0.8880 precision for extra-axial and intra-axial hemorrhagic detection respectively, whereas recall rate obtained is 0.9245 and 0.8043 for extra-axial and intra-axial hemorrhagic detection respectively

Hardware-Based Sobel Gradient Computations for Sharpness Enhancement

The majority of imaging systems are software based; they require some kind of microprocessor or microcontroller for the imaging algorithms to run. As the speed requirements of imaging and communications systems increase, the need for more hardware-based imaging systems arises. These fully hardware systems solve the fundamental problem inherent in software-based solutions, in which the speed of the algorithms depend on the instruction cycle speed of the processor. Once an algorithm is designed directly on hardware, the speed of the algorithm depends on the system clock frequency and the propagation delays of the logic cells (or standard cells) used in the design, usually measured in nanoseconds per cell. Therefore, such systems no longer depend on any instruction cycle delays, as there is no microprocessor involved. Most modern imaging and communications systems rely on digital signal processing (DSP) to compute complex mathematical operations. The emergence of powerful and low-cost field-programmable gate array (FPGA) devices with hundreds of arithmetic multipliers has enabled the development of many such DSP hardware applications, traditionally implemented only as software solutions

Evaluation of further reduced resolution depth coding for stereoscopic 3D video

This paper presents the results and analysis of the objective and subjective quality evaluations of Further Reduced Resolution Depth Coding (FRRDC) method for stereoscopic 3D video. FRRDC is developed based on the Scalable Video Coding (SVC) reference software and the result are objectively evaluated using rate distortion curve and subjectively evaluated using LCD and auto-stereoscopic video displays. FRRDC uses the Down-Sampling and Up-Sampling (DSUS) method of the depth data of the stereoscopic 3D video. The emergence of numerous auto-stereoscopic displays in the market confirms the growth of 3DTV services. It is essential that the coding method of stereoscopic 3D videos produces high quality 3D videos on both stereoscopic displays and emerging auto-stereoscopic 3D video displays to ensure the interoperability and compatibility among all the different display devices. In this paper, the stereoscopic 3D videos are compressed using the H.264/SVC codec with Reduced Resolution Depth Coding (RRDC) and compared with H.264/SVC-FRRDC. The experimental results indicate good 3D depth perception of FRRDC on both stereoscopic and auto-stereoscopic display devices with lesser bit rates compared to H.264/SVC-RRDC

Penuras terbitan Gaussian berorientasi untuk peruasan imej paru-paru radiograf mesin pegun dan mudah alih

Kaedah peruasan paru-paru tanpa seliaan adalah proses mandatori bagi membangunkan Sistem Dapatan Semula Imej Perubatan Berdasarkan Kandungan (CBMIRS) untuk imej sinar-x dada (CXR). Setakat ini, kajian berkenaan CXR bagi mesin mudah alih sangat terhad walhal ianya penting kerana pesakit yang tenat akan didiagnos menggunakan mesin mudah alih. Kajian ini membentangkan penyelesaian yang kukuh untuk peruasan paru-paru CXR bagi mesin pegun dan mudah alih, dengan kaedah automatik berasaskan penuras terbitan Gaussian dengan tujuh orientasi, digabungkan dengan teknik pengklusteran Fuzzy C-Means dan pengambangan untuk memperincikan kerangka paru-paru. Algoritma baru untuk menghasilkan nilai ambang secara automatik bagi setiap tindak balas Gaussian juga diperkenalkan. Algoritma ini digunakan untuk kedua-dua CXR PA dan AP daripada set data awam (JSRT) dan persendirian yang diperolehi daripada hospital kolaboratif. Dua blok pra-pemprosesan diperkenalkan untuk menyeragamkan imej dari mesin yang berbeza. Perbandingan dengan kajian terdahulu yang menggunakan set data JSRT menunjukkan kaedah kami menghasilkan keputusan yang memberangsangkan. Penilaian prestasi (ketepatan, F-skor, kepersisan, kepekaan dan kekhususan) bagi peruasan dari set data JSRT adalah lebih daripada 0.90, kecuali skor-bertindih (0.87). Nilai median skor-bertindih bagipangkalan data imej persendirian adalah 0.83 (mesin pegun) dan 0.75 (dari dua jenis mesin mudah alih). Algoritma ini juga pantas, dengan purata masa pelaksanaan 12.5s. Kaedah ini berupaya beroperasi tanpa penyeliaan, latihan atau pembelajaran untuk peruasan paru-paru bagi radiograf yang diambil dari mesin yang mempunyai piawaian berbeza, serta berupaya untuk digunakan dalam aplikasi CBMIRS

Development and validation of a new questionnaire assessing women perception on Malaysian road environment (WPRE)

High mobility rate among women has made them more vulnerable in the road environment. Lifestyle changes have urged many women to increase their mobility due to accommodate current demand. Therefore, women are exposed to the risk of accidents as many of them are populated in the road environment. However, most studies and instrumentation on-road environments are universal and not specifically targeting women's perception and anticipation preventing road accidents. Hence, the current study is developing and validating instrumentation of women's perception in Malaysia Road Environment. The sample of this study is 93 women with various age numbers. Out of 7 constructs, 6 were found most reliable and valid with the Cronbach Alpha value > 0.75. The present research provides details of factor analysis results, composite reliability, average variance extract, and reliability analysis which all concluded that the internal consistency of WPRE was not violated. Results reveal items developed are suitable to be adapted in future research with some modification. Finally, this research contributes to developing and validating women’s perception in a road accident which is reliable and valid for measuring WPRE

Block-based Against Segmentation-based Texture Image Retrieval

This paper concerns the best approach to the capture of local texture features for use in content-based image retrieval (CBIR) applications. From our previous work, two approaches have been suggested, the multiscale block-based approach and the automatic texture segmentation approach. Performance comparison as well as advantages and disadvantages of the two methods are presented in this paper. The databases used are the Brodatz and VisTex databases, as well as three museum image collections of various sizes and contents, with each collection presenting different challenges to the CBIR systems. Experimental observations suggest that the two approaches both perform well, with the multiscale technique having the edge in retrieval performance and scale invariance, while the segmentation technique has the edge in lighter computational complexity as well as having the shape information for later purposes. The choice between the two approaches thus depends on application

Content-Based Image Retrieval of Museum Images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associ-ated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low-quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections