Recent improvements in structural genomics efforts have greatly increased the number of hypothetical proteins in the Protein Data Bank. Several computational methodologies have been developed to determine the function of these proteins but none of these methods have been able to account successfully for the diversity in the sequence and structural conformations observed in proteins that have the same function. An additional complication is the flexibility in both the protein active site and the ligand. In this dissertation, novel approaches to deal with both the ligand flexibility and the diversity in stereochemistry have been proposed. The active site analysis problem is formalized as a classification problem in which, for a given test protein, the goal is to predict the class of ligand most likely to bind the active site based on its stereochemical nature and thereby define its function. Traditional methods that have adapted a similar methodology have struggled to account for the flexibility observed in large ligands. Therefore, I propose a novel fragment-based approach to dealing with larger ligands. The advantage of the fragment-based methodology is that considering the protein-ligand interactions in a piecewise manner does not affect the active site patterns, and it also provides for a way to account for the problems associated with flexible ligands. I also propose two feature-based methodologies to account for the diversity observed in sequences and structural conformations among proteins with the same function. The feature-based methodologies provide detailed descriptions of the active site stereochemistry and are capable of identifying stereochemical patterns within the active site despite the diversity. Finally, I propose a Markov Random Field approach to combine the individual ligand fragment classifications (based on the stereochemical descriptors) into a single multi-fragment ligand class. This probabilistic framework combines the information provided by stereochemical features with the information regarding geometric constraints between ligand fragments to make a final ligand class prediction. The feature-based fragment identification methodology had an accuracy of 84% across a diverse set of ligand fragments and the mrf analysis was able to succesfully combine the various ligand fragments (identified by feature-based analysis) into one final ligand based on statistical models of ligand fragment distances. This novel approach to protein active site analysis was additionally tested on 3 proteins with very low sequence and structural similarity to other proteins in the PDB (a challenge for traditional methods) and in each of these cases, this approach successfully identified the cognate ligand. This approach addresses the two main issues that affect the accuracy of current automated methodologies in protein function assignment.

NPR (Non-Photo-Realistic Rendering) is a new and quick-developed research topic in Image Processing. The main purpose of NPR is to generate pencil sketching¡Bwatercolor and oil painting, something different from photos, automatically by computer algorithms. On the other hand, there is another technique called PR (Photo-Realistic Rendering). The goal of PR is to generate real objects by computer algorithms, such as Matting or Inpainting. Furthermore, NPR includes two modes¡Gone is with physical model, researchers could write programs to simulate NPR by the properties of physical model. Without physical model, researchers could write programs to simulate NPR by their observation and deliberation. This thesis to the latter, there is no physical model in NPR. In the viewpoint of artists, drawing is performance of light and shadow. Then, in scientific, drawing depends on the degree of luminance. Luminance supports artists block and direction when drawing. Moreover, This thesis is mainly simulating oil painting with impressionist.

This thesis is based on the DSP to develop a facial expression recognition system. Most facial expression recognition systems suppose that human faces have been found, or the background colors are simple, or the facial feature points are extracted manually. Only few recognition systems are automatic and complete. This thesis is a complete facial expression system. Images are captured by CCD camera. DSP locates the human face, extracts the facial feature points and recognizes the facial expression automatically. The recognition system is divided into four sub-system: Image capture system, Genetic Algorithm human face location system, Facial feature points extraction system, Fuzzy logic facial expression recognition system. Image capture system is using CCD camera to capture the facial expression image which will be recognized in any background, and transmitting the image data to SRAM on DSP through the PPI interface on DSP. Human face location system is using genetic algorithm to find the human face¡¦s position in image by facial skin color and ellipse information, no matter what the size of the human face or the background is simple. Feature points extraction system is finding 16 facial feature points in located human face by many image process skills. Facial expression recognition system is analyzing facial action units by 16 feature points and making them fuzzily. Judging the four facial expression: happiness, anger, surprise and neutral, by fuzzy rule bases.. According to the results of the experiment. The facial expression system has nice performance on recognition rate and recognition speed.

As the digital technology advances with each passing day and the internet is evolving so quickly, the use of digital images is increasing on the demand. More information is showed in terms of digital patterns or images in our daily life. Besides retrieving image data from a given image database by context, we can alternatively do that by the image features we prescribed. This method is then called content-based image retrieval, CBIR. The wavelet transform possesses the power of multi-resolutional analysis for digital images. It¡¦s bands are mutually independent so that good results can often be obtained from partial analyses. Although wavelet transform is usually used for image compression and texture analysis, it has also many recent applications in the area of image retrieval. In this research, we propose the use of some new image roughness features to represent the variation of image textures. After an image is transformed on the wavelet, we collect the roughness features as well as wavelet energy features from each band. These features are then used to sort out desired images. We can show that the features as used in this work can be extracted even when the images are altered by some rotation, partial magnification or viewpoint changes.

Feature models are used for variability management in software product lines. Instead of developing a single feature model, merging small models can be an effective solution to obtain a unified view. Since each stakeholder views the product family from a different perspective, conflicts may occur during merging. In this research, merging of feature models arising from different viewpoints is considered. A normative procedure is proposed to merge feature models by applying local rules. This procedure can merge feature models with cross-tree relationships between sibling features. Application of the local rules is demonstrated with examples.

The technology of machine vision is board applied in many aspects such as industrial inspection, medical image processing, remote sensing and nanotechnology. It recovers useful information about a scene from its two-dimension projections. This recovery requires the inversion of a many-to-one mapping. But we usually lose some important data for not exactly correct mapping. It might occur from lens distortion, rotation and perspective distortion, and non-ideal vision systems. Camera calibration can compensate for these ill-conditions. In this thesis I present an active calibration technique derived from Song¡¦s research (1996) for calibrating the camera intrinsic parameters. It requires no reference object and directly uses the images of the environment. We only have to control the camera acting a series translational motion by the XYZ 3-D table.

Pattern recognition is very important in automatic industry. The automation machinery vision system must exchange information very fast with the object we need. So the machinery vision system must have powerful recognition ability. There are more important on image processing, lately. But most researches of image processing are developed on high-resolution image. However, in same situation, for increasing the processing speed, reducing the saving space. Low-resolution image are the only way to achieve the above condition up to now. For the purpose of quickly recognition, we construct the recognition system on low-resolution image. From observing the characteristic of hexagonal grid, we knew the hexagonal grid have greater angular resolution and better image performance than rectangular grid. Therefore, we apply the hexagonal grid on low-resolution image, and using Curve Bend Function (call CBF) on hexagonal gird system; for promoting the accuracy of recognition.We presented an technique of subpixel on low-resolution hexagonal image to obtain better results.

NPR (Non-Photo-Realistic Rendering) is a new and quick-developed research topic in Image Processing. The main purpose of NPR is to generate pencil sketching or watercolor, something different from photos, automatically by computer algorithms. On the other hand, there is another technique called PR (Photo-Realistic Rendering). The goal of PR is to generate real objects by computer algorithms, such as Matting. Furthermore, NPR includes two modes: one is with physical model and the other is not. With physical model, researchers could write programs to simulate NPR by the properties of physical model. Without physical model, researchers could write programs to simulate NPR by their observation and deliberation. This thesis belongs to the latter, NPR without physical model. In the viewpoint of artists, drawing is performance of light and shadow. Then, in scientific, drawing depends on the degree of luminance. Luminance supports artists block and direction when drawing. In this thesis, chapter 1 is introduction of art and previous researches. Chapter 2 describes theories what we can use and present the results. Chapter 3 describes the methods what this thesis use and necessary amendment, and present the results.

This thesis develops the method of using textures projected from the perspective of a projection camera, in combination with two-dimensional paintings and threedimensional models, to create digital environments. Past uses have demonstrated effectiveness only for background and midground scene elements with limited camera movement. This work explores how camera animation can be maximized using the projected texture technique onto foreground environment forms. Through several case studies, general guidelines for artists are developed for using camera-based projected textures.

In Chinese language, the smallest meaningful unit is a word which is composed of a sequence of characters. A Chinese sentence is composed of a sequence of words without any separation between them. In the area of information retrieval or data mining, the segmentation of a sequence of Chinese characters should be done before anyone starts to use these segments of characters. The process is called the Chinese word segmentation. The researches of Chinese word segmentation have been developed for many years. Although some recent researches have achieved very high performance, the recall of those words that are not in the dictionary only achieves sixty or seventy percent. An approach described in this paper makes use of the linear-chain conditional random fields (CRFs) to have a more accurate Chinese word segmentation. The discriminatively trained model that uses two of our proposed feature templates for deciding the boundaries between characters is used in our study. We also propose three other methods, which are the duplicate word repartition, the date representation repartition, and the segment refinement, to enhance the accuracy of the processed segments. In the experiments, we use several different approaches for testing and compare the results with those proposed by Li et al. and Lau and King based on three different Chinese word corpora. The results prove that the improved feature template which makes use of the information of prefix and postfix could increase both the recall and the precision. For example, the F-measure reaches 0.964 in the MSR dataset. By detecting repeat characters, the duplicated characters could also be better repartitioned without using extra resources. In the representation of date, the wrongly segmented date could be better repartitioned by using the proposed method which deals with numbers, date, and measure words. If a word is segmented differently from that of the corresponding standard segmentation corpus, a proper segment could be produced by repartitioning the assembled segment which is composed of the current segment and the adjacent segment. In the area of using the conditional random fields for Chinese word segmentation, we have proposed a feature template for better result and three methods which focus on other specific segmentation problems.