Geometry-based shading for shape depiction Enhancement,

Recent works on Non-Photorealistic Rendering (NPR) show that object shape enhancement requires sophisticated effects such as: surface details detection and stylized shading. To date, some rendering techniques have been proposed to overcome this issue, but most of which are limited to correlate shape enhancement functionalities to surface feature variations. Therefore, this problem still persists especially in NPR. This paper is an attempt to address this problem by presenting a new approach for enhancing shape depiction of 3D objects in NPR. We first introduce a tweakable shape descriptor that offers versatile func- tionalities for describing the salient features of 3D objects. Then to enhance the classical shading models, we propose a new technique called Geometry-based Shading. This tech- nique controls reflected lighting intensities based on local geometry. Our approach works without any constraint on the choice of material or illumination. We demonstrate results obtained with Blinn-Phong shading, Gooch shading, and cartoon shading. These results prove that our approach produces more satisfying results compared with the results of pre- vious shape depiction techniques. Finally, our approach runs on modern graphics hardware in real time, which works efficiently with interactive 3D visualization

Revealing the molecular signatures of host-pathogen interactions.

Advances in sequencing technology and genome-wide association studies are now revealing the complex interactions between hosts and pathogen through genomic variation signatures, which arise from evolutionary co-existence

LivePhantom: Retrieving Virtual World Light Data to Real Environments.

To achieve realistic Augmented Reality (AR), shadows play an important role in creating a 3D impression of a scene. Casting virtual shadows on real and virtual objects is one of the topics of research being conducted in this area. In this paper, we propose a new method for creating complex AR indoor scenes using real time depth detection to exert virtual shadows on virtual and real environments. A Kinect camera was used to produce a depth map for the physical scene mixing into a single real-time transparent tacit surface. Once this is created, the camera's position can be tracked from the reconstructed 3D scene. Real objects are represented by virtual object phantoms in the AR scene enabling users holding a webcam and a standard Kinect camera to capture and reconstruct environments simultaneously. The tracking capability of the algorithm is shown and the findings are assessed drawing upon qualitative and quantitative methods making comparisons with previous AR phantom generation applications. The results demonstrate the robustness of the technique for realistic indoor rendering in AR systems

Photorealistic rendering: a survey on evaluation

This article is a systematic collection of existing methods and techniques for evaluating rendering category in the field of computer graphics. The motive for doing this study was the difficulty of selecting appropriate methods for evaluating and validating specific results reported by many researchers. This difficulty lies in the availability of numerous methods and lack of robust discussion of them. To approach such problems, the features of well-known methods are critically reviewed to provide researchers with backgrounds on evaluating different styles in photo-realistic rendering part of computer graphics. There are many ways to evaluating a research. For this article, classification and systemization method is use. After reviewing the features of different methods, their future is also discussed. Finally, dome pointers are proposed as to the likely future issues in evaluating the research on realistic rendering. It is expected that this analysis helps researchers to overcome the difficulties of evaluation not only in research, but also in application

A review of dynamic motion control considering physics for real time animation character

This paper presents a current motion research in the real time animation character. Real time animation character has the problem with unrealistic and unnatural character movement. This review focuses on three main parts in dynamic motion generation with physics consideration and control: skeleton hierarchy and kinematics, motion capture data animation, and active dynamic control

Hierarchical occlusion queries on driving simulation

Hardware occlusion queries is a method by which graphic processing unit (GPU) can gives fast response to the visibility status of a geometry object in virtual environment. Increasing number of issued queries lead to stall in CPU and starvation in GPU. However, hardware occlusion queries have improved by exploiting temporal coherence (TC). TC is proven improved hardware occlusion queries based on previous research. In driving simulation as car or camera move fast, more and more objects in scene become less significant to be rendered. Implementation of TC on driving simulation need be redesigned so allow TC suitable to faster movement. Intent of this paper is to describe how automatic parameters adaptation in temporal coherence is applied to driving simulation environment. Automatic parameters adaptation manipulated when the car or camera movement become more faster to maintain interactively rendering process and maintain realisms of complex virtual environment

A study on natural interaction for human body motion using depth image data

This paper explains a study on natural interaction (NI) in human body motion using depth image data. It involves about overview of NI and depth image data. Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. Furthermore, interaction with computer or machine can be more realistic as real world and becoming more important to academic researchers, game industries, and can be adapt to other field like mechanical engineering for robotics movement and surgery purpose in medical area. Functional taxonomies will show step-by-step how human body motion were detected and created a skeleton joint. Also, we discuss about technologies behind Kinect for Xbox 360 (Kinect). Recent research in this area also included

Range detection approach in interactive virtual heritage walkthrough

Exploring virtual world will give a memorable experience to the users. Generally, they are expecting smooth navigation with high quality image drawn into their eyes. Nevertheless, when the virtual world is growing simultaneously with the number of objects, the navigation will be detained. Yet, the frame rates can become unacceptable. In this paper, we present and examine range detection approach for view frustum culling to effectively speed-up the interactive virtual heritage project namely Ancient Malacca Virtual Walkthrough. Normal six plane view frustum culling approach use the plane equation to test whether objects is within the visible region. However, range detection approach is different with normal six plane view frustum. It is based on camera referential point and tests the objects if they are in the viewing range. The results show that this approach is able to increase the rendering performance of the virtual walkthrough without sacrificing the visual quality

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Segmentation plays a crucial role in speech processing applications, where high accuracy is essential. The quest for improved accuracy in automatic segmentation, particularly in the context of the Arabic language, has garnered substantial attention. However, the differences between Qur’an recitation and normal Arabic speech, especially with regard to intonation rules affecting the lengthening of long vowels, pose challenges in segmentation especially for Qur’an recitation. This research endeavors to address these challenges by delving into the domain of automatic segmentation for Qur’an recitation recognition. The proposed scheme employs a hidden Markov models (HMMs) forced alignment algorithm. To enhance the precision of segmentation, several refinements have been introduced, with a primary emphasis on the phonetic model of the Qur’an and Tajweed, particularly the intricate rules governing elongation. These enhancements encompass the adaptation of an acoustic model tailored for Qur’anic recitation as preprocessing and culminate in the development of an algorithm aimed at refining forced alignment based on the phonetic nuances of the Qur’an. These enhancements are seamlessly integrated as post-processing components for the classic HMM-based forced alignment. The research utilizes a comprehensive database featuring recordings from 100 renowned Qur’an reciters, encompassing the recitation of 21 Qur’anic verses (Ayat). Additionally, 30 reciters were asked to record the same verses, incorporating various recitation speed patterns. To facilitate the evaluation process, a Random sample of the Qur’anic database was manually segmented, comprised 21 Ayats, totaling 19,800 words, with 89 unique words (14 verses x 3 recitation levels: fast, slow and normal x 6 readers). The outcomes of this study manifest notable advancements in the alignment of long vowels within Qur’an recitation, all while maintaining the precise alignment of vowels and consonants. Objective comparisons between the proposed automatic methods and manual segmentation were conducted to ascertain the superior approach. The findings affirm that the classic forced alignment method produces satisfactory outcomes when employed on verses lacking long vowels. However, its performance diminishes when confronted with verses containing long vowels. Therefore, the test samples were categorized into three groups based on the presence of long vowels, resulting in a Correct Classification Rate (CCR) that ranged from 6% to 57%, contingent on whether the verse includes long vowels or not. The average CCR across all test samples was 23%. In contrast, the proposed algorithm significantly enhances audio segmentation. It achieved CCR values ranging from 16% to 70% within the same database categories, with an average CCR of 45% across all test samples. This marks a notable advancement of 22% in segmented speech accuracy, particularly within a 30 ms tolerance, for verses containing long vowels