Earticle

Anti-collision calculation method is an important technique in the RFID system. On the basis of the research of RFID’ s special decoding method and classic anti-collision method in a dynamic environment, using results of the ability of the reader to test the ability of the electronic tags to adjust itself to the blank time frames, controlling the reader’s region of communication and allowing the reader to be at its highest efficiency and decoding rate, combined with the binary tree calculation method, the ability of the tags to adjust to the binary tree anti-collision method. When the tags are adjusting to the dynamic environment, at the same time, the tags that are interfering with the process can be removed, improving the stability of the RFID signal. The results shows that as compared to the calculation method used now, this calculation method effectively controls the happening of collisions, increasing the efficiency of the RFID system’s reading of tags.

Moving object detection is an important fundamental process in intelligent vision systems and an essential preprocessing step in high-level machine vision applications such as object tracking and moving analysis. This technique helps to detect suspicious events in video monitoring and is a key process for concentration estimation in traffic management. It is also one of the methods used in advanced vehicle control systems to keep vehicle in path and prevent accidents.In this paper, an effective weighted background moving object detection is presented,which is optimized for scenes with dynamic background. The proposed detection is based on real time background subtracting with high accuracy, low computational complexity and a short processing time, which makes it a good candidate for hardware implementation. The proposed algorithm is simulated in MATLAB software. The simulation results in MATLAB on various image sequences and comparison with mixture Gaussian method and median filter algorithm shows the effective weighted background method has better performance in different evaluation criteria that approves its efficiency in dynamic scenes.

The exposure fusion technique is very useful for directly fusing multi-exposure images into a high quality image, without the physically based high dynamic range (HDR) production step. In this paper, we present an improved method for multi-exposure images fusion using multi-resolution Laplacian pyramid weighted blending. We propose a Weight Modification Factor (WMF) to modify the original weight map for each image guided by contrast, saturation and well-exposedness of each pixel in the image, and to enhance the weight of the informative pixels in ultra-bright or ultra-dark areas. Compared with previous methods, our approach preserves more useful details for scenes with very large dynamic range and achieves no artifacts. Several objective quality metrics prove the advantages of our method.

A motion-based background subtraction method without geometric computation is proposed, allowing that the camera is moving parallel to the ground plane with uniform velocity. The proposed method subtracts the background region from a given image by evaluating the difference between calculated and model Hows. This approach is insensitive to small errors of calculated optical flows. Furthermore, in order to tackle the significant errors, a strategy for incorporating a set of optical flows calculated over different frame intervals is presented. An experiment with two real image sequences, in which a static box or a moving toy car appears, to evaluate the performance in terms of accuracy under varying thresholds using a receiver operating characteristic (ROC) curve. The ROC curves show, in the best case, the figure-ground segmentation is done at 17.8 ％ in false positive fraction (FPF) and 71.3％ in true positive fraction (TPF) for the static-object scene and also at 14.8％ in FPF and 72.4％ In TPF for the moving-object scene, regardless if the calculated optical flows contain significant errors of calculation.

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

Estimating the 3D geometric structure of a scene from images and generating accurate 3D models has been a long-standing research topic and remains an active area of study. However, the process of creating accurate 3D models requires relatively complex steps and achieving the desired quality of a 3D model is often challenging. Considering the complexity and difficulty of generating 3D models, technologies that create novel view images from scene images without precise 3D model information have seen consistent development. Recently, advancements in novel view synthesis technologies that generate images from given scene data have accelerated significantly, particularly after demonstrating the potential of combining these methods with AI technologies to produce higher-quality results. Among AI-based novel view synthesis approaches, AI-based dynamic scene reconstruction and rendering have received significant attention. This study explored recent trends in AI-based novel view synthesis for dynamic scenes.

최근 모바일 기기성능의 비약적인 향상에도 불구하고 아직 발열과 배터리의 한계로 인하여 PC 플랫폼에 비해 성능이 제한적이다. 따라서 고화질의 렌더링을 위하여 모바일 광선 추적 기술을 적용하는데 있어, 주 광선 계산은 래스터화 기반의 OpenGL ES 렌더링으로 대치한 후 이차 광선만을 추적하는 방법을 고려할 수 있다. 이 경우 전체 렌더링 과정에서 이차 광선의 추적 비용이 대부분의 시간을 차지하게 되는데, 본 논문에서는 이러한 문제를 해결하고자 동적인 장면 렌더링 시 응집성이 떨어지는 이차 광선의 탐색 성능을 개선하는 볼륨 격자 구조 방법을 제안한다. 이를 위해 가능한 모든 이차 광선을 정해진 적은 수의 샘플링 광선으로 모델링하여, 균일 격자 구조의 단점인 광선 추적 경로상의 모든 셀을 방문해야하는 문제를 완화하는 방법으로 격자 탐색의 성능을 향상시켰다. 또한 전체 렌더링 성능을 향상시키기 위하여 모바일 기기의 CPU와 GPU를 효과적으로 활용할 수 있는 하이브리드 렌더링 파이프라인을 제안한다.

Despite the recent remarkable advances in the computing power of mobile devices, the heat and battery problems still restrict their performances, particularly compared to PCs. Therefore, in the application of the ray-tracing technique for high-quality rendering, the consideration of a method that traces only the secondary rays while the effects of the primary rays are generated through rasterization-based OpenGL ES rendering is worthwhile. Given that most of the rendering time is for the secondary-ray processing in such a method, a new volume-grid technique for dynamic scenes that enhances the tracing performance of the secondary rays with a low coherence is proposed here. The proposed method attempts to model all of the possible spatial secondary rays in a fixed number of sampling rays, thereby alleviating the visitation problem regarding all of the cells along the ray in a uniform grid. Also, a hybrid rendering pipeline that speeds up the overall rendering performance by exploiting the mobile-device CPU and GPU is presented.